Dear EarthTalk: How are scientists using DNA to conserve wildlife?
Traditionally, conservation biologists have relied on field observation and sample and statistical analysis to help them understand the dynamics behind species loss, but today genetics is taking on an increasingly important role in helping quantify the biodiversity around us and even save some threatened species.
DNA analysis is helping conservation biologists reintroduce genetic variation in populations of wild African cheetahs and other endangered wildlife. Credit: Ed Yourdon, Flickr CC.
Conservation genetics is particularly useful for clarifying whether a particular wildlife population needs special protection as a genetically distinct sub-species. According to Susan Haig of the U.S. Geological Survey, conservationists are using DNA analysis to determine kinship lineage in selecting which individuals to reintroduce to a population for recovery. "DNA sequencing procedures … allow for identification of parentage, more distant relatives, founders to new populations, unidentified individuals, population structure, effective population size, population-specific markers, etc." reports Haig, adding that the result is more sophisticated information crucial to setting species recovery priorities.
The non-profit Revive and Restore is best known for its advocacy of so-called "de-extinction"—that is, bringing back extinct wildlife species and reintroducing them to the landscapes they used to call home—but also aims to provide "genetic assistance" to existing threatened wildlife species.
"Endangered species that have lost their crucial genetic diversity may be restored to reproductive health," reports the group. "Those threatened by invasive diseases may be able to acquire genetic disease-resistance." The group is hoping to apply what it learns from a pilot project restoring genetic diversity to an endangered population of black-footed ferrets to other species' recovery efforts.


Gerard Unger developed Swift™ between 1984 and 1987, with the intention of making a modern digital type for newspapers. The project was undertaken for the German firm Dr.-Ing Rudolf Hell GmbH. At that time, newspapers were produced on high-speed presses with low quality paper. Unger says Swift is designed to be a survivor." It has chunky triangular serifs, sturdy connections at junctures, condensed forms with open counters, and a tall x-height. The work of William A. Dwiggins (1880-1956) was Unger's major design influence for Swift. Dwiggins was a American type designer for Mergenthaler Linotype who, as early as 1930, was focusing on legibility in the design of alternative fonts for newspaper printing. Swift has become a contemporary classic, and is now used more often for corporate identities and magazines than for newspapers. Austere and concise, firm and original, Swift is a typeface suited to almost any purpose. Swift™ Pro is the OpenType version, and includes a full range of styles and weights from light to extra bold.
In 1990, Linotype AG merged with Dr.-Ing Rudolf Hell GmbH, forming the Linotype-Hell AG (today Linotype GmbH). Since then, Linotype has been the official source of all fonts that were originally designed for the Hell Corporation. Linotype has also improved the typefaces using new technologies, including OpenType."
Swift is a trademark of Monotype GmbH registered in the U.S. Patent and Trademark Office and may be registered in certain other jurisdictions.


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Tulane Has the Historic Edge on Cincinnati
By Clayton Trutor Oct 25, 2021, 9:05pm EDT
Share All sharing options for: Tulane Has the Historic Edge on Cincinnati
Photo by Jeff Gross/Getty Images
Your Cincinnati Bearcats have not fared well historically against our friends from New Orleans.
The Tulane Green Wave hold an 11-6 advantage in the all-time series against the Cincinnati Bearcats.
The Bearcats dropped their first 4 meetings against Tulane, a cluster of games that took place over the first three-quarters of the 20th century.
Tony Mason's Bearcats of '76 finally got a win against the Green Wave, a 21-14 decision down in New Orleans, but that was hardly the beginning of smooth sailing.
Until the clubs began playing in the AAC in the past decade, Tulane held an 11-3 advantage in the series. The Bearcats have taken the last three meetings (2014, 2017, 2018).
This year's seniors are the only players on Cincinnati's roster that have suited up against Tulane.
In 2018, Cincinnati topped Tulane 37-21, helping the Bearcats improved to 6-0 on the season. Desmond Ridder was already firmly ensconced as QB1 and tossed a pair of TD passes in the win. Michael Warren II busted out for an 81-yard TD run as well.
Check out the highlights below:
Next Up In College Football
2007: The inside story of the greatest CFB season ever
Meet the bag man
The all-time 'consensus' national championships list
The evolutionary history of option football

redpajama_set_name: RedPajamaCommonCrawl


Fitbit Aria Wifi Scale has a rich and pleasant looking smart and shrewd scale which works faultlessly to quantify the body weight, body fat rate, and body mass list.
Fitbit Aria Wifi Scale can monitor up to 8 individuals and let them know their fat status independently. You can setup the remote sync work effectively to get comes about and details on your cell phone and fitbit.com to gain different identifications which you could impart to your family to make them realize that you are fit and sound.
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Set up a formal on-boarding process and assign new hires a mentor. A little effort in the beginning goes a long way in retaining employees in the long run. Likewise, you should provide ongoing training to your existing employees. If your employees feel that they're working a dead-end job with little room for advancement, chances are they'll leave you sooner rather than later. Conversely, if your employees feel that you're invested in their development, they'll be more likely to stay with you.
Could you use some working capital to hire additional staff or implement a new training program? You can receive a free, no-obligation quote in as little as 48 hours!


Hyundai Motor Co., South Korea's largest carmaker by sales, said Sunday legendary rally driver Sebastien Loeb will contest six rounds of the 2019 World Rally Championship.
Nine-time WRC champion Loeb will start the two-year contract with Hyundai by attending the Monte-Carlo Rally with the Hyundai i20 Coupe WRC in Monaco next month, Hyundai Motor said in a statement.
Hyundai Motor's World Rally team is now composed of rally drivers Thierry Neuville, Andreas Mikkelsen, Dani Sordo and Sebastien Loeb.
"Joining Hyundai will give us a fresh challenge and one that I can't wait to tackle. I have been impressed with the team's approach and their determination to succeed. They were firmly in the title fight this season and I feel that I can bring a lot to them, together with Thierry, Andreas and Dani," Loeb said in the statement.
In 2019, Hyundai aims to win the WRC title. The carmaker has the package to achieve that, and its crew composition for next year will perfectly reflect that ambition, it said.


