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How to Succeed at Rethinking The Sales Profession
January 13, 2020 in Professional Development
Mike Montague interviews Haley Haggerty on How to Succeed at Rethinking the Sales Profession.
How to Succeed at Sandler Rule #1 – You have to learn how to fail to win [PODCAST]
This year, to celebrate the 10th anniversary of the book, Dave will revisit each of the original 49 Sandler Rules and give updated takes on their relevance to salespeople and sales leaders.
How to Succeed at Having 2020 Vision [PODCAST]
January 6, 2020 in Professional Development
Mike Montague has been involved with Sandler for over 20 years as a client, certified trainer, VP of online learning, and now global head of content.
How to Succeed at Marketing to Your Ideal Client [PODCAST]
January 3, 2020 in Sales Process
Mike Montague interviews Jurgen Strauss, marketing innovator and host of the Innovabuzz podcast, on How to Succeed at Marketing to Your Ideal Client In this episode:
How to Succeed at Prospecting Your Own Clients [PODCAST]
August 9, 2019 in Sales Process
David Mattson, President and CEO of Sandler Training and 6-Time Best-Selling Author, talks about his fifth book, Sandler Rules for Sales Leaders.
Listen Time10 Minutes
How to Succeed at Recruiting [PODCAST]
February 18, 2019 in Management & Leadership
Steve Herzog, Sandler trainer, shows you how to succeed with the attitudes, behaviors, and techniques needed to be more successful at recruiting top talent. Get the best practices collected from around the world.
How to Succeed at Avoiding Mutual Mystification [PODCAST]
January 25, 2019 in Sales Process
David Mattson, President and CEO of Sandler Training and 6-Time Best-Selling Author, talks about his fifth book, Sandler Rules for Sales Leaders. The book is on sale here, as well as, the companion video course.
How to Succeed at Using the Thermometer Close
September 14, 2018 in Professional Development
David Mattson, President and CEO of Sandler, shares his thoughts about using the Thermometer Close. Learn the attitudes, behaviors, and techniques of top performers, who can gauge the pace and progress of their presentation with this simple Sandler technique.
How to Succeed at Trade Shows
Tim Pattson is the TradeshowGuy. He is the author of Tradeshow Success, host of the TradeshowGuy Monday Morning Coffee podcast, and founder of TradeshowGuy Exhibits. He joins us to talk about the best practices for succeeding at trade shows.
How to Succeed at Setting Onboarding Benchmarks
September 7, 2018 in Sales Process
Dave Mattson is back to talk to leaders and managers about onboarding. What is your plan for getting new sales up to speed and how do you know if things are going according to plan? Find out in this special selling the sandler way episode from our CEO.
How to Succeed at Channel Selling
Sandler trainers and new authors, Marcus Cauchi and David Davies, join us to talk about selling through retails, distributors, reps, and other third parties. Whether you are currently using other channels to sell your products or services or you are exploring new channels, you should listen to these two experts and read their new Sandler book.
How to Succeed at Negotiating Concessions
July 27, 2018 in Sales Process
Dave Mattson does another Selling the Sandler Way audio tip. This time he shares his thoughts and secrets to dealing with price concessions and other negotiation tactics from buyers during the sales process. Learn how to succeed at negotiating concessions to your proposal.
Special Podcast: The Art and Skill of Supporting the Mobile Sales Team
In partnership with Evernote, the app that keeps your notes organized and syncs your memos so they're searchable and accessible anywhere, Dave Mattson, Sandler CEO and President, participated recently in a special podcast.
How to Succeed at Getting Commitments throughout the Sales Process
June 18, 2018 in Sales Process
Learn how to get commitments and be strong throughout the sales process, not just at the end. John Rosso, author of Prospect the Sandler Way, talks about how to avoid sales calls that go nowhere or the dreaded think-it-over at the end.
How to Succeed at Active Listening
May 21, 2018 in Management & Leadership
Learn how to improve your attitude, behavior, and technique in active listening. Frank Moore talks about paying attention, paraphrasing, and other best practices for engaging conversations.
How to Succeed and Keep Succeeding
April 9, 2018 in Professional Development
Learn how to get to the top and stay there. Jody Williamson, long-time Sandler trainer, speaker, and author, joins us to talk about the future of selling and the dangers of success. What you do after you find success is just as important as what you do on the way up...
How to Succeed at the HVAC Industry
March 30, 2018 in Sales Process
Matt Rister, Sandler trainer, talks to Dave Mattson about the ins and outs of the HVAC industry. Matt has experience in the industry and talks about how to succeed at selling the Sandler way in heating and air.
How to Succeed at Gaining Evidence
March 2, 2018 in Sales Process
Tim Roberts, Sandler trainer from Indianapolis, joins Dave Mattson to talk about how to find and collect verifiable proof of your prospects needs, budget, and decision-making process throughout the sales cycle.
How to Succeed the Decision Timeline
February 23, 2018 in Sales Process
Dave Mattson, CEO of Sandler Training, talks to Hamish Knox, Sandler trainer and two-time author of Accountability the Sandler Way and Change the Sandler Way, about working through the decision timeline with a prospect. You will learn why decision is a part of the qualification process with a client and what you can do about it.
How to Succeed at the Four Hats of Leadership
Dave Mattson, President and CEO of Sandler Training, talks to Tom Niesen about selling the Sandler way and the four different hats that sales leaders have to wear. Learn why each is crucial to your team's success.
How to Succeed at Dealing with the Games People Play
February 12, 2018 in Professional Development
Matthew Newberger joins us to talk about our recently revised No Guts No Gain program and how to deal with the games and powerplays people make. Whether it is for negotiation, co-worker relationships, or in your personal life, it is important to be able to identify, deal with, and remove yourself from the games people play.
How to Succeed at the Investment Triangle
February 9, 2018 in Professional Development
Dave Mattson, President and CEO of Sandler Training, interviews John Rosso, a Sandler Trainer, about how to transition from the Pain step to the Budget step using the investment triangle. This is a special episode of the Selling the Sandler Way series.
How to Succeed at Using Third Party Stories
February 2, 2018 in Sales Process
This selling the Sandler way episode is all about using stories in your sales process. Dave Mattson, President and CEO of Sandler Training, interviews Sean Coyle, one of our corporate trainers and prospecting experts about how to use third-party stories to engage your prospects emotionally in the sale.
How to Succeed at Hacking the Rockstar Attitude
Episode 100 brings a very special guest! Mark Schulman is currently the drummer for PINK, and he has played with Foreigner, Billy Idol, Beyonce' & Cher. He is also a celebrity keynote speaker and author of Conquering Life's Stage Fright that provides three simple concepts to boost attitude and performance for through compelling stories about working with world-class artists.
How to Succeed at Getting Back to the Basics
This year, we are combining the Selling the Sandler Way podcast series with this show, and adding some special audio blog episodes. On Fridays, Dave Mattson, our President and CEO, will interview Sandler trainers about how to succeed in sales and sales leadership. This first episode is all about getting back to the basics of selling the Sandler way.
Creating Equal Business Stature in Your Sales Calls
How to Succeed at Networking
December 25, 2017 in Professional Development
Clint Babcock talks about joining the right groups, getting your networking plan together, and other best practices for succeeding at business networking as part of your prospecting plan.
Recovering from a Bad Sales Call
December 8, 2017 in Sales Process
How to Succeed from the Inside Out
November 20, 2017 in Professional Development
How to Succeed at Surviving and Thriving
October 30, 2017 in Professional Development
Selling the Sandler Way 27-Questioning-Chinese Menu with Danny Wood [PODCAST]
October 27, 2017 in Sales Process
No-Pressure Prospecting [PODCAST]
October 6, 2017 in Prospecting & Qualifying
The Buyer-Created Solution [PODCAST]
September 15, 2017 in Sales Process
How to Succeed at Fundraising for Non-Profits [PODCAST]
August 14, 2017 in Sales Process
Mike Crandall, a Sandler trainer and author from Oklahoma, talks about his best practices for fundraising, including asking for money, creating a plan, and getting introductions to the right people. Mike shares his attitudes, behaviors, and techniques for raising more money and doing it with a sales mindset.
How to Succeed at Asking Questions
August 7, 2017 in Prospecting & Qualifying
Antonio Garrido, Sandler trainer, and new author of Asking Questions the Sandler Way joins us to talk about the best sales questions. You will learn his favorite questions, the right attitude for asking questions, and why you should be asking more and better questions in the first place.
How to Succeed at Managing Expectations [Podcast]
July 17, 2017 in Professional Development
Amy Woodall, a Sandler trainer, talks about her best practices for managing expectations. Whether you are setting the first appointment with a prospect, setting clear guidelines for delivery, or just talking with your co-workers or spouse, setting expectations can be the difference between success and failure. Amy shares attitudes, behaviors, and techniques for setting clear up-front agreements with others.
How to Succeed at Selling Something No One Has Ever Heard of [Podcast]
July 3, 2017 in Sales Process
Matt Pletzer, a Sandler Trainer, shares his best practices for selling something new that no one has ever heard of. Sure, we would all love to be Apple and have people talking about us all the time, and people lined up to buy our new products. Unfortunately, most salespeople have to try to open doors and new markets when the prospects have never heard of you. In this episode, Matt talks about the attitude, behavior, and technique of doing just that.
How To Succeed at the End of the Month
May 22, 2017 in Sales Process
Gabe Larson, Director of the InsideSales.com Labs and host of the Sales Acceleration podcast, joins us for a special conversation about the end of the month. Gabe's team has just released new information about the best and worst practices of sales teams at the end of the month. Learn what to do and what not to do to make the most of the last few days of the quota period.
How to Succeed at Getting Unblocked
Lindsay Harle- Kadatz, Sandler client and author of "Depression Constipation," joins us for a special conversation about mental health in sales and entrepreneurship. Lindsay talks about how journaling and small actions helped her to get unblocked and moving again.
Selling the Sandler Way - Buyer Seller Dance
We are proud to introduce a new Sandler podcast, Selling the Sandler Way with host Dave Mattson, the President and CEO of Sandler Training. He is a five-time bestselling author, speaker, trainer, and consultant to hundreds of international organizations. In this show, he talks to other Sandler trainers about the Sandler Selling System. Listen to episode one in which Dave discusses the psychology behind the sale with Sandler Trainer, Pat Heidrich.
How to Succeed at Patient Care
May 4, 2017 in Professional Development
Welcome to the How to Succeed Podcast, the show that helps you get to the top and stay there. This is how to succeed at patient care. The show is brought to you by Sandler, the worldwide leader in sales, management and customer service training. For more information on Sandler, visit Sandler.com. I'm your host, Mike Montague, and my guest this week is Donna Bak. She is a Sandler trainer from Connecticut and brand new author of the new Sandler book, "Patient Care The Sandler Way". We're going to talk to her about how to succeed at patient care. Donna, welcome to the show. Tell me a little bit about patient care and who should be listening today.
The How to Succeed Podcast Year-In-Review
April 13, 2017 in Professional Development
Wow, what an amazing first year for the How to Succeed Podcast! Thank you so much for helping to make it as a success. The podcast was launched last April with 5 episodes and quickly hit the charts in the new and notable section in iTunes. Now, with over 60 shows in the can, we can truly say that we are helping people get to their best and stay there. The show has received over 90,000 total downloads, and it is now averaging over 15,000 per month. We have had listeners in 92 countries.
How to Succeed at Customer Service
June 30, 2016 in Management & Leadership
Customer service is an interesting aspect of any business. Whether you call it inside sales or customer care, your frontline employee may have the most difficult job in the company. Have you ever cringed when listening to one of your frontline people on the phone? Do you find your staff to be too strict with the policies or too loose?
How to Succeed at Coaching [Podcast]
There is no one-size-fits-all sales coaching model. There are only approaches that have been shown to be successful in particular situations. Acting as a coach, the manager must identify each salesperson's personal "success code" – and use that code to unlock the salesperson's potential for success.
How to Succeed at Enterprise Selling [Podcast]
June 6, 2016 in Prospecting & Qualifying
Special guest, Brian Sullivan, Sandler trainer and author shares his thoughts about how to succeed at enterprise selling. It is an inside look at the new Sandler Training book, Sandler Enterprise Selling: Winning, Growing, and Retaining Major Accounts.
How to Succeed at Social Selling [Podcast]
April 27, 2016 in Prospecting & Qualifying
You don't have to be a tech wizard to grow your business through social selling. You can use your network of business contacts, to take a proactive, client-centered view of prospecting on social media. This special bonus podcast cuts through all the noise, tech-speak, and misconceptions about LinkedIn and social selling.