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OPINION: If Henri Lansbury leaves Nottingham Forest it MUST NOT be to Derby County
Steve Corry
2017 has certainly started with a bang for Nottingham Forest, following the news that Henri Lansbury could be off to Derby County.
Lansbury is a colourful character who divides opinion among Forest fans, a kind of Marmite player if you will.
It's no secret that I have a love/hate perspective of the lad, he goes from the sublime to the ridiculous and there is never a dull moment when he's around.
The only surprising element of this latest transfer rumour is that it doesn't involve Burnley; but there is a touch of "Groundhog Day" about it all.
Every time the midfielder's contract is almost up, he seems to apply himself a bit more than usual, application is the key word here for me. The marathon of televised games has highlighted Lansbury's talents and he's certainly put himself in the window once more.
Before anyone thinks that I'm going to give him a major dressing down, let it be said that when he does apply himself, he's certainly got the quality to back it up.
For the record, he has more ability in his right toe than I ever did with both feet, he can certainly play. Our stats without him in the side are compelling too, only one win in eight is undeniable proof of worth.
There lies my frustration with him, I want him to play the shop window, come and get me game, all season long.
Some fans just despise him whilst others think he can do no wrong; if I haven't made myself clear, I'm somewhere in the middle.
The fact that the interested party are Derby makes the whole saga more controversial, it's a real tinder box situation.
It's nothing new though is it? There was talk of the Rams tabling an offer for Britt Assombalonga a while back and how could we forget the greed of Chris Commons as he sold his soul for thirty pieces of silver. Nathan Tyson is another red that went on to play for the Rams, this after antagonising their supporters with the red flag flying high, following a fiery victory in 2009.
Derby County's initial bid of £2.5m has just been rejected by Nottingham Forest but this could open the door for Watford, and a chance of Premier League football for Henri Lansbury.
There's also the possibility of an improved offer from the Rams which could trigger a price war of sorts, something that may appeal to Fawaz.
Nottingham Forest impress on Sabri Lamouchi's City Ground debut
This creates even more intrigue because NFFC are supposedly going through a takeover; which could be one of the longest in football history if continues to drag on.
At first I thought that Mr Al-Hasawi was deliberately prolonging the takeover to encompass the January transfer window; a parting shot and another way of recouping some of the squandered family silver.
Which was exactly the motive behind the disgraceful sale of Oliver Burke, the thought of which still makes me sick to the stomach.
However; if what we're led to believe is true, Fawaz is going to retain twenty percent of the club so it may not be a cut and run job.
There are so many facets to this bizarre set of circumstances, some people will vent their frustrations at Fawaz, whilst others will undoubtedly vilify the player in question. For the most part in football, you are basically a pawn on the chairman's, guild edged chess board.
In layman's terms, they can sell you to whoever they want, for how much they like and without your approval. That would ultimately divert any cruel intentions away from the player in question, but when it involves Derby and Forest there is contrary to that theory.
In 2011 Derby County showed interest in signing Forest's French midfielder Guy Moussi; a scenario that is almost identical to the current one involving Lansbury.
At the time Moussi was also 26 years of age, had offers from the Premier League and was nearing the end of his contract.
The Moose endeared himself to the Red's supporters instantaneously by publicly voicing his opinion on the possibility of joining our arch rivals.
Moussi told BBC Radio Nottingham "I don't want to be rude but I could not have gone to Derby.
It was impossible!" Regardless of whether you're a Lansbury lover or loather, wouldn't it be fantastic to hear such words from the Londoner at this moment in time?
There's another cynical way of looking at the attempted Ram raid for Lansbury; could it just be mind games by the club at the wrong end of Brian Clough way? After all, they have very talented players of their own, especially in the midfield.
I hate to admit it but Will Hughes ran the show against us in the recent defeat at the I PRO and would be a hard man to displace in Steve McLaren's side. However; Hughes himself is reportedly being touted by Premier League clubs and could be the very man to replace, should he be sold in January.
There's a whirlwind of scenarios to comprehend regarding this whole story, and it's the last thing us Trickies need to hear just hours into the new year. Lansbury is a very similar character to Kris Commons and Lewis McGugan in my eyes and if I could sum him up in one word it would be "Enigma".
The other two players I've just mentioned never really fulfilled their potential after leaving Forest, although you could argue the Champions League case for Commons at Celtic. Only Henri Lansbury knows if it's time to hit the road; but it must not be the A52!
Update: Major road in Sutton re-opens after 'crash' that caused 'serious injuries'
Man 'face down in urine with hand in pants' sparks Mansfield trader's fury at state of town
Driver 'suffers facial injuries' in Sutton crash
Missing Mansfield teenager found
Blidworth parents' anger as council fail to pick up 'asbestos'
More from Mansfield and Ashfield Chad


Love is not to be cultivated. Love cannot be divided into divine and physical; it is only love -not that you love many or the one. That again is an absurd question to ask:"Do you love all?" You know, a flower that has perfume is not concerned who comes to smell it, or who turns his back upon it. So is love. Love is not a memory. Love is not a thing of the mind or the intellect. But it comes into being naturally as compassion, when this whole problem of existence as fear, greed, envy, despair and hope has been understood and resolved. An ambitious man cannot love. A man who is attached to his family has no love. Nor has jealousy anything to do with love. When you say, "I love my wife," you really do not mean it, because the next moment you are jealous of her. Love implies great freedom -not to do what you like. But love comes only when the mind is very quiet, disinterested, not self-centered. These are not ideals. If you have no love, do what you will go after all the gods on earth, do all the social activities, try to reform the poor, the politics, write books, write poems -you are a dead human being. And without love your problems will increase, multiply endlessly. And with love, do what you will, there is no risk; there is no conflict. Then love is the essence of virtue. And a mind that is not in a state of love is not a religious mind at all. And it is only the religious mind that is freed from problems, and that knows the beauty of love and truth.


Shipping & Returns | Tag Aloha Co.
We currently ship all over the world! All parcels are sent with USPS for domestic and FedEx for international shipments, Monday to Friday.
We happily accept returns/exchanges on full-priced items in their original condition if returned within 14 working days. If you have any concerns regarding our returns policy, please get in touch via the contact page.
For all returns or exchanges please submit a request using the link above.
We will then ask you to return your items to us.
Exchanges will be processed within 48 hours (subject to stock availability). Refunds, once approved will be processed within 15 working days.
The cost to return an item for refund or exchange is at the expense of the customer. For security and peace of mind, we recommend that you use an insured registered post to ensure safe and documented delivery as we are not liable for any lost returned items.


The Turning The Tide Fundraising Walk is an annual fundraising walk along a 17-kilometre stretch of Muriwai Beach.
2019 Fundraising walk will be held on Sunday 28 April, 8am - 1pm. If you believe your community should and can be free of family violence, then join us and help us #TurnTheTide on family violence in northwest Auckland.
Funds raised will help the local women's refuge to run support groups and programmes designed to help both women and men find a way to live without violence.
The morning begins at Muriwai Surf Club with a powhiri to welcome all our participants. Buses will then transport walkers to either the Rimmer Road access to begin the 17-km walk, or to the creek for the start of the four-kilometre Second Wave walk.