Scout Gaming has entered the Brazilian market after signing a deal to provide start-up operator Jogajà with its full fantasy sports platform.
Jogajà, whose Scout Gaming-powered platform is expected to launch during the first half of 2019, is backed by Swedish venture capitalists with experience of working in Brazil. The product range will be boosted by the inclusion of local leagues.
The Jogajà team will be led by Jacob Lindorff, an industry executive who has worked for the likes of Expekt, Betway and Bonnier Gaming, where he served as CMO between April 2011 and July 2014.
Brazil has already made today's industry news on SBC Americas for a bill which, if passed, would authorise the opening of resort hotels with casinos across the country.
According to the proposals, states with a population of 15 million or less would be permitted to open one casino, while states with populations between 15 million and 25 million would have two. Only São Paolo, with its 45 million inhabitants, would qualify for a third.


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Collectively driving the future of Global Logistics
To become THE scoring company for Professional Truck Drivers by building a FICO-style Commercial Driver Score, allowing for automated risk assessment for Shippers, Manufacturers, and other vested Supply Chain Companies – and in the process creating new revenue opportunities for Professional Commercial Drivers.
Company Focus
To flatten every friction point in the global supply chain; to facilitate the removal of every costly and unnecessary process in the broken transportation industry.
To build a global transportation network connected by thousands – and ultimately millions – of data points, ultimately benefiting the entire freight logistics ecosystem, consumers, and the environment.
The Core Team
RICK BURNETT
Position: CEO & Founder
Rick's extensive knowledge of mobile technology, blockchain technology, and deep experience in trucking and logistics makes him a natural fit to lead the LaneAxis team. Rick spent 15 years researching and developing a mobile-based platform that provides real-time tracking and data analytics over freight movements. Rick's software and transportation expertise culminated with the design of a shipper-to-carrier direct optimization platform that was granted a full U.S. Patent in March, 2018.
Rick's extensive knowledge of mobile technology, blockchain technology, and deep...
MASON BURNETT
Position: COO
Success-driven blockchain and transportation logistics expert currently focused on building a shipper-to-carrier direct model for global supply chain. Coordinated marketing and internal logistics planning for data analytics company. Now managing external marketing channels and internal operations for LaneAxis focused on reshaping global freight logistics sector.
Success-driven blockchain and transportation logistics expert currently focused on building...
Himanshu Yadav
Position: CTO
A focused and tenacious technology veteran, Himanshu's expertise ranges from blockchain system design to software programming to website development. As a full stack developer, he is fluent in virtually all coding languages. Himanshu has immersed himself in the freight logistics and supply chain industries, and is passionate about building technological solutions focused on solving the massive inefficiencies that have plagued global transportation for decades.
A focused and tenacious technology veteran, Himanshu's expertise ranges from...
Andrew Rivera
Position: CMO
Andrew is an award-winning former TV news journalist with corporate experience in Public Relations and Marketing. Spent 10+ years with Disney ABC Television. Now immersed in logistics, freight, and blockchain space, Andrew led a global marketing campaign for a successful token sale focused on a cutting edge data analytics platform. Technically and editorially savvy, he thrives on every opportunity to create imaginative writing and visuals that slice through the clutter.
Andrew is an award-winning former TV news journalist with corporate...
Position: VP of Client Relations
With over 7 years of transportation and customer support experience for a fortune 50 company, Megan has managed both the operational and documentation processes that support international container exports and domestic truck loads. Megan's specialties include project management, workflow process evaluation, maintaining US Customs regulation requirements, and managing a number of key vendors and contacts throughout the operational chain to ensure customer satisfaction.
VP of Client Relations
With over 7 years of transportation and customer support experience...
Anand Harihan
Position: VP Software and Mobile App Development
Technology architect with diverse management and product experience working for organizations ranging from start-ups to Fortune-100 corporations. Results-oriented leader with experience building products from idea to maturity. Experience in hiring and building multi-functional teams (Dev/Design/QA). Strong interpersonal and leadership skills; team-oriented approach focusing on customer needs and market success. Product development areas of focus include: Virtualization, Large Scale systems, Networking, User interface, High Availability, Enterprise Software, Storage management.
VP Software and Mobile App Development
Technology architect with diverse management and product experience working for...
Position: VP of Sales
Alex is a veteran of the transportation and logistics industry, having worked for and with freight brokerages and directly with large shippers looking to move loads. He has deep experience with the "Big 3" American automakers. Alex also worked for a forward-thinking global insurance brokerage. Alex specializes in risk management, helping clients minimize risk through data-backed insight and innovative solutions.
VP of Sales
Alex is a veteran of the transportation and logistics industry,...
Position: Sales Manager
President and CEO of the Campbell Company and Managing Director of Automotive Technology Solutions. Over 25 years of leadership and organizational building to support business development, brand development and technology implementation in the automotive and transportation industries.
President and CEO of the Campbell Company and Managing Director...
Clint Johnson
Position: Director, Logistics & Supply Chain Operations
Former Walmart logistics and supply chain executive, Clint is an accomplished, results-driven executive with a proven track record of achievement in strategic planning, merchandising, supply chain, analytics, team leadership, inventory control, auditing, plan-o-grams, trade management, and key performance indicators. Clint continually identifies opportunities to increase efficiencies and realize extensive cost-savings.
Director, Logistics & Supply Chain Operations
Former Walmart logistics and supply chain executive, Clint is an...
David Patton
Position: Director Product Innovations
For over 26 years, David and his agency have specialized in brand Marketing, sales, long-term contract negotiations, and international new product introduction into North America. Customers have included: General Motors, TRW, Ford Motor, Magna International, ZFLS, Tesla Motors, the EPA, and various freight carriers.
Director Product Innovations
For over 26 years, David and his agency have specialized...
Divine Maloney
Position: Utility Token Engineer & Researcher
A specialist in blockchain and cryptoeconomics, Divine is pursuing a PHD in Human Centered Computing at Clemson University. His research focuses on how new technologies – specifically virtual reality/augmented reality – will change the way we live and interact. He has worked with researchers at MIT, Harvard, Columbia, University of Central Florida, and Vanderbilt University. Divine is a crypto-enthusiast, who loves how this new technology will disrupt and change the world we live in. He is dedicated to making LaneAxis one of the premier utility tokens in the crypto-space.
Utility Token Engineer & Researcher
A specialist in blockchain and cryptoeconomics, Divine is pursuing a...
Charles Sellers
Position: CPA
Charles is a Senior Tax Manager with over 30 years of experience in public practice. He began his career at Ernst & Young and spent 8 years there before leaving to found his own firm, Sellers & Company. After successfully managing that practice for 20 years, he merged it into a larger firm and accepted a faculty position at San Diego State University. While there, he taught Accounting Ethics to both upper division and graduate students. In 2013, he joined McLean, Rotherham & Company, specializing in tax and estate planning for small businesses and high net worth individuals.
Charles is a Senior Tax Manager with over 30 years...
Kim "Mo" Mahoney
Position: Financial Strategy Team
Mo is a Marine F/A-18 pilot and Colonel in the Reserves, having flown in carrier and commercial aviation as well as having Fortune 250 executive level experience. Earning his MBA while still active-duty, Mo has worked in Finance since 2011 and is currently working in advanced business strategy for a Fortune 50 firm.
Financial Strategy Team
Mo is a Marine F/A-18 pilot and Colonel in the...
Jason Giorgio
Jason Giorgio has over 22 years of guiding business owners and executives with financial strategies to accelerate net worth. Nationally recognized for his performance, Jason is also a keynote speaker for various national associations, particularly on business planning and succession strategies.
Jason Giorgio has over 22 years of guiding business owners...
Sasha Ablovatskiy
Position: Legal Counsel
Sasha is a partner of Foley Shechter LLP. Sasha began his legal career as a corporate and securities associate at a well-known Wall Street based boutique law firm. He then practiced at Kaye Scholer, a large international New York based law firm with offices in Los Angeles, Germany, London and Shanghai, where he was the sole senior associate in the firm's securities practice.Sasha brings over 13 years of extensive experience to FS and focuses on counseling public and private companies on offerings of equity and debt securities, strategic investments, acquisitions and joint ventures, IPOs, reverse mergers and alternative public offerings, venture capital investments, public company reporting obligations, corporate governance matters, Sarbanes-Oxley compliance and other general corporate law matters. Sasha also works closely with startups on their capital raising needs, structure and formation, contractual matters and various other startup and commercial matters.
Sasha's experience in structuring and closing offerings, corporate finance and M&A transactions, including the legal, founder and business aspects related to such transactions, are enhanced by his know-how of having founded several startups himself and wforking with a vast array of management teams and boards of directors.
Sasha actively participates in the New York startup and venture capital community and specifically, in the tech, bio-tech, AI and crowdfunding space. Sasha is also an angel investor.
Sasha is a partner of Foley Shechter LLP. Sasha began...
Jonathan Shechter
Position: Legal Team
Jonathan is a founding partner of Foley Shechter LLP. Previously, Jonathan was a corporate and securities attorney with a Wall Street based law firm, as well as the Head of the Corporate Finance group of a New York firm with affiliate offices in Tel Aviv, Israel and Shanghai, China, where he was principally engaged in the practice of corporate and securities law. Jonathan brings extensive experience in structuring and closing corporate finance and M&A transactions, including the legal and practical business aspects related to such transactions, having worked with a vast array of management teams and boards of directors.
Jonathan focuses on representing clients in a wide variety of general corporate and finance transactions, with a concentration on secured and unsecured private equity financing transactions as well as registration statements associated with these financings, public offerings, joint ventures, employment and shareholder agreements, as well as with the formation of private equity funds.
Jonathan also represents publicly held companies with their 1934 Act reporting obligations and in the representation of broker-dealers and registered individuals in a wide array of matters, including ongoing compliance and regulatory inquiries.
Jonathan is a founding partner of Foley Shechter LLP. Previously,...
Carl "Coach" Hargrave
Position: Driver Relations "Truckercize"
As a former coach in the National Football League for 11 years, as well as an extensive tenure of 18 years as an assistant coach at the major college level, Carl Hargrave coached three seasons with the Arizona Cardinals, originally joining Head Coach Dennis Green's staff as an Offensive Consultant in 2004. In 2005, Hargraves contributions helped the Cardinals' finish the season with the 8th-ranked overall offense in the NFL, and the #1 passing offense (4,437 yards/277.3 yards per game) for the first time in team history. Prior to his tenure with the Cardinals, Hargrave was an assistant with the Minnesota Vikings from 1994-2001, and worked primarily with the running backs. In his eight seasons in Minnesota, the Vikings compiled a 77-51 record, made six playoff appearances, won three NFC Central titles, and twice advanced to the NFC Championship Game.
During Hargrave'sstint with the Vikings, the 1998 team set the NFL scoring record with 2,563 points, a record that stood for ten seasons until then Super Bowl Champion New England eclipsed that mark in 2008.Additionally, the Vikings offense set a league record for team rushing in 2000 with 2,565 yards which still exists today.
Hargrave coached in two Pro Bowls, where he tutored some of the great running backs in the NFL at that juncture. This list includes his own Minnesota Viking Robert Smith, Dallas Cowboy Hall-Of-Famer Emmitt Smith, Tampa Bay Buccaneer Mike Alstott, and Atlanta Falcon Jamal Anderson. Hargrave has been a vital component in the lives of numerous student athletes, having coached at the college level. With successful stints at Northwestern University, The University of Pittsburgh, The University of Houston and The University of Iowa, Hargrave balanced the delicate scales of athletic involvement with the priority of education.
"Football is a career, education is for life," says Hargrave. One of the proudest moments in Hargrave's career involved witnessing one of his college understudy's, Andre Ware, win the Heisman Trophy while at the University of Houston in 1989. During Hargrave's stretch with the Cougars, the school set 88 individual NCAA offensive records along with 36 team NCAA marks.
Driver Relations "Truckercize"
As a former coach in the National Football League for...
Ellie Burnett
Position: Internal Logistics Coordinator
Ellie manages internal logistics for LaneAxis events, conferences, and a multitude of company initiatives. With a degree in Business Management, Ellie most recently partnered with the founder of beauty app MiaMia on marketing initiatives and company outreach. Ellie also has retail marketing experience with such prestigious companies as COACH and Kate Spade & Company.
Internal Logistics Coordinator
Ellie manages internal logistics for LaneAxis events, conferences, and a...
Kevin Morgan
Position: Senior Corporate Advisor
Kevin Morgan has a 35 year career manufacturing engineered components for the Rubber and Plastics industry. He's worked for domestic and international corporations, including Southland Industries, Duramax Corporation, and Dowty Seals Ltd, a UK company. Kevin is currently President of start-up Morgan Polymer Seals LLC, incorporated in 1997. Morgan Polymer Seals has since grown to over 600 employees and manufactures powertrain components for GM, Ford, Fiat Chrysler, Audi, Porsche, Honda, Volkswagen and Land Rover. Kevin and his brother also purchased a $5 million per year composite extrusion operation.
Kevin Morgan has a 35 year career manufacturing engineered components...
Phil Treglia
Position: Strategic Advisor
A former Lieutenant Colonel in the United States Marine Corps, Phil's enthusiastic approach to team collaboration and discipline keeps the LaneAxis team focused and prepared. Phil is a strategic minded Operations Executive and Advisor with strong process improvement skills. Phil provides the leadership, team building, and direction necessary to achieve LaneAxis' performance objectives.
A former Lieutenant Colonel in the United States Marine Corps,...
Want to join a team committed to reinventing the logistics industry?
If you have a passion for freight logistics, technology, and transforming the global supply chain – we want to hear from you.
LaneAxis Inc.
419 Main Street, #121
Huntington Beach, CA. 92648
marketing@laneaxis.com
Copyright © 2015-2019 LaneAxis, Inc.