A record is made each time you visit a hospital, physician, or other health care provider. Your symptoms, examination and test results, diagnoses, treatment, and a plan for future care are recorded. This information is most often referred to as your "health or medical record," and serves as a basis for planning your care and treatment. It also serves as a means of communication among any and all other health professional who may contribute to your care. Understanding what information is retained in your record and how that information may be used will help you to ensure its accuracy, and enable you to relate to who, what, when, where, and why others may be allowed access to your health information. This effort is being made to assist you in making informed decisions before authorizing the disclosure of your medical information to others. Use or disclosure of your health information will follow the more stringent of State or Federal laws.
Your health record is the physical property of the health care practitioner or facility that compiled it but the content is about you, and therefore belongs to you. You have the right to request restrictions on certain uses and disclosures of your information, and to request amendments be made to your health record. Your rights include being able to review or obtain a paper copy of your health information, and to be given an account of all disclosures. You may also request communications of your health information be made by alternative means or to alternative locations. Other than activity that has already occurred, you may revoke any further authorizations to use or disclose your health information.
Our responsibilities:This office is required to maintain the privacy of your health information and to provide you with notice of our legal commitment and privacy practices with respect to the information we collect and maintain about you. This office is required to abide by the terms of this notice and to notify you if we are unable to grant your requested restrictions or reasonable desires to communicate your health information by alternative means or to alternative locations.
This office reserves the right to change its practices and effect new provisions that enhance the privacy standards of all patient medical information. In the event that changes are made, this office will notify you at the current address provided on your medical file. If applicable, this office will post changes on our web site that provides information about our customer service and/or benefits.
Other than for reasons described in this notice, this office agrees not to use or disclose your health information without your authorization.
For further explanation of this notice you may contact Jessica Ra Kemp at 503-206-EYES(3937).
If you believe your privacy rights have been violated, you have the right to file a complaint with this office by contacting the individual above, or by contacting the Secretary of Health and Human Services, with no fear of retaliation by this office.
Your health information will be used for treatment, payment, and health care operations.
Treatment – Information obtained by your health practitioner in this office will be recorded in your medical record and used to determine the course of treatment that should work best for you. This consists of your physician recording his/her own expectations and those of others involved in providing you care. The sharing of your health information may progress to others involved in your care, such as specialty physicians or lab technicians.
Payment – Your health care information will be used in order to receive payment for services rendered by this office. A bill may be sent to either you or a third-party payer with accompanying documentation that identifies you, your diagnosis, procedures performed and supplies used.
Health Care Operations – The medical staff in this office will use your health information to assess the care you received and the outcome of your case compared to others like it. Your information may be reviewed for risk management or quality improvement purposes in our efforts to continually improve the quality and effectiveness of the care and services we provide.
• Business Associates – Some or all of your health information may be subject to disclosure through contracts for services to assist this office in providing health care. For example, it may be necessary to obtain specialized assistance to process certain laboratory tests or radiology images. To protect your health information, we require these Business Associates to follow the same standards held by this office through terms detailed in a written agreement.
• Notification – Your health record may be used to notify or assist family members, personal representatives, or other persons responsible for your care to enhance your well being or your whereabouts.
• Communications with Family– Using best judgment, a family member, or close personal friend, identified by you, may be given information relevant to your care and/or recovery.
• Appointment Reminders/ Mailings - This office reserves the right to contact you with appointment reminders or information about treatment alternatives and other health-related benefits that may be appropriate to you. Contact may be verbal or written, including mailings in the form of newsletters, postcards or letters.
• Food and Drug Administration (FDA) – This office is required by law to disclose health information to the FDA related to any adverse effects of food, supplements, products, and product defects for surveillance to enable product recalls, repairs, or replacements.
• Worker's Compensation – This office will release information to the extent authorized by law in matters of worker's compensation.
• Public Health – This office is required by law to disclose health information to public health and/or legal authorities charged with tracking reports of birth and morbidity. This office is further required by law to report communicable disease, injury, or disability.
• Correctional Facilities – This office will release medical information on incarcerated individuals to correctional agents or institutions for the necessary welfare of the individual or for the health and safety of other individuals. The rights outlined in this Notice of Privacy Practices will not be extended to incarcerated individuals.
• Law Enforcement – (1) Your health information will be disclosed for law enforcement purposes as required under state law or in response to a valid subpoena. (2) Provisions of federal law permit the disclosure of your health information to appropriate health oversight agencies, public health authorities, or attorneys in the event that a staff member or business associate of this office believes in good faith that there has been unlawful conduct or violations of professional or clinical standards that may endanger one or more patients, workers, or the general public.
NOTICE OF PRIVACY PRACTICES AVAILABILITY: The terms described in this notice will be posted where registration occurs. All individuals receiving care will be given a hard copy.
By checking the box below, you consent to our use and disclosure of protected health infonnation about you for treatment, payment and health care operations. You have the right to revoke this consent, in writing, except where we have already made disclosures in trust on your prior consent.
I request that payment of authorized Medicare/Insurance carrier benefits be made on my behalf to Optik PDX for any services furnished to me by that physician or supplier. I authorize any holder of medical information about me to release to the Centers for Medicare/Medicaid Services and its agent and/or any other Insurance Carriers for which I have coverage, any information needed to determine these benefits or the benefits payable for related services. I agree to provide all referral and treatment planes) as required by my insurance carrieres). All co-pays must be paid at the time of service in accordance with the contracted Insurance Carrier Agreements.


Bollywood actress Asin has found love. The South beauty is all set to tie the knot with Micromax founder Rahul Sharma. Delhi based entrepreneur 36 year old Rahul Sharma, is the founder of Micromax, India's biggest mobile company. The cupid behind the match made in heaven is none other than our Khiladi Akshay Kumar.
Akshay Kumar who is a good friend of Rahul Sharma and has also worked with the 'Ghajini' actress Asin, introduced the two to each other. Asin will be completing all her professional commitments before plunging into holy matrimony with Rahul. Asin is awaiting the reelase of her upcoming film 'All Is Well', with Abhishek Bachchan.


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All of our Orchard Hills private investigators have years of experience conducting government-approved marital extramarital relations investigations. You can rest assured knowing our investigations are always legal.
We keep our team's skill sets polished by requiring them to go to regular training seminars, therefore making sure that they use just the very best methods for each investigation.
Our detectives in Orchard Hills 2748 have a keen eye for spotting the signs of an unfaithful partner, but we do not act based on speculation. Rather, our private investigators employ tactical methods to capture strong evidence of any cheating.
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You don't need to remain in the relationship if you believe your partner's not fully committed to you. If you can't stand the thought of not knowing the fact, let us assist you put your fears to rest. Give us a call, and we'll walk you through the whole procedure from start to finish.


Bonusmail.eu - Die Plattform für alle Cleveren!
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You are here: Home >> Society and Culture >> NGOs >> BRAC
BRAC, a national, private organization, started as an almost entirely donor funded, small-scale relief and rehabilitation project initiated by Fazle Hasan Abed to help the country overcome the devastation and trauma of the Liberation War and focused on resettling refugees returning from India. Today, BRAC has emerged as an independent, virtually self-financed paradigm in sustainable human development. It is one of the largest Southern development organizations employing 97,192 people, with 61% women, and working with the twin objectives of poverty alleviation and empowerment of the poor.
Website http://www.brac.net
Address BRAC Centre, 75 Mohakhali, Dhaka-1212, Bangladesh
Phone + 880-2-9881265, 8824180-7
Category Society and Culture >> NGOs
More NGOs Related Listings
Agrajattra – non-profit NGO of Bangladesh - The Agrajattra is a non-profit, national NGO of Bangladesh. By utilizing its unique development model, Agrajattra aims to aid in poverty alleviation by focusing on the rural i
The Hunger Project Bangladesh - The Hunger Project-Bangladesh is the nation's largest volunteer-drive development organization. It was registered with the NGO Bureau in 1991. It has trained more than 250,0
Noakhali Rural Development Society – NRDS - Noakhali Rural Development Society – NRDS is a non-profit organization, working in the Chittagong region the southeastern coastal part of Bangladesh, where widespread povert
UCEP Bangladesh - UCEP Bangladesh is a non-profit and non-governmental organization aims to uplift the socio-economic conditions of underprivileged communities through help to learn skills to e
Bangladesh Ngo Foundation - The Government of the Peoples' Republic of Bangladesh has established Bangladesh NGO Foundation to support the NGOs, with a view to associate the Non-Governmental Organizati
Palli Karma-Sahayak Foundation (PKSF) - Palli Karma-Sahayak Foundation (PKSF), an apex development organisation, was established by the Government of Bangladesh (GoB) in May 1990, for sustainable poverty reduction t
COAST: Coastal Association for Social Transformation Trust - COAST organizes strategically important activities related to development, which in turn, will facilitate the sustainable and equitable improvement of life, especially of wome
Habitat for Humanity Bangladesh - Habitat for Humanity International is an ecumenical Christian housing ministry. Founded in 1976 by Millard Fuller, HFHI seeks to eliminate poverty housing, and to make decent
Save the Children Bangladesh - Save the Children has been working to assist the children in Bangladesh since 1970. The programme works across seven thematic sectors: Child Protection, Health, Nutrition and
ICA Bangladesh - ICA Bangladesh has been registered as Dhaka Institute of Cultural Affairs with the Government of Bangladesh since 2004. Its registration status is stated below: ICA Bangladesh
Bangladesh NGOs Network for Radio and Communication (BNNRC) - Bangladesh NGOs Network for Radio and Communication (BNNRC) and with other Civil Society Organizations (CSOs) has been continuing advocacy with the Government for a long time
BURO Bangladesh - BURO Bangladesh is a national not-for-profit organization that was set up in 1990 with a view to working for the poor on sustainable basis to reduce poverty. It is a specializ
RDRS Bangladesh – Rangpur Dinajpur Rural Service - RDRS Bangladesh – Rangpur Dinajpur Rural Service is a respected, long-established development NGO working to empower the rural poor in northern Bangladesh for over 34 ye
Connect-Bangladesh – Global Connections and Exchange Project - Connect-Bangladesh.org – The Global Connections and Exchange Project for Bangladesh project has established Internet Learning Centers (ILCs) in secondary schools through
ASA - ASA is the largest and fastest growing fully financially self-sufficient microfinance institution in the world. The experiences of ASA's managerial dynamism and replicat