redpajama_set_name: RedPajamaCommonCrawl


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Coordination Council
Expert Group
Tihomir Strizrep
An international consultant in health care financing with experience in Central and Eastern Europe (Croatia, Montenegro, Armenia, Kazakhstan, Ukraine).
Tatiana Dumenko
Director of rational pharmacotherapy and support of state formulary system at State Expert Center, MoH Ukraine, authorized representative for the Ministry of Health of Ukraine in WHO cooperation centers on pricing and reimbursement of medicines.
Roman Fishchuk
ENT specialist, Master of Business Administration in Health Care.
Senior health economist, World Health Organization expert on universal access to health care.
Oleg Petrenko
First Deputy General Director of private multidisciplinary clinic "Oberig", Advisor to the Minister of Health in 2005-2007.
Mykola Prodanchuk
Director of scientific center for preventive toxicology, food and chemical safety named after L. I. Medvid.
Igor Yakovenko
Freelance Advisor to the World Bank on health financing, Deputy Minister of Health of Ukraine in 2008-2010.
Volodymyr Kurpita
Head of SAG Expert Board, health specialist at WHO Country Office in Ukraine (2005-2008), Executive Director of the All-Ukrainian Network of People Living with HIV/AIDS (2008-2014),Head of State Service of Ukraine on combating HIV/AIDS and other socially dangerous diseases (2014).
Antonio Duran
International consultant on organization and policy in health sector with extensive experience in Central and Eastern Europe (Croatia, Ukraine, Bulgaria).
Andriy Huk
Expert on public health and health systems organization with considerable experience in international projects in Ukraine.
Alexander Kvitashvili
Minister of Labor, Health and Social Policy of Georgia in 2008-2010, an international consultant on implementation of social, educational and humanitarian policy.
Ain Aaviskoo
eHealth consultant, head of the group that initiated eHealth in Estonia.
One thought on "Expert Group"
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Domett Street
46 Domett Street
46 Domett Street Kawerau, 3127
46 Domett Street , Kawerau, 3127
Last Sold on 8 Dec 1986 for $36,000
About 46 Domett Street, Kawerau, 3127
46 Domett Street, Kawerau, 3127 is a Residential property built in 2008 with 2 bedrooms, 1 bathroom and 1 parking space. The property is estimated to be valued in the range of $400,000 to $450,000.
The property last sold for $36,000 on 8 December 1986. On 1 August 2021, 46 Domett Street, Kawerau, 3127 had a Rating Valuation with a Capital Value of $360,000, Land Value of $129,000 and Improvement Value of $231,000.
8 Dec 1986 SOLD $36,000
35 years 1 month 12 days
22 Domett Street, Kawerau, 3127 22 Domett Street, Kawerau, 3127
16 Fitzgerald Street, Kawerau, 3127 16 Fitzgerald Street, Kawerau, 3127
16 Newall Street, Kawerau, 3127 16 Newall Street, Kawerau, 3127
27 Onslow Street, Kawerau, 3127 27 Onslow Street, Kawerau, 3127
16 Robinson Street, Kawerau, 3127 16 Robinson Street, Kawerau, 3127
22 Hobson Street, Kawerau, 3127 22 Hobson Street, Kawerau, 3127
6 Hinemotu Avenue, Kawerau, 3127 6 Hinemotu Avenue, Kawerau, 3127