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Alpha Black Zero - Intrepid Protocol
Main Page / Games
Most of the time, squad-based strategy games require great use of thought, planning, and the player's ability to take advantage of stealth-based gameplay. They're in-depth, involving and generally regarded as some of the best games around. And while the same is true of GMX's Alpha Black Zero, the game also delivers an action element and a greater freedom of choice without sacrificing the intelligent gameplay found in most titles of this genre. A somewhat futuristic affair, Alpha Black Zero pits players in a conflicted world, filled to the brim with hostile enemies and deadly weaponry as a team of five elite soldiers charged with restoring peace... or at least some of it. It's very typical of a squad-based shooter, but where most games of this type require gamers to proceed through a level in a very specific way, Alpha Black Zero gives players the freedom to make their own choices. Players can progress by means of stealth, or by opting for an all-out skirmish. Controlling a team of five is also made easier not just by the fact that they have their own wits about them, but also because they follow your commands to the letter. Quite simply, you can't blame your teammates for failed missions. Any mistakes made are as a result of bad commands. In addition, opponent AI is severe. Your enemies are difficult to eliminate, and will generally come down hard on you if you fail to take them out efficiently. Alpha Black Zero isn't as big a graphical achievement as, say, Halo 2 and the likes, but as a game it's as good as anything to go before it. Well worth playing. read more
For only 29.99 GBP
Shop: Direct4Games
SubCategory: Games
LastUpdated: 2006-11-07 04:06:07
Most of the time, squad-based strategy games require great use of thought, planning, and the player's ability to take advantage of stealth-based gameplay. They're in-depth, involving and generally regarded as some of the best games around. And while the same is true of GMX's Alpha Black Zero, the game also delivers an action element and a greater freedom of choice without sacrificing the intelligent gameplay found in most titles of this genre. A somewhat futuristic affair, Alpha Black Zero pits players in a conflicted world, filled to the brim with hostile enemies and deadly weaponry as a team of five elite soldiers charged with restoring peace... or at least some of it. It's very typical of a squad-based shooter, but where most games of this type require gamers to proceed through a level in a very specific way, Alpha Black Zero gives players the freedom to make their own choices. Players can progress by means of stealth, or by opting for an all-out skirmish. Controlling a team of five is also made easier not just by the fact that they have their own wits about them, but also because they follow your commands to the letter. Quite simply, you can't blame your teammates for failed missions. Any mistakes made are as a result of bad commands. In addition, opponent AI is severe. Your enemies are difficult to eliminate, and will generally come down hard on you if you fail to take them out efficiently. Alpha Black Zero isn't as big a graphical achievement as, say, Halo 2 and the likes, but as a game it's as good as anything to go before it. Well worth playing.
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text:
\section{Results}

\subsection{Experiment Setup}
\begin{figure}
    \centering
    \vspace{0.2cm}
    \includegraphics[width=1\columnwidth]{figs/sample_compare_new.pdf}
    \caption{The learning curves show the average of five random seeds for each action space. Actions in task space are able to learn rough stepping gaits at the beginning of the training and quickly converge to a plateau. However, actions in joint space need to explore for joint coupling before the agents can effectively explore in a more useful episode. Star represents when we observe the robot starting to walk stably in simulation with the learned policy, which takes around 8 and 32 hours (wall-clock time) for task space and joint space respectively. }
    \label{fig:sample}
\end{figure}

\begin{figure*}[ht!]
    \centering
    \vspace{0.3cm}
    \includegraphics[width=0.9\linewidth]{figs/simresults.pdf}
    \caption{Simulation results for learned policies. Plot (a) shows a speed tracking comparison. Plot (b) shows the ground reaction forces recorded from the speed tracking test averaged over the stance period.}
    \label{fig:sim}
    \vspace{-.5cm}
\end{figure*}

For our experiments, the learning problem is given with a set of reference signals $\mathbf{x^{ref}, F^{ref}}$ that are optimized based on the actuated spring mass models in the extended work from \cite{Green2020}.
These trajectories are not dynamically feasible because of the simplified model, but are useful feedforward signals for the inverse dynamics controller.
The gains for the task space controllers are $\mathbf{K_P^{swing}}=\{300, 150, 400\}\text{s}^{-2}$ and $\mathbf{K_D^{swing}}= \{10, 3, 10\}\text{s}^{-1}$ for the forward/lateral/vertical directions respectively. $\mathbf{K_P^{stance}}$ and $\mathbf{K_D^{stance}}$ have the same corresponding values but with units N/m and Ns/m.
The gains for the hip yaw motors and the foot pitch motors are $\mathbf{K_P^{joint}}=$\{100, 50\}$\text{Nm/rad}$ and $\mathbf{K_D^{joint}}=$\{10, 5\}$\text{Nm/rads}^{-1}$ respectively.
The gains are the same in simulation and on hardware, and are tuned to track task space targets without stability problems across different configurations.

To compare the proposed method with actions learned in joint space on the sample efficiency and task space dynamics, we implemented the joint space action learning approach where policy outputs the residual joint setpoints with gains and PD controller similar to \cite{Xie2019}.
We used the same references signals from the spring mass model for both action spaces, allowing both action spaces to use the same references for reward and control. The joint space references are numerically derived using offline inverse kinematics on the same set of reference motions.

For the sampling and training process, we use \emph{cassie-mujoco-sim} simulation packages\footnote{https://github.com/osudrl/cassie-mujoco-sim} based on the Mujoco physics simulator \cite{Todorov2012}.
The training horizon for each episode is 150 steps, which corresponds to running the policy at 40Hz for 3.75 seconds. We sample 5000 simulation timesteps for each iteration of training.
During the learning process, the initial conditions of the robot are randomized for each episode, including the reference phase and the velocities of the robot base to simulate disturbances.
The termination condition of each episode only considers if the robot base height falls below 0.6 meters.
Policies were trained using 50 cores on a dual Intel
Xeon
Platinum 8280 server.

\subsection{Simulation Results}
We primarily focused on comparisons in simulations for sample efficiency and how well each action space achieves the desired task space control interests.

\subsubsection{Sample Efficiency}
We trained policies for both action spaces to compare sample efficiency under the same reward and learning setup.
Fig. \ref{fig:sample} shows that learning task space actions reaches high reward much faster than learning joint space actions.
This increase in sample efficiency has a large practical impact, decreasing wall-clock training time from 32 hours to 8 hours in order to obtain a policy that starts to walk stably.
During the early stages of training, actions in task space have a direct effect on the robot dynamics by moving the feet around in coordination, as opposed to actions in joint space which results in the robot standing still without any intentions to move the robot base towards the desired velocities. We show the during-training comparison in the video and discuss the potential cause further in Section \ref{sec:explore_noise}.

\subsubsection{Commanded Velocity Tracking}
We tested the steady-state locomotion by sending varying desired speed commands into the corresponding input to the policy. During training, desired speed for each episode holds constant, but the robot is initialized with random velocity of the robot base. The final learned policy can interpolate across the desired speed range as shown in Fig. \ref{fig:sim}(a). Both action spaces have comparable performance on speed tracking.

\subsubsection{Ground Reaction Forces}
We used the optimized reduced-order references to derive the references in task space and joint space.
By following the desired CoM dynamics defined in the reward, the ground reaction forces should capture the trend described from the references as seen from Fig. \ref{fig:sim}(b).
Ground reaction forces are recorded during the speed tracking experiments where Cassie walks across the speed range.
Although policies by both action spaces are able to produce stable locomotion behaviors, the underlying ground reaction forces are very different.
Directly learning task space actions produces the desired ground reaction forces over repeated gait cycles, whereas joint space actions cannot reproduce the desired stance dynamics even after reward plateaus.
Furthermore, we can see that the task space actions are not exactly following the feedforward force profile, but rather adapting the profile and stretching the mid to late stance to achieve better stability.