It's been almost 8 months since we left our home in Nashville, TN and crossed the United States to enter Mexico via Tecate on the Baja Peninsula. We have now made our way across the entire country, are a couple of weeks from our second border crossing into Belize and its gotten me thinking about how different daily life is now than it was this time last year – not to mention how much we have learned about ourselves as individuals and as a couple.
We left successful careers, our cozy East Nashville home, our friends, family, the security of medical insurance, the comforts of on command air conditioning and the ease of living in a place where everyone speaks your language, literally and figuratively. Our daily existence was pretty normal.
Our life now resembles nothing of what it did just a few short months ago. We have learned a lot, we have adjusted a lot, we have come to accept a very different version of normal.
The first, and maybe hardest lesson: Not everything changes. Just because you leave your life behind and hit the road doesn't mean all of your problems or responsibilities disappear.
At first glance, from postings of photographs and route maps and updates filled with tequila toasts and playtime with monkeys, this may seem to be a carefree way of living. Sometimes, yes, it is. Actually, more and more. But our previous lives still creep in.
You don't entirely stop being who you were – if you were someone who worried about the future before, you will still worry about the future. Thoughts such as "We left EVERYTHING." "We sold EVERYTHING." "We might have NOTHING in about about a year," will still creep in just as you are watching a glittering lightshow of shooting starts from inside your rooftop tent under a perfect sky.
Unless you hit the lottery to do this or sold some million-dollar app, you will still have bills and debts from your previous life. Leaving on an epic journey across 23 countries doesn't stop your responsibilities; it doesn't stop your need for things like car insurance or cell phone bills. These things DO get better, because out of an increasing need for freedom, you face and manage more of them and you rack up far less but they don't magically disappear.
There will still be the daily tasks of life – laundry, dishes, car maintenance, grocery shopping and errands to run. Only now, each of those tasks will take an entire day or longer because you don't know the language well, you don't know the landscape, you don't know the town. Ever. You will be brand new in every place you are, every single time you need to accomplish anything. These things also take on new meaning and value, you will do dishes under a waterfall or with ocean water. You will take your laundry to a local woman who needs the work to feed her family, you will try new foods and ways of preparing dishes. Errands will become new friendships, funny exchanges and stories to tell.
You won't stop loving and missing your family and friends. Doing something like this doesn't change the fact that your family needs you. It doesn't stop your grandfather from slipping into dementia. It doesn't stop your need to comfort your mother when she is feeling overwhelmed and alone. It doesn't stop you from wanting to eat nothing but her chicken soup when you are sick from some unknown foodborne illness. This kind of life doesn't stop you from wishing you were more a part of your friends' lives or worrying that they will forget about you. This isn't an escape from your life; you take who you are and who you were with you. You want the people you love to care about what you are doing - you want them to follow your posts, check your weekly route progress, read your blog (I just laughed out loud when i wrote that), you want them to think its exciting and cool even if they think you are out of your mind for doing it.
First, we plan a lot less far in advance. We are learning to LIVE IN THE PRESENT. There is no room for anything else. We never know what is coming ahead – we don't know what road or weather conditions may be on our next day's route. We don't know if we will like a place or hate it. Generally, we can't plan more than two days in advance. Sometimes, even that is a stretch. We have to TAKE EACH DAY AS IT COMES.
It is so different than our previous lives. Before, we had schedules planned weeks in advance, filled with meetings, deadlines and projects, vacations booked months ahead, dinner reservations and Sunday fundays. Now, we really take in every moment because it's the only moment we have for certain. There is no looking ahead to what's next because we have no idea what's next. There is very little looking back because we are figuring things out and taking in what's going on in the present. This trip has taught us to soak in things we would only have taken brief note of before – colors, skies, smells, people we meet.
We have learned to FOCUS ON NOW, on what's right in front of us. I have found myself listening to what people say to me in a way I was never good at before, studying their expressions and mannerisms, hearing the tone of their voice. This change came about from basic attempts at communication. Being present and in the moment with someone is your only option when you don't speak the same language but need to convey something important.
Second, we PAY ATTENTION TO THE ELEMENTS Never before have wind, water and sunlight played such a role in the ongoings of my day. We live in a tent on top of our car. There is no real shelter or living space. If it's too windy, our stove has trouble lighting. We have to make sure we have enough fresh purified water every day to drink, cook, wash our hands, keep our dog hydrated. We wake up with the sunrise and fall asleep when its dark. We have started to smell rain hours before it begins and notice how altitude can affect our moods.
Third, We are learning that LESS IS REALLY MORE. I repeat, we live out of a car. Our Landcruiser has very limited space, especially with a portable refrigerator and large dog sharing the small bit of room that we have. When we pulled out of our driveway in Nashville, things we thought we needed for daily survival were tucked into every crevice. You couldn't open a door without items falling out. It was miserable. Now, we find relief and freedom in anything we can get rid of. We have slimmed down our belongings by at least 50% since we left. And every purge makes us happier. THE ESSENTIALS ARE ALL WE HAVE AND ALL WE NEED. Sometimes, we go into cities with intentions of buying all kinds of things that we can't get out on the road – and leave those cities empty handed because buying things just doesn't hold the same thrill it once did. There is just so little we actually need. Our current daily needs include figuring out where we will camp, preferably with clean bathrooms and a hot shower, making sure we have enough food and water for that day, finding gas stations, generally trying to land in a place with either phone service or wifi, setting up camp, sleeping, taking down camp, moving on to the next place. The less we have, the easier all of this becomes and the happier we are.
Fourth, we APPRECIATE DIFFERENT THINGS THAN WE USED TO – simpler things that were once taken for granted and now, are gifts from the universe, worthy of the little happy dance that comes when we have them. HOT SHOWERS. CLEAN CLOTHES. GOOD CHEESE. BATHROOMS WITH TOILET SEATS. ICE. And the holy grail – real CREAM FOR OUR MORNING COFFEE. These are the things that get our clocks ticking.
We have learned to REALLY TRUST EACH OTHER. I have gained so much information about how capable Karin is as a person, as a partner and in life. She can figure anything out. Her optimism when I was panicking about some little thing gone wrong used to wear me out. Now, I've seen her literally, figure out ANYTHING, and it gives me tremendous security. These Mexican roads, when there are actual roads, are often treacherous – filled with giant holes, invisible speed bumps, mile long drop offs, donkeys, dogs, you name it. Karin is a GREAT driver, exceptionally qualified for a journey like this. She has kept us safe and I have come to accept that as simply the way it is. If its drivable, she can drive it. She is brilliant and courageous in ways I never saw coming. For example, I never would have guessed she would be so….okay staying in Mexico City by herself for a week when I had to go home for a family emergency. Not only was she okay but she also thrived, making the city and the experience her own.
We are learning to GIVE EACH OTHER SPACE, AND QUIET when one of us senses the other needs it. Its not an easy thing to learn to notice or to give without condition or subtext but I've come to believe in the power of giving another person what they need, before they have to ask for it but especially if they do.
People told me that this type of trip, being with each other 24/7 for months at a time would make or break us. It has only made us stronger. I love her and see her in a completely different way. I can't speak for her but I have felt her love and respect for me grow as well.
Finally, we have learned to TRUST OURSELVES. I know now that we can handle any situation that may arise. Stuck deep in the sand on a deserted beach in Mexico with no other people for miles? No problem! We planned for this – we have the gear we need to get out. Lost in the mountains without working GPS or phone signal? No problem! We have a paper map, a compass, big smiles and an ability to talk to locals in a way that makes them want to help. Policia trying to shake you down for cash on the side of the road with a bogus claim that your dog is supposed to be in a cage while riding in your vehicle? No sweat! We know how to be the right amounts of firm and polite in letting them know we won't be messed with. We are CAPABLE.
I don't think these changes in our daily routines and perspectives will be lost upon our return to a more regular life. I don't think I will ever forget how much happier simplicity and presentness have made me.
I also don't think I will take for granted again the things from home that I miss the most – the sound of my mother's laughter, meeting with friends for drinks and hearing the stories from their daily lives, easily accessible organic markets, bookstores, walls, electricity, clean water, juice bars (this one really gets me).


text:
Evolution, developmental expression and function of odorant receptors in insects
Hua Yan, Shadi Jafari, Gregory Pask, Xiaofan Zhou, Danny Reinberg, Claude Desplan
Biology and Genomics
Animals rely on their chemosensory system to discriminate among a very large number of attractive or repulsive chemical cues in the environment, which is essential to respond with proper action. The olfactory sensory systems in insects share significant similarities with those of vertebrates, although they also exhibit dramatic differences, such as the molecular nature of the odorant receptors (ORs): insect ORs function as heteromeric ion channels with a common Orco subunit, unlike the G-protein-coupled olfactory receptors found in vertebrates. Remarkable progress has recently been made in understanding the evolution, development and function of insect odorant receptor neurons (ORNs). These studies have uncovered the diversity of olfactory sensory systems among insect species, including in eusocial insects that rely extensively on olfactory sensing of pheromones for social communication. However, further studies, notably functional analyses, are needed to improve our understanding of the origins of the Orco-OR system, the mechanisms of ORN fate determination, and the extraordinary diversity of behavioral responses to chemical cues.
jeb208215
The Journal of experimental biology
Pt Suppl 1
https://doi.org/10.1242/jeb.208215
Odorant Receptor
Insecta/genetics
Insect Proteins/genetics
Receptors, Odorant/genetics
10.1242/jeb.208215
Dive into the research topics of 'Evolution, developmental expression and function of odorant receptors in insects'. Together they form a unique fingerprint.
Odorant Receptors Medicine & Life Sciences 100%
odorant receptors Agriculture & Biology 96%
Insecta Medicine & Life Sciences 75%
chemical cue Earth & Environmental Sciences 42%
sensory system Earth & Environmental Sciences 41%
neurons Agriculture & Biology 23%
Yan, H., Jafari, S., Pask, G., Zhou, X., Reinberg, D., & Desplan, C. (2020). Evolution, developmental expression and function of odorant receptors in insects. The Journal of experimental biology, 223(Pt Suppl 1), [jeb208215]. https://doi.org/10.1242/jeb.208215
Evolution, developmental expression and function of odorant receptors in insects. / Yan, Hua; Jafari, Shadi; Pask, Gregory et al.
In: The Journal of experimental biology, Vol. 223, No. Pt Suppl 1, jeb208215, 01.02.2020.
Yan, H, Jafari, S, Pask, G, Zhou, X, Reinberg, D & Desplan, C 2020, 'Evolution, developmental expression and function of odorant receptors in insects', The Journal of experimental biology, vol. 223, no. Pt Suppl 1, jeb208215. https://doi.org/10.1242/jeb.208215
Yan H, Jafari S, Pask G, Zhou X, Reinberg D, Desplan C. Evolution, developmental expression and function of odorant receptors in insects. The Journal of experimental biology. 2020 Feb 1;223(Pt Suppl 1):jeb208215. doi: 10.1242/jeb.208215
Yan, Hua ; Jafari, Shadi ; Pask, Gregory et al. / Evolution, developmental expression and function of odorant receptors in insects. In: The Journal of experimental biology. 2020 ; Vol. 223, No. Pt Suppl 1.
@article{981c2dde8437410fbae9b79646bea9a2,
title = "Evolution, developmental expression and function of odorant receptors in insects",
abstract = "Animals rely on their chemosensory system to discriminate among a very large number of attractive or repulsive chemical cues in the environment, which is essential to respond with proper action. The olfactory sensory systems in insects share significant similarities with those of vertebrates, although they also exhibit dramatic differences, such as the molecular nature of the odorant receptors (ORs): insect ORs function as heteromeric ion channels with a common Orco subunit, unlike the G-protein-coupled olfactory receptors found in vertebrates. Remarkable progress has recently been made in understanding the evolution, development and function of insect odorant receptor neurons (ORNs). These studies have uncovered the diversity of olfactory sensory systems among insect species, including in eusocial insects that rely extensively on olfactory sensing of pheromones for social communication. However, further studies, notably functional analyses, are needed to improve our understanding of the origins of the Orco-OR system, the mechanisms of ORN fate determination, and the extraordinary diversity of behavioral responses to chemical cues.",
keywords = "Ant, Development, Diversity, Drosophila, Evolution, Odorant Receptor, Olfaction, Orco, Insecta/genetics, Insect Proteins/genetics, Pheromones, Smell, Animals, Receptors, Odorant/genetics",
author = "Hua Yan and Shadi Jafari and Gregory Pask and Xiaofan Zhou and Danny Reinberg and Claude Desplan",
note = "Funding Information: This work was supported by a Howard Hughes Medical Institute Collaborative Innovation Award (HCIA; #2009005 to D.R. and C.D.), the National Institutes of Health (NIH; grants R01 AG058762 to D.R. and C.D., and R01 EY13010 to C.D.), the University of Florida Center of Smell and Taste (UFCST; seed grant to H.Y.) and the Swedish Research Council (Vetenskapsr{\aa}det; VR grant 2016-06726 to S.J.). Publisher Copyright: {\textcopyright} 2020. Published by The Company of Biologists Ltd.",
doi = "10.1242/jeb.208215",
journal = "Journal of Experimental Biology",
number = "Pt Suppl 1",
T1 - Evolution, developmental expression and function of odorant receptors in insects
AU - Yan, Hua
AU - Jafari, Shadi
AU - Pask, Gregory
AU - Zhou, Xiaofan
AU - Reinberg, Danny
AU - Desplan, Claude
N1 - Funding Information: This work was supported by a Howard Hughes Medical Institute Collaborative Innovation Award (HCIA; #2009005 to D.R. and C.D.), the National Institutes of Health (NIH; grants R01 AG058762 to D.R. and C.D., and R01 EY13010 to C.D.), the University of Florida Center of Smell and Taste (UFCST; seed grant to H.Y.) and the Swedish Research Council (Vetenskapsrådet; VR grant 2016-06726 to S.J.). Publisher Copyright: © 2020. Published by The Company of Biologists Ltd.
N2 - Animals rely on their chemosensory system to discriminate among a very large number of attractive or repulsive chemical cues in the environment, which is essential to respond with proper action. The olfactory sensory systems in insects share significant similarities with those of vertebrates, although they also exhibit dramatic differences, such as the molecular nature of the odorant receptors (ORs): insect ORs function as heteromeric ion channels with a common Orco subunit, unlike the G-protein-coupled olfactory receptors found in vertebrates. Remarkable progress has recently been made in understanding the evolution, development and function of insect odorant receptor neurons (ORNs). These studies have uncovered the diversity of olfactory sensory systems among insect species, including in eusocial insects that rely extensively on olfactory sensing of pheromones for social communication. However, further studies, notably functional analyses, are needed to improve our understanding of the origins of the Orco-OR system, the mechanisms of ORN fate determination, and the extraordinary diversity of behavioral responses to chemical cues.
AB - Animals rely on their chemosensory system to discriminate among a very large number of attractive or repulsive chemical cues in the environment, which is essential to respond with proper action. The olfactory sensory systems in insects share significant similarities with those of vertebrates, although they also exhibit dramatic differences, such as the molecular nature of the odorant receptors (ORs): insect ORs function as heteromeric ion channels with a common Orco subunit, unlike the G-protein-coupled olfactory receptors found in vertebrates. Remarkable progress has recently been made in understanding the evolution, development and function of insect odorant receptor neurons (ORNs). These studies have uncovered the diversity of olfactory sensory systems among insect species, including in eusocial insects that rely extensively on olfactory sensing of pheromones for social communication. However, further studies, notably functional analyses, are needed to improve our understanding of the origins of the Orco-OR system, the mechanisms of ORN fate determination, and the extraordinary diversity of behavioral responses to chemical cues.
KW - Ant
KW - Development
KW - Diversity
KW - Drosophila
KW - Evolution
KW - Odorant Receptor
KW - Orco
KW - Insecta/genetics
KW - Insect Proteins/genetics
KW - Pheromones
KW - Smell
KW - Receptors, Odorant/genetics
U2 - 10.1242/jeb.208215
DO - 10.1242/jeb.208215
JO - Journal of Experimental Biology
JF - Journal of Experimental Biology
IS - Pt Suppl 1
M1 - jeb208215


text:
Czech shopping mall boom looks set to continue
https://english.radio.cz/czech-shopping-mall-boom-looks-set-continue-8595377#player=on
The Czech Republic has seen a massive increase in the number of shopping malls in recent years, with around 60 of them dotted around the country. Rare a decade ago, large shopping centres are now part of the everyday lives of millions of Czechs. And while the number of malls keeps on growing, some major retailers are also making inroads into the corner shop market.
In the centre of Prague you will find Palladium, one of the latest and, with 170 shops, biggest shopping malls in the Czech Republic. The explosion in shopping centre building began only a decade ago, but has become a real phenomenon. I asked a few people in front of Palladium for their views on that development.
Woman: "For those who don't have much time, say people who are at work all day, it's practical – they can find everything under one roof. But on the down side, they're bad for small businesses – they swallow them up."
Second woman: "I don't like them, because I don't like really crowded places – I get kind of overwhelmed. And they're all the same – the same shops, the same people, nothing new."
Man: "I think they're great, because they make it possible to buy everything in one place and they increase competition. They're great."
Tomáš Drtina is the head of INCOMA Research, who are experts in the retail field in the Czech Republic:
"One of the reasons why we have that many shopping centres here is that our regulations are not as strict as for example in the Netherlands of the UK. Which enables developers to, let's say, introduce more projects."
Mr Drtina tells me the Czech Republic probably has more shopping malls per head than any other country in the central and eastern Europe region – and traces developments over the last decade.
"In the very first wave, in the late '90s, there were mainly edge-of-town projects, large shopping centres, usually looking like a hypermarket plus a simple shopping mall. Now the structure is much more differentiated. We have retail parks, we have inner-city shopping malls, we have the first outlet centres…so we have a, let's say, interesting offer for all kinds of customers."
And some of the biggest players are hoping to increase their market share – by opening relatively small stores. Travel a short distance from the tourist-filled centre of Prague and you'll find one of the first branches of Tesco's Expres chain of small shops, which follow a model that has proven successful in the UK. Tesco, the Polish chain Zabka and others are slowly but surely making inroads into the Czech local retail segment. Tomáš Drtina:
"Most of the big international retailers would like to introduce smaller concepts now – not just big hypermarkets but also small supermarkets or even smaller stores. Different types of customers prefer different types of store formats. But it takes time and it's really important to use good locations, and there are not that many good locations. Definitely their networks will be larger in the future, but it will really take many months and years."
In the meantime, the boom in big shopping malls looks set to continue: on top of the 60 or so already in the Czech Republic, Tomáš Drtina says there are plans to build another 50 in the coming years.
Savarin Palace in central Prague set for major renovation
Tesco to sell its Prague My store
Retail mall development resumes in Czech Republic
Construction of new shopping and office complex in Prague 7 appears set to resume


text:
\section{INTRODUCTION}

Continuous contact sensing is crucial for robot manipulation or tactile exploration as these activities usually require the robot to be in continuous contact with objects. Furthermore, it is frequently desirable to use compliant touch sensors, which make the perception more challenging due to motion dependency caused by their sensitivity to shear \cite{Cramphorn2018}. Shear deforms the sensor (Fig.~\ref{fig:setup}) depending on the sliding direction, thus making sensor readings history dependent.

The novelty of this work is to verify the hypothesis that features found in independent tactile readings (taps) can also be extracted from data perturbed by sliding motion. Sliding motion causes the sensor skin to deform, and so sensor readings would depend on both the tactile features of the object and the shear direction. Thus, while discrete tap data is similar for the same tactile features, with sliding motion those features can produce completely different readings (Fig.~\ref{fig:sliding}). This paper finds a link between discrete tactile data (taps) and movement dependent data by showing that a linear transform can extract features of interest despite the sliding.

For validation, we apply a simple perception method trained on discrete contact data (taps) to continuous
contact data collected offline and to continuous
contact data for contour-following whilst sliding. The perception method uses principal component analysis (PCA) to extract features from
tactile data which are then mapped to the sensor pose using nonlinear regression. We also exploit PCA to visualise the
output of a soft biomimetic tactile sensor (the TacTip~\cite{chorley2009,Ward-Cherrier2018}, shown in Fig.~\ref{fig:setup}) and show that the data are strongly influenced by the sliding direction of the sensor (Fig.~\ref{fig:sliding}). This shear-invariant perception method extends previous tactile exploration studies \cite{Lepora2017,Martinez-Hernandez2017} that used tapping movements (independent tactile data) by demonstrating reliable sliding contact tactile exploration (contour-following) of various objects (Fig.~\ref{fig:setup}a) despite being trained using discrete contacts on a straight edge.

\begin{figure}[t!]
	\vspace{0.7 em}
	\centering
\begin{overpic}[width=0.38\textwidth]{Figures/setupTacTipshortNatural}
\put(2,85){\textbf{(a)}}
\put(60,85){\textbf{(b)}}
\put(60,40){\textbf{(c)}}

\end{overpic}
	\caption{(a) The experimental setup showing four 3D-printed objects, a natural object (a tape measure) and a laser-cut spiral used for contour-following. The TacTip is the end effector of a 6 axis ABB robot (IRB120). (b) Close-up of the TacTip sensor. (c) The image captured by the TacTip at no contact.}
		\vspace{-1em}
	\label{fig:setup}

\end{figure}

\section{BACKGROUND}

Sliding motion influences tactile sensing both in humans \cite{Srinivasan1990,Edin1995}, where the direction of motion results in different responses, and in artificial touch sensors, where shear deforms their outer soft layer. For example, sliding displaces the internal markers of the GelSight sensor \cite{Li2014,yuan2015} and introduces higher inaccuracies in a force/torque sensor embedded in a rubber-covered fingertip \cite{Liu2015}. Continuous tactile sensing during sliding can thus be a challenging task.

Tactile exploration is a commonly studied task which usually involves executing a sequence of discrete contacts on an object \cite{Yi2016a,Jamali2016,Martinez-Hernandez2017,Matsubara2017,Lepora2017}. Some have addressed this by using a Gaussian process (GP) model of the surface and exploring the most uncertain regions \cite{Yi2016a,Jamali2016}, or by combining the uncertainty with the cost of travel \cite{Matsubara2017}. Others used the perceived edge orientation to perform contour-following of a flat object \cite{Martinez-Hernandez2017,Lepora2017}.

Other studies perform continuous tactile exploration where the sensor is constantly in contact. Some of these studies perform tactile exploration by using a tactile servoing approach \cite{Li2013,Kappassov2016}, whilst others explore along the tangential plane of the surface \cite{Liu2015,Back2014,Driess2017}.  Typically these use either flat tactile sensor arrays \cite{Li2013,Kappassov2016}, which provide limited tactile feedback through tactile images, or force-torque measurements only \cite{Liu2015,Back2014,Driess2017}. There is thus a need for more studies that address continuous tactile exploration using tactile sensors with complex outputs (such as biomimetic sensors) that provide more information but that at the same time require a more complex perception system. In this work we show that sliding contact tactile exploration can be achieved using a complex touch sensor with an adequate perception system.