\subsection{Exploration Noise in Action Space}
\label{sec:explore_noise}
Noise in learning algorithms allows agents to explore their environment but it can make learning very brittle without the proper tuning.
For legged locomotion tasks, the robot dynamics are primarily influenced through the motion of the feet. In order to evaluate the effective exploration by both action spaces, we compare the size of the space covered by the feet during random exploration.
In our experiments we find that the exploration noise $\sigma$ in the Gaussian policies must be different for each action space in order to produce feasible policies.
Thus, we analyzed the effects of the noise for each action space independent of the particular learning algorithms.
Fig. \ref{fig:noise} shows the distribution comparison of sampled task space locations when sampling in each action space.
Actions are drawn from a Gaussian distribution $a \sim \mathcal{N}(0,\,\sigma^{2})$, where $log\sigma = -1.5$ for joint space and $log\sigma = -2.5$ for task space, same as used in PPO in the Appendix.
First, we collected data where we apply the action noise to a single 40Hz timestep.
This allows us to compare the joint space and task space action distributions.
In the single step plots on the right we can see that the distributions are comparable, with joint space exploring slightly more in the Y (lateral) direction.

We then test how the action noise propagates through an entire episode, where random actions are continually drawn and acted until a termination condition is satisfied or the horizon is reached. We collected 50,000 samples for each scenario.
We see that over a whole episode that the task space sampling covers a much larger area of the state space in the episode scenario. Furthermore, since termination conditions are used, the data that task space sampling generates captures dynamics before falling. In other words, it is not just the case that task space sampling generates a larger range of states, it generates a larger range of \textit{useful} states. We noticed that simply increasing the noise in joint space to $log\sigma = -1$ during sampling did not increase the feet range of motions, because the extra noise would just cause the robot to fall down and lead to bad states cutoff by the termination condition. This result highlights the need for effective \textit{directed} exploration for legged locomotion in its natural action space. One cannot just use more random noises in an effort to sample more states to improve learning. Task space both contains the primary control interests and allows for directed exploration that greatly improves sample efficiency.

\begin{figure}
    \centering
    \includegraphics[width=0.9\columnwidth]{figs/sample_distribution.pdf}
    \caption{
    Distributions of foot locations under random action noises for each action space. Single step shows both action spaces covering a similar space of foot locations. Task space shows a more direct exploration over the entire episode that results into a larger space covered of feet movements compared to joint space.
    ``Stripe-like" samples are a result of using a small set of initialization states of the robot.
    }
    \label{fig:noise}
\end{figure}

\subsection{Hardware Transfer}
We are able to transfer the learned policies onto Cassie without additional randomizations on robot model parameters.
The supplementary video shows the robot is capable of stepping in-place and walking at various speeds on treadmill.

\section{APPENDIX}
\begin{appendix}
Hyperparameters for Proximal Policy Gradient,
\renewcommand{\arraystretch}{1.2}
\begin{table}[h!]
    \centering
    \vspace{-.25cm}
    \begin{tabular}{l | l}
      \hline
      Parameter & Value  \\
      \hline
      Horizon ($H$) & 150 \\
      Adam stepsize & 1$\times10^{-4}$  \\
      Number epochs & 8 \\
      Minibatch size & 1024  \\
      Discount ($\gamma$) & 0.99\\
      Clipping parameter ($\epsilon$) & 0.2 \\
      Max gradient norm & 0.05\\
      Log stddev. of action distribution & -2.5 (task space) \\
      & -1.5 (joint space) \\
      \hline
    \end{tabular}

    \label{tab:ppo_hyper}
    \vspace{-.4cm}
\end{table}

\end{appendix}

\section{CONCLUSION}

In this work, we present an approach that combines bipedal robot control with reinforcement learning to enable a policy to learn actions in task space.
We show that learning actions in task space can produce steady-state locomotion behaviors and demonstrated the successful sim-to-real transfer on Cassie.
Specifically, the learned actions represent setpoint residuals relative to the robot base.
By varying this setpoint over the stance and swing phases, the learned policies adapt the references trajectories while being able to reject disturbances.

The proposed approach unloads RL from having to re-learn the known robot models, so it is sample efficient and explores the task space regions more broadly to find solutions. We found that simply increasing the action noises in joint space does not correlate to better exploration, because undirected exploration in Gaussian policies may gather many useless samples.
On the other hand, directly giving such joint coordination information into learning may hinder the discovery of interesting coupling effects among the joints.
Further research may focus on how to embed the notions of the effective task space changes into the learning process.
Learning task space action could also allow RL to communicate in the same language with potentially various layers of a control hierarchy, where exchange signals are mostly defined in task space.
Future research may benefit on incorporating more advanced model-based control designs, such as adding centroidal dynamics as one of the actions.

\section{INTRODUCTION}

Robotic legged locomotion has benefited from decades of focused analysis and rigorous control theory.
Many of the most successful and capable systems choose to control actions in task space by controlling foot placements, ground reactions forces (GRFs), swing foot motions, or center-of-mass (CoM) motion to maintain balance and stability.
In the simplest case, reduced order models often parameterize locomotion dynamics by choosing a particular set of actions in task space, such as feet setpoints relative to CoM along with the controlled impedance, so that the model can generate compliant behaviors and reproduce the characteristic dynamics observed from animals \cite{Green2020, Geyer2006, Blum2014}.

\begin{figure}
    \centering

    \includegraphics[width=0.48\textwidth]{figs/title_v3.png}
    \caption{

    Learned policy outputs actions represented in task space to directly control $\mathbf{X, \dot X, F}$. The learned policy produces the desired task space dynamics in simulation and on hardware Cassie.

    }
    \label{fig:title}
\end{figure}

By contrast, current model-free reinforcement learning (RL) methods for controlling legged robots mostly focus on learning joint control actions and defining the reward to track joint space trajectories \cite{Xie2019, Tan-RSS-18, Siekmann-RSS-20}.
Representing learned actions in joint space may obstruct the policy from directly optimizing primary control interests and regulating task space dynamics.
Joint space policies must implicitly re-learn significant information about the relation between task space and joint space in order to coordinate the joints for successful control of task space behaviors.
These approaches also lack an interface to many successful model-based controllers, which exploit the task space formulation of the locomotion problems.

Our approach aims to address such drawbacks by learning at the level of task space for legged locomotion.
Actions in task space, such as feet movements, can directly influence the primary control interests and the behaviors of the agents, leading to more effective exploration than noisy, undirected exploration in joint space.
Furthermore, the known models often come with the use of model-based controllers that maps from task space dynamics to joint-level commands.
This allows the learned policies to interface with the same language as existing reduced order models \cite{Green2020} and whole-body control methods \cite{Apgar2018a, Luo2018a}.

In our proposed method, the learned policy outputs actions directly in task space as residual setpoints of the feet relative to the robot base. In particular, we integrate inverse dynamics reasoning about the known robot model so that the RL-learned task space actions are converted to joint commands. Thus, the agent does not need to learn about joint coordination and can explore directly in the more meaningful task space. In addition, the policy can now directly learn from reward functions that align with task space interests.

Our first contribution is to show that this integrated approach can significantly reduce the sample requirements of reinforcement learning on bipedal locomotion, which agrees with other task space formulation in non-locomotion domains \cite{Martin-Martin2019, Varin2019a}.
Our second contribution is to demonstrate the successful steady-state walking in simulation and on hardware using the bipedal robot Cassie. We use spring mass models to generate a library of desired task space dynamics as the references for the learning process. By integrating learning with the task space controller, the learned control policies can adapt the references and produce desired control interests. To our knowledge this is the first demonstration of a task space learned policy for real-world dynamic bipedal locomotion.