Previous work with the TacTip mostly involved discrete contacts (taps) with a surface for localization \cite{Lepora2015}, tactile exploration \cite{Lepora2017} or performed a rolling motion \cite{Cramphorn2016,Ward-Cherrier2017} or slip detection \cite{James2018}. This study takes a different approach from \cite{Lepora2015,Cramphorn2016,Ward-Cherrier2017,Lepora2017} by using unsupervised and supervised learning to perform sliding motion over an edge stimulus.

\begin{figure}
	\centering
	\begin{overpic}[scale=0.62]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_rawFull}
		\put(-2,74.5){\textbf{(a)}}
		\put(86.5,74.5){\textbf{(b)}}
	\end{overpic}\hspace{-0.2cm}
	\includegraphics[scale=0.62,trim={0 0 0 0.3cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_rawZoom}
	\caption{The effect of sliding motion on the tactile data. (a) The black trace shows the pin motion of the sensor when tapping across a straight edge at different lateral positions without sliding. The coloured traces depict the sensor moving the same amount across the edge as the black trace whilst sliding in different directions (from 0\degree{} to 315\degree{}). (b) Zoomed in version of (a) over the central pin marked by the central rectangle. Notice the difference between statically collected points (black) and those collected whilst sliding (coloured), showing that sliding influences the tactile sense.}
	\vspace{-1em}
	\label{fig:sliding}
\end{figure}
\section{METHODS}

\subsection{Experimental Setup}
\label{sec:setup}

\subsubsection{Hardware}
The setup consists of a 6 axis robot arm (ABB IRB120) with a biomimetic optical tactile sensor (the TacTip~\cite{chorley2009}) as an end effector (Fig.~\ref{fig:setup}a). The TacTip is a multi-material 3D-printed sensor with a 40\,mm rubber-like dome with 127 white internal pins placed in a hexagonal arrangement (Figs.~\ref{fig:setup}b, c). The dome is filled with a gel that makes it soft and compliant. A USB camera tracks each individual pin in each recorded frame using image processing techniques \cite{Ward-Cherrier2018}, thus the sensor outputs the 2D pixel positions of each pin for each frame; refer to \cite{Ward-Cherrier2018} for further details.

The stimuli used in the experiment are four 3D-printed plastic objects, one natural object (a tape measure) and one laser-cut acrylic spiral~\cite{Lepora2017}. The 3D-printed shapes are a rectangle, two differently sized circles and a 5-petal flower-like shape. The objects are rigidly fixed in front of the robot.
\subsubsection{Training set experimental procedure} \label{subsubsec:trainingProcedure}
We collected a training set by tapping the sensor over the rectangular stimulus edge at different lateral positions, depths and orientations (Fig.~\ref{fig:setup}a). During a tap, the robot reaches the required sensor lateral position and orientation and then moves down onto the object to the required depth. The collected dataset comprises 288 poses (18 orientations $\times$ 8 lateral positions $\times$ 2 depths), with 1 frame recorded at each sensor pose.

The sensor rotates in 20\degree{} increments from -160\degree{} to 180\degree{}. At each sensor orientation $\theta_i$ ($i\in\{1,\dots,18\}$), the sensor moves to 8 different lateral positions $l_i$ with $i\in\{1,\dots,8\}$ (Fig.~\ref{fig:training}); namely, 9.9\,mm and 6\,mm to -6\,mm in \mbox{-2\,mm} steps. The middle of the sensor is in contact with the edge of the object at the 0\,mm position, while at the 9.9\,mm position the sensor is not touching anything. At each lateral position, the sensor moves to two different depths:~2\,mm and 4\,mm into the object. Data were collected at different depths to ensure the perception method would still generalise in~case the stimuli are not perfectly aligned. The perception label $w_i$ refers to either the $l_i$ label or $\theta_i$ label.

\begin{table}[t]

	\centering

	\setlength{\tabcolsep}{0.1em}

	\begin{tabular}{ccc}

		\includegraphics[grid,width = 0.25\textwidth]{Figures/training_final}&
		\includegraphics[width =0.05\textwidth,trim={1.2cm 1.1cm 0 0},clip]{Figures/rectangle_tap_down_03262135_colorbar_orientation}&

		\begin{sideways}{\textsf{ \small \hspace{0.25cm} orientation (degrees)}}\end{sideways}
	\end{tabular}
	\captionsetup{type = figure}
	\caption{Training performed on a straight edge. At each orientation the sensor moves forward along the arrow without touching the object until it reaches the required position, then the sensor descends to touch the object (tap). The horizontal lines show the lateral training positions. Position 0\,mm aligns the middle of the sensor with the edge.}
	\vspace{-1.2em}
	\label{fig:training}
\end{table}

\subsection{Algorithms}

\subsubsection{Data Representation} \label{subsection:dataRep}

The data recorded by the TacTip consists of the $x$ and $y$ pixel positions of the 127 internal pins, as observed in each camera frame. The data is a column vector of these positions:
\begin{equation}
\label{multi dim data}
\bm{z} =[s_1, \dots, s_{N_{\rm dims}}]^T
\end{equation}
where $s_k$ is a pixel position value, $k$ is the dimension index and $N_{\rm dims}$ is the total number of sensor dimensions (here 254). The training data is stored as a data matrix, $D =\setlength\arraycolsep{1pt}
\begin{bmatrix}\bm{z_1},&\dots, &\bm{z_N}\end{bmatrix}^T$, where $N$ is the total number of collected frames during the experiment with $N =$ 288 (the total number of sensor poses).

\subsubsection{Principal Component Analysis and Data Transformation}

\label{sec:methodsPCA}

Aquilina et al. \cite{Aquilina2018} showed that when the taxels (sensing elements) of a touch sensor are correlated to each other, linear dimension reduction (PCA) can uncover useful structure in the data. This applies to the TacTip \cite{Aquilina2018}, which motivates the use of PCA in this work and where we extend it by showing that features found in tap data can also be used for sliding contact data.

PCA is performed on the data matrix $D$ and the first 3 principal components (PCs), are used in the rest of this study. These PCs, which are the projections of $D$, cumulatively describe 88.3\% of the signal variance in $D$. Each frame $\bm{z}$ is projected into vector $\bm{p}$ using \mbox{$\bm{p}\!=\!\setlength\arraycolsep{2pt}\begin{pmatrix}
 \bm{e_1} & \bm{e_2} & \bm{e_3}
 \end{pmatrix}^T(\bm{z}-\bar{\bm{z}})$}
where $\bm{p}$  is a 3D column vector, $\bar{\bm{z}}$ is the mean of each column in $D$, and $\bm{e_1}$, $\bm{e_2}$ and $\bm{e_3}$ are the column eigenvectors.

Motivated by the correlation between sensor orientation and the angle of $\bm{p}$ in the 2\textsuperscript{nd} and 3\textsuperscript{rd}~PC plane \cite{Aquilina2018}, we transform each vector $\bm{p}$ into modified spherical coordinates $\bm{v}$. The vector $\bm{v}$ consists of $\rho$, $\theta_{\text{PC23}}$ and $\phi$ where \mbox{$\rho^2\!=\!\text{PC1}^2\!+\!\text{PC2}^2\!+\!\text{PC3}^2$}, \mbox{$\theta_{\text{PC23}}\!=\!\atantwo(\text{PC3},\text{PC2})$} and \mbox{$\phi\!=\! \atantwo((\text{PC2}^2\!+\!\text{PC3}^2)^{\frac{1}{2}},\text{PC1})$}. The operator $\atantwo$ is the four quadrant inverse tangent operator which considers the full 360\degree{} range and PCN is the N\textsuperscript{th} PC. The first no contact point collected is set as the origin, unless there are multiple depths in the training set in which case the origin used to compute $\rho$ and $\phi$ is shifted in the PC2-PC3 plane for each $\theta_{PC23}$ so that the same lateral positions in the middle of the collected range for different depths have the same $\phi$.

\begin{figure}[t!]

	\vspace{0.175cm}
	\centering
	\includegraphics[width = 0.55\linewidth]{Figures/test_offline_final}
	\caption{Multi-directional data collection procedure. The sensor slides along different directions at 45\degree{} increments. Along each direction the sensor pose varies as seen in Fig.~\ref{fig:training}, so the same colour bar applies here. The cross markers indicate the starting position of the sensor's midpoint and the arrow indicates the sliding direction. Here a 90\degree{} motion means that the sensor slides westward along the edge.}
	\label{fig:omnidirection}
	\vspace{-1.1em}
\end{figure}

\subsubsection{Training Data Pruning}

Data pruning is the elimination of noisy or mislabelled data from the training set to improve the performance of the learning algorithm \cite{Hong2008,Angelova2005,Kubica2002}.
Points collected at no contact during the training procedure have different physical quantity labels but are in reality indistinguishable. These points would introduce a discontinuity in a regression-based algorithm, therefore we remove them from the training set.

We use the sensitivity measure $S$ introduced in Aquilina et al. \cite{Aquilina2018} to remove the indistinguishable data points. The sensitivity is defined by
\begin{equation}
\label{eq:sens}
 S = \frac{||\Delta\bm{p}||}{|\Delta w_i|}
\end{equation}
where $||\Delta\bm{p}||$ is the change in the projected vector $\bm{p}$ and $|\Delta w_i|$ is the change in label values (either orientation or lateral position). A large sensitivity  $S$ denotes a large change in sensor measurements $||\Delta\bm{p}||$ for a fixed change in the physical quantity of interest $|\Delta w_i|$, thus poses with a small $S$ are the most difficult to perceive. Using the sensitivity measure $S$ of each training point we eliminate outlying data from the training set by comparing it to a decision criteria. Following Hoaglin et al. \cite{Hoaglin1986}, the Tukey outlier detection rule (boxplot) is used to remove outliers that have a large $S^{-1}$, thus keeping only the easily distinguishable observations. An upper limit is used for the lateral position training set to ensure that at least 90\% of the data is always kept.

\begin{figure}[t!]

		\vspace{0.175cm}
	\centering
	\includegraphics[width = 0.48\textwidth]{Figures/flowchart4}
	\caption{Flowchart of the overall contour-following system. The perception block consists of a PCA module which projects the sensor data into a 3D vector $\bm{p}$, this is transformed into spherical coordinates $\bm{v}$  which are the input of the GPs that output $\hat{\theta}$ and $\hat{l}$. The control policy block shows the dependencies of the control variable $\bm{u}$ on the estimated orientation $\hat{\theta}$ and the P controller used to reach a desired lateral position $l_d$.}
	\label{fig:contourMethod}
	\vspace{-1.2em}
\end{figure}

\begin{table*}[t]
		\vspace{0.15cm}
	\centering

	 \sffamily
		\begin{tabular}{ccccc}

			& {\normalsize \textbf{Sliding Direction 0\degree{}}} & {\normalsize  \textbf{Sliding Direction 90\degree{}}} & {\normalsize  \textbf{Sliding Direction 180\degree{}}} & {\normalsize  \textbf{Sliding Direction 270\degree{}}}\\

			\raisebox{0.5\totalheight}{\rotatebox[origin = lt]{90}{\mbox{\small Second PC} }}

			& 	\includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_1_motion0_PC}
			& 	\includegraphics[width = 	0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_3_motion90_PC}
			& 	\includegraphics[width = 0.19\textwidth ,  trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_5_motion180_PC}
			& 	\includegraphics[width = 0.19\textwidth,  trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_7_motion270_PC}\\
			& {\small First PC}
			& {\small First PC}
			& {\small First PC}
			& {\small First PC}\\

			\raisebox{0.5\totalheight}{\rotatebox[origin = lt]{90}{\mbox{\small Fifth PC} }}

			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_9_motion0_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_11_motion90_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_13_motion180_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03270015_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_15_motion270_PC}\\

			& {\small Fourth PC}
			& {\small Fourth PC}
			& {\small Fourth PC}
			& {\small Fourth PC}\\

	\end{tabular}

	\captionsetup{type = figure}
	\caption{The PCs obtained by projecting the multi-directional test set onto the PCA eigenvectors of the multi-directional test set. The top row shows the projections obtained using the 1\textsuperscript{st} and 2\textsuperscript{nd} eigenvectors of the multi-directional set. The bottom row shows the projections obtained using the 4\textsuperscript{th} and 5\textsuperscript{th} eigenvectors of the multi-directional set. The colour of each point shows the true sensor orientation (same colour bar as Fig.~\ref{fig:training}). }
	\label{tbl:visualtable}
	\vspace{-0.25cm}
\end{table*}

\begin{table*}[t]
	 \sffamily
	\centering

		\begin{tabular}{ccccc}

			& {\normalsize \textbf{Sliding Direction 0\degree{}}} & {\normalsize  \textbf{Sliding Direction 90\degree{}}} & {\normalsize  \textbf{Sliding Direction 180\degree{}}} & {\normalsize  \textbf{Motion Direction 270\degree{}}}\\

			\raisebox{0.5\totalheight}{\rotatebox[origin = lt]{90}{\mbox{\small Third PC} }}
			& \includegraphics[width = 0.19\textwidth ,  trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_1_motion0_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_3_motion90_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_5_motion180_PC}
			& \includegraphics[width = 0.19\textwidth, trim={0 0 0 0.1cm},clip]{Figures/rectangle_slide_continuous_omnidirectional_03270015_03262135_aug0_pcaFlag_4_TargetNo_1_partial_0singleNo_7_motion270_PC}\\
			& {\small Second PC}
			& {\small Second PC}
			& {\small Second PC}
			& {\small Second PC}\\

	\end{tabular}
	\captionsetup{type = figure}
	\caption{The PCs obtained by projecting the multi-directional test set onto  the 2\textsuperscript{nd} and 3\textsuperscript{rd} PCA eigenvectors of the training set (Fig.~\ref{fig:training}) . The colour of each point shows the true sensor orientation (same colour bar as Fig.~\ref{fig:training}). }
	\label{tbl:visualtableTrain}
	\vspace{-0.25cm}

\end{table*}
\subsubsection{Nonlinear Regression}
\label{sec:methodsGP}
We use nonlinear regression to map between the sensor data and the physical quantities of interest (the sensor lateral position and orientation). We use Gaussian Process Regression \cite{Rasmussen2005} with a Mat\'{e}rn 5/2 covariance function and a constant mean function. The GP is implemented using \cite{rasmussen2010} and the hyperparameters are found by optimising the log marginal likelihood \cite{Rasmussen2005} using a constrained optimiser (MATLAB optimisation toolbox).

Three independent GPs are used for regression: two to predict the sensor orientation, encoding the cosine (referred to as $\text{GP}_{\cos}$) and sine (referred to as $\text{GP}_{\sin}$) of the sensor orientation and a third for the sensor lateral position (referred to as $\text{GP}_{\text{lat}}$). The predicted sensor orientation is computed by using $\hat{\theta }= \atantwo(\bar{f}_{\sin\theta},\bar{f}_{\cos\theta})$, where $\bar{f}_{\sin\theta}$ is the mean prediction of $\text{GP}_{\sin}$ and $\bar{f}_{\cos\theta}$ is the mean prediction of $\text{GP}_{\cos}$. $\text{GP}_{\text{lat}}$ outputs the predicted lateral position $\hat{l }$. The spherical coordinates $\bm{v}$ are the inputs of the GPs and the outputs are $\hat{\theta }$ and $\hat{l }$.

We constructed baseline GPs to have a comparison method that does not use PCA. The baseline GPs perform regression using the raw sensor measurements directly using only one hyperparameter for all the pin positions. We use the baseline GPs to quantify the increase in perception accuracy obtained by including the PCA transform.

\subsection{Offline Testing data collection} \label{subsec: omni}

The offline test dataset is referred to as a multi-directional dataset as we collected the data over different sliding directions. We use the multi-directional set to evaluate the generalisation performance of the perception method which is trained using data recorded while performing taps.

The multi-directional data collection consists of recording the tactile data whilst the sensor slides against the edge of a rectangular object (Fig.~\ref{fig:omnidirection}). The dataset comprises 1152 points (18 orientations $\times$ 8 sliding directions $\times$ 8 lateral positions), with 1 frame recorded at each sensor pose.

At each sensor orientation (see Sec.~\ref{subsubsec:trainingProcedure}) the robot slides in different directions in 45\degree{} increments (Fig.~\ref{fig:omnidirection}). The starting position of each sliding movement is depicted by a black cross (Fig.~\ref{fig:omnidirection}). For each sliding direction, the sensor moves to 8 lateral positions in steps (refer to Sec.~\ref{sec:setup}), starting at position -6\,mm (on the surface) for motions from 90\degree{}--270\degree{}, and at position 9.9\,mm (off the surface) otherwise.

The sensor spends approximately the same amount of time in contact with the surface irrespective of the sliding direction. Therefore the sensor is only fully in contact with the surface for a length of 6\,mm along the sliding direction, except for the  90\degree{} and 270\degree{} motions where the sensor is in contact with the edge during all the lateral positions.

\subsection{Task: Contour Following}
\label{sec:methodsContour}

We achieve tactile exploration by combining the perception method with a simple controller (Fig.~\ref{fig:contourMethod}). The control policy \cite{Martinez-Hernandez2017} moves the sensor to the robot position $\bm{u}$ with $\Delta\bm{u}$ composed of: 1) 3\,mm linear exploratory movement $e$ along the perceived edge orientation and 2) a lateral correction movement perpendicular to the perceived edge orientation. We assume that the position of one point on the contour is known. The sensor orientation is fixed during the task and a proportional (P) controller with a hand tuned gain $K$ of 0.35 is used for the correction movement. The control policy (Fig.~\ref{fig:contourMethod}) is thus
\begin{equation}
\Delta\bm{u} = R(\hat{\theta})\begin{pmatrix} e \\ K( l_d-\hat{l})\end{pmatrix}
\end{equation}
where $R(\theta)$ is the rotation matrix that aligns $e$ with the edge and $l_d$ is a constant representing the desired lateral position.

\begin{table*}[b]
	\vspace{-0.5em}
	\centering
	\caption{Summary of results.  Each column shows the RMS error obtained for each sliding direction which in turn is divided into ``On"  and ``Off" category. ``On" means the middle of the sensor is on the object surface and ``Off" means the middle of the sensor is off the surface.}
	\label{tbl:errortable}
	\setlength{\tabcolsep}{0.3em}
	\setlength{\extrarowheight}{0.4em}
	\begin{tabular}{cccccccccccccccccc}
		\hline
		& &  \multicolumn{2}{c}{\textbf{Direction 0\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 45\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 90\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 135\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 180\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 225\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 270\degree{}}}& \multicolumn{2}{c}{\textbf{Direction 315\degree{}}}\\
		\hline
		&\multicolumn{1}{c|}{} &On&\multicolumn{1}{c|}{Off}&On&\multicolumn{1}{c|}{Off}&\multicolumn{2}{c|}{On}&On&\multicolumn{1}{c|}{Off}&On&\multicolumn{1}{c|}{Off}&On&\multicolumn{1}{c|}{Off}&\multicolumn{2}{c|}{On}&On&Off\\
		GP baseline   &\multicolumn{1}{c|}{} &26.01\degree{}&\multicolumn{1}{c|}{16.59\degree{}}&2.94\degree{}&\multicolumn{1}{c|}{8.02\degree{}}&\multicolumn{2}{c|}{50.68\degree{}}&92.37\degree{}&\multicolumn{1}{c|}{99.59\degree{}}&127.18\degree{}&\multicolumn{1}{c|}{145.27\degree{}}&141.56\degree{}&\multicolumn{1}{c|}{153.39\degree{}}&\multicolumn{2}{c|}{90.32\degree{}}&50.10\degree{}&31.97\degree{}\\
		PCA + GP   &\multicolumn{1}{c|}{} &49.41\degree{}&\multicolumn{1}{c|}{10.17\degree{}}&24.17\degree{}&\multicolumn{1}{c|}{8.24\degree{}}&\multicolumn{2}{c|}{3.27\degree{}}&22.74\degree{}&\multicolumn{1}{c|}{10.82\degree{}}&46.74\degree{}&\multicolumn{1}{c|}{14.61\degree{}}&52.67\degree{}&\multicolumn{1}{c|}{11.98\degree{}}&\multicolumn{2}{c|}{12.66\degree{}}&51.87\degree{}&12.77\degree{}\\

		\hline
	\end{tabular}

\end{table*}
\section{RESULTS}

\subsection{Multi-directional Continuous Tactile Contact }\label{sec:resultOmni}

Here we examine the response of the sensor when sliding along the edge of a rigid object. We visualise the data using PCA to understand the sensor's response. Subsequently, the projections obtained using the tap training set are presented and lastly the accuracy of the perception method is evaluated.

\subsubsection{Multi-directional test data visualisation} \label{subsec:visualise}

PCA projections provide a low-dimensional vector representing the vector of tactile measurements $\bm{z}$. We plot this projection in 3D (see \cite{Aquilina2018}) to gain a better understanding of the underlying behaviour of the sensor with respect to the environment. We visualise the multi-directional set in Fig.~\ref{tbl:visualtable} to examine the effects of sliding motion; the colour shows the true sensor~orientation.

The 1\textsuperscript{st} and 2\textsuperscript{nd} PCs of the multi-directional set do not show a correlation between the sensor orientation and the PC1-PC2 angle ($\theta_{\text{PC12}}$), but instead, show a correlation to the sliding direction. This is shown in the first row of  Fig.~\ref{tbl:visualtable} by the location of the individual colours varying from panel to panel (left to right). Additionally, the magnitude of the vector comprising PC1 and PC2 is correlated to the magnitude of the shear, with a small magnitude for measurements which were recorded before the sensor started sliding. When the sensor slides along an object, shear force effects dominate the movement of the pins, which explains why sliding is represented in the first two PCs.