\section*{Acknowledgments}
\small{We thank Intel for providing vLab resources. We also thank DRL members and Jianwen Luo for helpful discussions.}

\def\footnotesize{\footnotesize}
\bibliographystyle{IEEEtranN}

\section{Control Structure}
\label{sec:control}

To facilitate the policy directly learning locomotion behaviors in task space, this section explains the task space control structure to which the learned controller issues commands. The overall control diagram is illustrated in Fig \ref{fig:framework}.

\subsection{Task Space Constrained Dynamics}
The Cassie robot's dynamics can be written in terms of its generalized, floating base coordinates.
Each leg contains two passive springs and a closed kinematic loop.
Following \cite{Apgar2018a}, we treat all of the passive spring joints as rigid for the purposes of control.
We can then eliminate dependent coordinates in the leg using acceleration level loop-closure constraints \cite{featherstone2014rigid}.
In this way, the equation of motion is written in terms of only the independent coordinates so that $\mathbf{q}=[\mathbf{q_{base}} \in \mathbf{SE}(3), \mathbf{q_{joints}} \in \mathbb{R}^{10}]$ and,
\begin{align}
    &\mathbf{M(q)\Ddot{q} + C(q,\Dot{q})\Dot{q} + G(q)=B\tau + J^T F_{feet}}, \label{eq:3}
\end{align}
where $\mathbf{M(q)} \in \mathbb{R}^{n \times n}$ is the inertia matrix, $\mathbf{C(q,\Dot{q})\Dot{q}} \in \mathbb{R}^{n}$ includes Coriolis and centripetal forces, $\mathbf{G(q)} \in \mathbb{R}^{n}$ is the gravity term, and $\mathbf{B} \in \mathbb{R}^{n \times 10}$ is the actuator selector matrix given the torque at each actuated joint $\tau \in \mathbb{R} ^ {10}$.
The Jacobian to the feet is $\mathbf{J}$ and the ground reaction force applied to the feet is $\mathbf{F_{feet}}$.
The translational positions, velocities, and accelerations of the feet are a function of the generalized coordinates,
\vspace{-5pt}
\begin{align}
    \mathbf{x} &= \mathbf{FK(q)} \nonumber\\
    \mathbf{\dot{x}} &= \mathbf{J \dot{q}} \nonumber \\
    \mathbf{\ddot{x}} &= \mathbf{J \ddot{q} + \dot{J} \dot{q}}, \nonumber
\end{align}
\vspace{-5pt}
where $\mathbf{FK(q)}$ is forward kinematics of the legs.

\subsection{Task Space Control}
The task space inverse dynamics controllers consist of swing phase, stance phase, and the transition between the two.
This basic setup allows the learning to explore in terms of different subfunctions in locomotion. $\mathbf{x}, \mathbf{x^{\delta}}, \mathbf{x^{ref}}$ are feet positions from the state estimator, the learned policy, and references.

\subsubsection{Swing leg}
Swing leg control is formed as a PD acceleration controller with inverse dynamics on feet to reach to desired setpoints relative to base. First, the desired feet accelerations based on task space PD feedback is calculated as,
\begin{equation}
    \mathbf{\Ddot{x}^{des} = K_P^{swing}(x^{ref} + x^{\delta} - x) + K_D^{swing}(0 - \dot{x})}.  \nonumber
\end{equation}
Then we can compute the motor torques required to create the commanded task space acceleration using task space inertia, ($\mathbf{\Lambda = (JM^{-1}J^{T})^{-1}}$).
We dropped the velocity product terms from \eqref{eq:3} and $\mathbf{\dot{J} \dot{q}}$ resulting in,
\begin{equation}

    \mathbf{\tau_{swing} = J^T (\Lambda\Ddot{x}^{des} + \Lambda JM^{-1}G)}. \nonumber
\end{equation}

\subsubsection{Stance Leg}
The stance leg is primarily concerned with force interactions, so the control converts the set point commands into forces though the impedance controller with feedforward forces,
\begin{equation}
    \mathbf{F^{des}_{feet} = K_P^{stance}(x^{ref} + x^{\delta} - x) + K_D^{stance}(0- \dot{x}) + F^{ref}}. \nonumber
\end{equation}
This force command is translated into joint torques through the simple Jacobian transpose control of feet to robot base,
\begin{equation}
    \mathbf{\tau_{stance} = J^TF^{des}_{feet}}. \nonumber
\end{equation}
This is the torque that would be required to statically apply the force command in the absence of all other forces.

\subsubsection{Transitions between Stance/Swing}
The control structure also includes a transitioning rule to switch between the two phases of the gait, and relies on RL to find solutions to stabilize the robot. For each leg, the overall torque computation is,
\begin{equation}
    \mathbf{\tau_{ID} = \phi \tau_{stance} + (\text{1}-\phi) \tau_{swing}},
\end{equation}
where $\phi \in [0,1]$ is a scalar output that depends on the phase along the target force profile on each leg, meaning if the target force profile is active, then $\phi = 1$ and if it is not active, then $\phi = 0$. This also allows for a double support phase where $\phi$ smoothly transits between stance and swing phases. During the transition phases around toe-off and touchdown, $\phi$ linearly interpolates between 0 and 1 and vice versa.

\subsubsection{Feet Orientation Control}
Swing and stance control only commands the translational positions of the feet.
Foot orientation can be controlled via joint control of the hip yaw and foot pitch motors on each leg $\mathbf{\theta = \{\theta_{hipyaw}, \theta_{footpitch}\}}$.
We use joint PD controllers on only the hip yaw and foot pitch motors. The policy outputs $\mathbf{\theta^{\delta}}$ to control the feet orientation to be flat and facing forward. $\mathbf{\theta^{ref}}$ represents a neutral joint position and is constant for each motor.
These complimentary torques are defined as,
\begin{equation}
\begin{aligned}
    \mathbf{\tau_{orient}} &=  \mathbf{\{\tau_{hip yaw}, \tau_{foot pitch}\}} \\
    &=\mathbf{ K_P^{joint}(\theta^{ref} + \theta^{\delta} - \theta) - K_D^{joint} \dot{\theta}}. \label{eq:foot}
\end{aligned}
\end{equation}

\subsubsection{Joint Level Commands and Control Rate}
The final torque commands to low-level actuators are,
\begin{equation}
    \mathbf{\tau_{cmd} = \tau_{ID} + \tau_{orient}}. \label{eq:10}
\end{equation}

As shown in Fig. \ref{fig:framework}, the full inverse dynamics control runs at 2kHz to compute torques based on \eqref{eq:10}. The RL policy outputs residual setpoints at 40Hz, meaning the desired target setpoints are updated at 40Hz.

\section{RL Problem Formulation}

\subsection{Learning Algorithm}
We use the Proximal Policy Optimization (PPO) algorithm \cite{Schulman2017}, a model-free policy gradient method with an actor-critic network.
Both the actor and the critic use fully connected feed-forward neural networks with one hidden layer of size 256 and ReLU activation.
Output layers use $tanh$ so that the policy outputs are bounded.
We used the clipped objective for PPO with hyperparameters listed in the Appendix.

\subsection{Policy Inputs}
The input to the neural network policy consists of the observed robot state and user commands.
The entire observations $\mathbf{S \in \mathbb{R}}^{47}$ input to the neural network are measured through the proprietary state-estimator from Agility robotics.
Using estimated states as inputs allows sim-to-real transfer without extra steps when considering possible differences between simulator and hardware.
The state space covers primarily the observations on the robot base and feet.
More specifically, $\mathbf{S}$ includes commanded forward velocity $\mathbb{R}^{1}$, base's translational, rotational velocities $\mathbb{R}^{6}$ and orientations in quaternion $\mathbb{R}^{4}$, actuated and un-actuated joint positions and velocities measurable on hardware $\mathbb{R}^{28}$, and feet Cartesian positions relative to robot base $\mathbb{R}^{6}$.
Also, $\mathbf S$ has an additional $\mathbb{R}^{2}$ input based on the phase $\phi$ variable along the references.
This input is calculated with both sine and cosine functions to create unique phase identifiers and to wrap around the cycle.