Higher PCs obtained from the multi-directional set represent sensor orientation features that are independent of the sliding direction. There is a large correlation between the angle of the projections in the PC4-PC5 plane and the true sensor orientation which is mostly invariant to the shear direction (bottom row of  Fig.~\ref{tbl:visualtable}). The 3\textsuperscript{rd} PC is not shown as it does not contain any information related to sensor~orientation.
\subsubsection{Multi-directional projections using the training set}
 \label{subsec:visualiseTrain}

The PCA-based perception algorithm uses a small training set (288 data frames) collected during discrete contacts with the object to infer orientation features in the continuous contact tactile dataset (1152 data frames). In  Fig.~\ref{tbl:visualtable} we visualised the multi-directional set using eigenvectors of sliding data. Here, we discuss a shear-invariant transformation, obtained by projecting the multi-directional dataset (Fig.~\ref{tbl:visualtableTrain}) onto the eigenvectors of the taps training set.

PC1 is correlated with the sensor pins expansion/compression which relates to the lateral position and not with the orientation features we are discussing here.

PC2 and PC3 are correlated with the true sensor orientation (Fig.~\ref{tbl:visualtableTrain}). Thus, irrespective of the sliding direction, the tactile orientation features are still visible in the PC2-PC3 angle ($\theta_{\text{PC23}}$).

The same tactile features can be captured by using PCA eigenvectors of a discrete contact set without the need of a larger multi-directional set as shown by the similarity between the projections obtained using either set (compare the bottom row of  Fig.~\ref{tbl:visualtable} to  Fig.~\ref{tbl:visualtableTrain}). The eigenvectors of the multi-directional set (1152 data frames) capture orientation features better than the eigenvectors of the training set. This is expected since the dominant eigenvectors of the multi-directional set capture sliding; thus, the higher PCs are orthogonal to that sliding component. Nonetheless, the projections obtained using the eigenvectors of the training set are sufficiently good to represent a shear-invariant space with the advantage of a using much smaller dataset collected without sliding movements.

\subsubsection{Multi-directional offline test} \label{subsection:offline_errors}

Here we quantitatively analyse the effects of sliding motion on the sensor response to complement the qualitative analysis described above. The full perception method is considered in this section including the nonlinear regression that maps between the visualisations seen in  Fig.~\ref{tbl:visualtableTrain} to the sensor pose. The increase in performance obtained by using PCA prior to regression is evaluated by using the baseline GP model.

The root mean square (RMS) errors of the perceived sensor orientation ($\hat{\theta}$) are partitioned depending on the sliding direction and are further divided into two groups for each motion: ``On" denoting the middle of the sensor is on the object;  ``Off" denoting that the middle of the sensor is off the object. For the 90\degree{} and 270\degree{} motions, only the ``On" group is available since the middle of the sensor is always on the object surface.

Overall, the errors obtained for the ``Off" category are smaller than the ``On" category for both models (\autoref{tbl:errortable}). During the ``Off" motion the magnitude of the shear disturbance is smaller compared with the ``On" category explaining this result. This also agrees with the visualisations seen in  Fig.~\ref{tbl:visualtableTrain} and the bottom row of  Fig.~\ref{tbl:visualtable} where the correlation of the orientation feature is weaker in the centre of the plots where the magnitude of the shear disturbance is larger.

The errors obtained for the PCA-based perception algorithm are in most cases smaller than the baseline method. Importantly, the proposed model obtains a far better (7-15x) accuracy for the 90\degree{} and 270\degree{} motion, which is crucial as this is the main sliding direction used during contour-following. Therefore the projection performed by the PCA provides a good shear-invariant transform for this sliding direction.

Otherwise, for sliding motions starting from the outside of the object edge (direction 0\degree{}, 45\degree{} and 315\degree{}) the baseline model produces better results for the ``On" groups. In these directions, the main perturbation experienced by the sensor pins is a change in magnitude rather than direction thus enabling the baseline model to obtain the least errors in these directions. The PCA performs a transformation of the data which attenuates the influence of shear in some directions more than in others but which overall gives better accuracy in the majority of the sliding directions. This thus validates the use of PCA eigenvectors to project the pins into a low dimensional space less influenced by shear.

\begin{figure*}[t!]
			\vspace{0.15cm}
	\centering
	\begin{subfigure}{0.281\textwidth}
		\centering

		\begin{overpic}[width=\textwidth,trim = 0 0.07cm 0 0,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_03262320_Rectangle}
			\put(10,80){\textbf{(a)}}
		\end{overpic}

	\end{subfigure}
	\begin{subfigure}{0.22\textwidth}
		\centering

		\begin{overpic}[ width=\textwidth, trim = 0 0 0 0.01cm,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_03262344_LargeCircle}
			\put(-10,95){\textbf{(b)}}
		\end{overpic}

	\end{subfigure}
	\begin{subfigure}{0.22\textwidth}
		\centering

		\begin{overpic}[width=\textwidth,trim = 0 0 0 0.01cm,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_03262358_SmallCircle}
			\put(-10,95){\textbf{(c)}}
		\end{overpic}

	\end{subfigure}
	\begin{subfigure}{0.22\textwidth}
		\centering

		\begin{overpic}[width=\textwidth,trim = 0 0 0 0.01cm,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_03262338_Flower}
			\put(-10,95){\textbf{(d)}}
		\end{overpic}
	\end{subfigure}

	\caption{(a) The trajectory (blue trace) performed by the robot when following the contour of a: (a) rectangle, (b) large circle, (c) small circle and (d) flower-like shape. The grey lines show the normal to the object edge as perceived by the sensor.}
	\label{fig:contours}
	\vspace{-1.2em}
\end{figure*}

\begin{figure}[h]
	\centering

\begin{subfigure}{0.255\textwidth}
	\centering
		\begin{overpic}[width=\textwidth,trim = 0 0 0 0.01cm,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_09041935_NaturalObject}
		\put(70,25){\includegraphics[width=0.3\textwidth]{Figures/tape_measure_black_burned}}
		\put(10,83){\textbf{(a)}}
	\end{overpic}

\end{subfigure}
	\begin{subfigure}{0.2\textwidth}
	\centering

	\begin{overpic}[width=\textwidth,trim = 0.9cm 0 0 0.01cm,clip]{Figures/contour_test_circle_slide_record_track_thetauncontrolled_pca_gp_04161848_Spiral}
		\put(5,93){\textbf{(b)}}
	\end{overpic}

\end{subfigure}
	\caption{Natural object (tape measure) and spiral contour-following. The same colour coding of Fig.~\ref{fig:contours} is used here.}
	\label{fig:spiralContour}
\vspace{-1.6em}
\end{figure}
\subsection{Online Contour Following}

Finally, we present the online results for a tactile exploration task, specifically contour-following where the sensor continuously slides along the edge of various shapes.

We use four 3D-printed shapes, an acrylic laser-cut spiral shape, and a tape measure (Fig.~\ref{fig:setup}a) for this task. The shapes were chosen to test the limits of the method. The rectangle has zero curvature and corners. The two circles have different constant curvatures. The flower-like shape has both negative and positive curvatures. The tape measure is a natural object and has different curvature values and a corner. Finally, the spiral shape has negative and positive curvatures, contains corners and has closely spaced features.

Our approach successfully traced all contours (Fig.~\ref{fig:contours} and Fig.~\ref{fig:spiralContour}) by using the perception algorithm which consist of a PCA dimension reduction step (Sec.~\ref{sec:methodsPCA}) and nonlinear regression (Sec.~\ref{sec:methodsGP}) along with a simple control policy (Sec.~\ref{sec:methodsContour}). The RMS orientation errors are similar for all shapes with an average value of 12.2\degree{} which agrees with the errors obtained in Sec.~\ref{subsection:offline_errors} for the 270\degree{} sliding direction. The perception algorithm handles corners (Fig.~\ref{fig:contours}a and Fig.~\ref{fig:spiralContour}) by gradually perceiving a varying angle which changes smoothly over the corner. This was not a trained behaviour but emerged from the algorithm. The algorithm thus generalises to different curvatures.

The algorithm generalises from discrete contact tactile data collected on a zero curvature edge to data recorded whilst performing sliding motion over edges having varying curvature properties. The perception is not influenced by changes in curvature magnitude as shown by the two circles (Figs.~\ref{fig:contours}b, c) or by whether the curvature is positive or negative as demonstrated by the flower-like shape (Fig.~\ref{fig:contours}d). The method also performed contour-following of a natural object (Fig.~\ref{fig:spiralContour}a). The robustness of the perception is demonstrated with the spiral task (Fig.~\ref{fig:spiralContour}b) where apart from curvature variations, a different material is used from the training set and an upper limit on the lateral position perception error is imposed. The perceived lateral position errors do not impact the success of the contour-following as shown by the smooth shape-preserving trajectories achieved.

\FloatBarrier
\section{DISCUSSION}
The paper has demonstrated that a PCA-based perception algorithm can infer edge features invariant of the sliding motion. We achieved successful continuous contour-following of a wide range of shapes, thus showing that the simple features found by PCA with a suitable discrete contact training set are robust to sliding and to changes in curvature.

The PCA part of the algorithm also helps to visualise the sliding data which builds upon the tactile visualisation presented by Aquilina et al. \cite{Aquilina2018}. Here we show that visualisation of the multi-directional data reveals continuous tactile data is history dependent on the sliding direction. However, PCA can also encode the sliding vector in each sensor frame, which may be useful in experiments where sliding is a quantity of interest.

Additionally, due to the simple features extracted using PCA, the algorithm was able to generalise to various flat objects of an unknown shape despite being trained only on a portion of a straight edge. This is related to the work by Luo et al. \cite{Luo2014} where a specific data descriptor yields rotation and translation invariance. However, the most common approach is to obtain invariance by training on samples which have different properties such as the shape-invariant method presented by Yuan et al. \cite{Yuan2017}. This would have been equivalent to using a sliding contact training set in this work. However, in practice it may not be possible to train for every event the system could encounter; instead we view it desirable to have a robust system that produces reasonable results on a wide range of scenarios.

A limitation of this work is that it only considers planar 2D objects which constrains the tactile exploration problem considerably. However, this still demonstrates the proof of concept. It would be instructive to test the method with objects that do not have clear cut edges. Another limitation of the current method is that the lateral displacement predictions are not as accurate (overall error of 2.15\,mm for the offline task) as the orientation predictions. These limitations will be addressed in future work.

We expect the proposed method to apply to more challenging tasks due to its generalisation capabilities. Such a method could be combined with more complicated controllers using predictive or adaptive control to complete demanding manipulation or exploration tasks in unstructured environments.

\bibliographystyle{IEEEtran}
\input{ICRA2019_Aquilina_arXiv.bbl}
\end{document}

redpajama_set_name: RedPajamaArXiv


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