\subsection{Action Space}
The action space $\mathbf{A \in \mathbb{R}}^{10}$ is defined as residual terms, including the Cartesian positions of feet $\mathbb{R}^{6}$ relative to the robot floating-base and the joint positions of hip yaw and foot pitch $\mathbb{R}^{4}$ for foot orientation control in \eqref{eq:foot}.

\subsection{Reward}
The reward centers on achieving task space control interests from the references for the robot base and end-effector feet.
The desired reference quantities are defined with the corresponding phase along the references. We chose to not fully track the references because a tracking-only reward could result in the policy fitting too much to trajectories that do not have the notion of the full-order robot, making performance very brittle.
Rather, we define a more general reward based on the fundamentals of locomotion: transitions between stance and swing. By focusing on task space costs this structure is flexible and can be applied to bipedal locomotion in general as opposed to constraining all joints to specific motion dictated by a robot specific trajectory. We still use the information encoded by the references by having them define the contact sequence and timing, but this method allows for more adaptive solutions.

Rewards on the feet $r_{\text{lfoot}} \text{ or } r_{\text{rfoot}}$ are defined as locomotion subfunctions, swing and stance for each foot.
Swing reward $r_{\text{foot-swing}}$ calculates the foot clearance cost weighted by swing foot velocities. Stance reward $r_{\text{foot-stance}}$ checks whether the foot is in contact with the ground by calculating both foot height and velocities.
With the references from reduced-order models, the timing of swing and stance is predefined, as is the desired foot clearance height $h_{\text{swing-height}}$ which is set to 0.15m.
Similar to transitions defined in controller \eqref{eq:10}, a continuous transition weighting $\phi \in [0, 1]$ indicates whether each foot should be in stance or swing,
\begin{align}
     r_{\text{lfoot}} \text{ or } r_{\text{rfoot}} &= \phi r_{\text{foot-stance}} + (1-\phi) r_{\text{foot-swing}}.  \nonumber
\end{align}
The reward function then consists of all the control interests on the robot floating-base defined by references on $r_{\text{base-xvel}}$,  $r_{\text{base-zvel}}$, and $r_{\text{base-zpos}}$, along with extra terms to encourage smooth, stable behaviors as,
\begin{align}
    \text{R} &= 0.1 r_{\text{lfoot}} + 0.1 r_{\text{rfoot}} + 0.15 r_{\text{base-xvel}} + 0.1 r_{\text{base-zvel}} \\
    &+ 0.1 r_{\text{base-zpos}} + 0.1r_{\text{base-orientation}} + 0.15r_{\text{base-straight}} \nonumber \\
      & + 0.05r_{\text{foot-orientation}} + 0.025r_{\text{base-linear-accel}} \nonumber \\
      &+0.025r_{\text{base-angular-vel}} + 0.1r_{\text{action-smooth}} \nonumber
\end{align}

Each reward term is a function of one or more physical quantities of the robot $s$ as described in Table \ref{tab:rewards}. The values are normalized using the exponential $r(s,a) =  \exp\Big(\frac{1}{T} \sum^{T}_{i=0}f(s,a)\Big)$ as a kernel function, creating a smooth reward between $(0, 1]$ for each reward term. Each $f$ is averaged over the duration $T=50$ between each policy update, given torque control frequency. The neutral orientation in quaternion $\mathbf{q_{\text{neutral}}}=(1,0,0,0)$.
\renewcommand{\arraystretch}{1.3}
\begin{table}[h!]
    \centering
    \vspace{-.25cm}
    \begin{tabular}{l | l}
      \hline
      Terms ($f(s,a)$) & Expressions  \\
      \hline
      foot-stance & $x_{\text{foot-height}}^2 +  \|\mathbf{\Dot{x}_{\text{foot}}}\|_{2}$ \\
      foot-swing & $40 (h_{\text{foot-height}} - x_{\text{z-height}})^2$ \\

      base-xvel & $3|\Dot{x}_{\text{base}} - \Dot{x}_{\text{base-ref}}|$ \\

      base-zvel & $3|\Dot{z}_{\text{base}} - \Dot{z}_{\text{base-ref}}|$ \\

      base-zpos & $3|z_{\text{base}} - z_{\text{base-ref}}|$ \\

      base-orientation & $50(1-\langle \mathbf{q_{\text{base}}}, \mathbf{q_{\text{neutral}}} \rangle)$  \\

      base-straight & $5|y_{\text{base}}| + 3|\Dot{y}_{\text{base}}|$  \\

      foot-orientation & $30(1-\langle \mathbf{q_{\text{foot}}, q_{\text{neutral}}} \rangle)$  \\

      base-linear-accel & $\|\mathbf{\Ddot{x}_{\text{base}}}\|_{2}$
      \\

      base-angular-vel & $\|\bm{\omega_{\text{base}}}\|_{2}$ \\

      action-smooth & $3 \|\mathbf{a_{i} - a_{i-1}}\|_{2}$ \\
      \hline
    \end{tabular}
    \caption{Reward Function Details}
    \label{tab:rewards}
    \vspace{-.4cm}
\end{table}

\section{RELATED WORK}

\begin{figure*}[t]
    \centering
    \vspace{0.3cm}
    \includegraphics[width=0.98\textwidth]{figs/framework_id_only_v5.pdf}
    \caption{Approach Overview:

    The output of the neural network policies $\pi(s)$ are residual setpoints of the feet relative to the robot base.
    These residual setpoint components $\mathbf{x^{\delta} \in \mathbb{R}^6}$ together with the reference signals $\mathbf{ \{x^{ref}, F^{ref}\}}$ form the overall commands (red colored, updated at 40Hz) to the inverse dynamics control of the feet relative to the base.
    Furthermore, learned actions are used in different control strategies depending on the gait phase.
    For stance foot, the learned actions will be composed with task space impedance to produce forces that are added to the feedforward force to stabilize the robot.
    For swing foot, the learned actions will be converted to desired foot accelerations by task space PD control to then create compliant swing motions.
    Signals from the policy are commands to the inverse dynamics controller which maps commanded setpoints to torques (blue colored, 2kHz). Details are in Section \ref{sec:control}. }
    \label{fig:framework}
    \vspace{-.5cm}
\end{figure*}
\textbf{Action Space of RL for Dynamic Robot Control.}
In RL problems, the action space defines the range of options agents can take to influence dynamics and make transitions between states.
When using RL to generate control policies for dynamic robots, such as legged robots, the action space also defines the direct interface from policy to low-level actuator commands, such as joint positions or torques.
Previous research \cite{Peng2017a} showed that using joint setpoints as the policy output into a joint PD controller provides a better interface to control fast locomotion dynamics than pure torques or joint velocities.
Joint position control has then been used extensively in RL approaches for controlling legged robots in simulation and on hardware \cite{Xie2019, Hwangbo2019}.

Recent studies \cite{Martin-Martin2019, Varin2019a, 8793506} proposed a set of new action spaces for contact-sensitive manipulation control policies by defining the action space in the manipulator's end-effector space (task space).
These approaches show that, by leveraging the kinematic and dynamic models of the robot, the learning process is more sample efficient than having actions defined in joint space.
The learned policy is also able to better perform contact-rich tasks with constrained dynamics on redundant and fixed-based manipulators.
In our work, we extend this idea and show the sample efficient learning even when the learning method is applied to underactuated floating-base robots where contact state changes more frequently and is fundamental for system stability.

\textbf{Model-based Control for Bipedal Robots.}
Many successful model-based controllers for bipedal robots center on task space control methods to produce desired CoM and feet dynamics. These methods are integrated closely with the known robot models that are readily available.
For example, \cite{Apgar2018a} uses an operational space controller to generate robust gaits while the dynamics model of the robot is used to perform fast online optimization.
\cite{Martin2017a} proposes tracking the CoM trajectory and GRFs with LQR and an inverse dynamics controller.
Despite many successes with model-based control methods, less is known on how to create a structured approach for learning locomotion policies that can work with existing model-based control on physical hardware.
Our approach aims to provide an integrated method by bridging both sides through the same language defined in task space.

\textbf{RL with Model-based Controllers.}
Combining conventional feedback control with reinforcement learning can be done in various ways to improve sample efficiency and take advantage of existing controllers.
By decomposing actuator commands into both learned- and model-based controllers, the residual learning approach \cite{Johannink2019} has shown promise towards merging both sides. The method used in \cite{9197125} improves sample efficiency by further dividing the control space in terms of uncertainty of the controller.
Learning only happens near the uncertain regions where the model-based controller has less confidence.
\cite{Xie2020} proposed using RL to generate foot contact sequences that are sent to a low-level task space controller on a quadruped, which demonstrates an integrated way of generating control policies for quadrupeds using a linearized centroidal dynamics model.
Our aim is to learn reactive policies in task space for a human scale bipedal robot.
We formulate the low-level task space controller into swing and stance phases that takes commands from learned policies.

redpajama_set_name: RedPajamaArXiv


If you are contemplating about new landscaping or if you have an existing landscape that requires hours of your time to water, an underground irrigation system is worth considering.
A properly designed irrigation system broadcasts water evenly to insure consistent coverage. Application rates are adjustable according plant requirements. It is a common misconception that irrigation systems waste water. They actually use water very efficiently. There is almost no overspray and each system is on a consistent schedule requiring less water to maintain the proper moisture levels within the soil. Plants not only thrive with the proper amount of water, but thrive when watered consistently.
We have designed many standard systems that operate using city water and also more advanced systems that utilize rainwater.


This verse is translated in the New King James Version as, "Now when He rose early on the first day of the week, He appeared. . . ." The King James Version translates it, "Now when Jesus was risen early the first day of the week, He appeared. . . ." Finally, The Interlinear Bible, in its word-for-word translation, renders it, "having risen And early on the first of the week, He appeared. . . ." Does this verse say the resurrection was early on the first day of the week?
Here is another instance of the translators mistranslating a verse because of their preconceived beliefs, arising from the "traditions of men"! The commentators admit the construction of the sentence is unusual, but refuse to acknowledge its plain sense. The literal translation, with only slight modification, gives the best rendering: "And having risen, early on the first day of the week He appeared. . . ."
The Greek word translated "having risen" (anastas, an active aorist participle) suggests an action completed prior to the time of the main verb, in this case, "appeared." Thus, Jesus was resurrected sometime before He appeared to Mary Magdalene early on the first day of the week. That is all that Mark is trying to say! Placing a simple comma after "rose" (NKJV) or "risen" (KJV) is the easiest way to resolve the matter. The words of the angel to the women, "He is risen!" (Matthew 28:6; Mark 16:6; Luke 24:6) also give the sense that He was raised at some point prior to His Sunday morning appearances.


Last night, the Giles Wilson team were delighted to attend the Law Society's 10th annual Excellence Awards. It is with great delight that we can announce that, our partner, Melinda Giles, was named as 'Highly Commended' in the Solicitor of the Year – Private Practice category.
One of the most highly regarded events in the law social calendar, The Law Society's Excellence Awards recognises exceptional work in legal services. The night saw the best and brightest legal professionals from all over the UK travel to the Hilton Park Lane in London to celebrate outstanding work and listen to speeches from the Law Society President, Robert Bourns, Baroness Shami Chakrabarti and Tan Ikram.
As a passionate advocate for gender equality, Melinda declined to be entered into the 'Woman Lawyer of the Year' category but she was delighted for her work to be recognised within the highly respected 'Solicitor of the Year – Private Practice' category. The category saw competition from some of the most talented legal professionals in the UK, including winner John Halford of Bindmans LLP, but Melinda was honoured to be named as 'Highly Commended' Solicitor of the Year.
As the head of non-contentious matters at Giles Wilson, Melinda guides our team of expert solicitors in all areas of Private Client law, including wills and trusts, Lasting Powers of Attorney, probate and certain areas of property law. She also works closely with the litigation team at Giles Wilson on contentious probate issues.
She has been working in the private client area since 1987 and during this time has established herself as one of the leading specialists in this area. Melinda has represented clients at mental health tribunals, has been a Court of Protection panel deputy for over 10 years and is on the MENCAP-recommended list of specialist solicitors, providing wills and trust advice to parents of children with learning disabilities.
Giles Wilson would like to congratulate Melinda, and all other winners and nominees, for their achievements and thank The Law Society for a fantastic evening.
If you would like to contact our award-winning solicitors for friendly and informative legal advice, arrange a free 30-minute consultation at our offices in Leigh-on-Sea and Rochford on 01702 477106. Also, don't forget to find us on Facebook, Google+, LinkedIn, Twitter and Instagram for the latest updates from our local law firm.


text:
Kansas City Symphony holds benefit for area high school
In Wednesday's (4/15) Kansas City Star, Robert A. Cronkleton writes, "Liberty Public Schools' instrumental music program will get a financial boost tonight when the Kansas City Symphony performs a benefit concert. Mary Lou Jones, director of orchestras for Liberty High School, said the symphony stepped forward to raise money for the music program after hearing about the district's financial difficulties and budget cuts that are coming next year. … Earlier this year, the Liberty school board cut $400,000 from the fine arts program in the 2009-2010 budget. Among other changes: The fifth-grade strings programs will be offered as a before- or after-school activity. Some of the proceeds from the 7 p.m. concert may be used to help defray costs to send band students to Hawaii in June and to let orchestra members attend the Shanghai World Exhibition in China next year. … Steven Murray, education manager for the Kansas City Symphony, said the symphony's Support School Music program was started in 2003 to help schools raise money for music programs and to heighten awareness of the importance of in-school music programs."
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text:
PGA of America to permit distance-measuring devices during play at its 2021 championships
Icon Sportswire
By Daniel Rapaport
The PGA of America will permit the use of distance-measuring devices during play in its 2021 major championships, including the PGA Championship at Kiawah Island (May 20-23).
The association announced the decision on Tuesday, with it also applying to the KPMG Women's PGA Championship (June 24-27 at Atlanta Athletic Club) and the KitchenAid Senior PGA Championship (May 27-30 at Southern Hills).
"We're always interested in methods that may help improve the flow of play during our championships," said Jim Richerson, president of the PGA of America, in a release. "The use of distance-measuring devices is already common within the game and is now a part of the Rules of Golf. Players and caddies have long used them during practice rounds to gather relevant yardages."
Anything used by players and caddies will need to comply with Rule 4.3a (1), which prohibits any devices that measure elevation changes or "directional information," such as using a device to recommend a line of play or club selection.
The announcement suggests this is not a one-year exception, and that the devices will be permitted at these three championships moving forward.
When the USGA and R&A modernized the Rules of Golf in 2019, the language of the rules changed to allow the use of distance-measuring devices as a default rather than something a tournament could permit through a local rule. Tournaments committees now must apply a local rule when they want to prohibit the use of DMDs.
That's the case for events on the PGA Tour, where distance-measuring devices are permitted only in practice rounds. During competition, players get their yardages from highly detailed yardage books, which do indeed include information about elevation changes. These yardage books have long been debated as a root cause of slow-play across professional golf.
It is not yet clear whether fans will be permitted at any of the PGA of America's major championships this year.
TagsgolfGolf + distance-measuring devicesKiawah IslandU.S. PGA ChampionshipUSGA
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