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Posts tagged DeflateGate
Deflating your ego
Few quarterbacks have dominated the NFL like Tom Brady. In his 13 full seasons, he has led the New England Patriots to four Super Bowl titles. What he may lack in raw talent, he makes up for in hard work. He watches lots of game film and pays attention to detail on and off the field, which is a common character quality of someone who performs at the highest level like he does.
But now the reputation of the reigning Super Bowl MVP is tarnished, with the league recently announcing that he will be suspended for the first four games of the upcoming season for participating in the deflating of footballs in the first half of the AFC championship game.
Breaking the rules, as the NFL has claimed, may not have been the most damaging thing Tom Brady did. He may not have even been suspended if he had admitted early on to his involvement (whatever that was) and apologized to the league for his indiscretion. Instead he allowed his agent to speak for him and deny even knowing of a scandal.
But after spending months reviewing the evidence surrounding the "DeflateGate" scandal, the NFL found enough in text messages to confidently say that Brady was involved in some way. And now public opinion has turned against him, with about 70 percent of avid football fans believing Brady cheated.
Let's face it: if you don't take the blame for your own mistakes (as small or as big as they may be) other people will spend their time, effort, and energy putting the blame on you. I learned that lesson in my last year with the Seattle Seahawks and gained a great appreciation for the importance of accepting responsibility. Even though I wasn't involved in a cheating scandal or at the center of some controversy, the incident did involve my integrity.
I was the starting quarterback with the Seattle Seahawks and we had just suffered a 20-13 loss in an important game with Kansas City. In press interviews after the game, rather than own up to my shortcomings, I chose to play the optimist. "We're going to do better next week; we're going to turn the corner and go forward."
It wasn't until later in the week that I realized the damage that I had done. Eugene Robinson, a great friend and teammate, came up to me and told me privately, "Dude, a bunch of the coaches and defensive guys are questioning whether you're a stand-up guy or an excuse maker. They don't think you're owning up to your responsibility for that loss."
Their criticism wasn't aimed at my skills or performance, but at who I am—my character. As I wrote in my book, Facing the Blitz:
They thought that, in my optimism, I'd left the blame with the team instead of taking my part in it. Not only had I contributed to the loss, it seemed I wasn't being an accountable and trustworthy leader.
I felt misread and misjudged. I decided to talk privately to a couple of the defensive coaches who reportedly held these concerns. I told them I was my own worst critic and knew I'd fallen way short of what we needed to win. I knew I'd played a major role in our loss. … My team wanted to hear that I understood my role in our loss. My play wasn't the only reason we lost, but they needed to see that, first, I got it, and second, I was willing to take the heat, not simply leave it with my teammates and coaches.
The bottom-line issue isn't the results of your actions as much as what it says about your character. Whether it's me playing down my part in a loss or Tom Brady refusing to admit even an awareness of the team fudging on league rules, the ends still don't justify the means.
Another NFL great quarterback recently weighed in on the "DeflateGate" controversy. Brett Favre believes that even if Tom Brady broke the rules it wasn't really cheating because it didn't affect the outcome of the game. He was just doing what everyone else does—trying to get a competitive edge.
A common philosophy in the world, and in the world of professional sports is, "If you're not getting caught every once in a while, you're not working hard enough." It's ironic that someone as good as Brady would feel a need to do something that has so little impact on the outcome of the game to gain a competitive advantage.
Maybe an even bigger issue is what happens when you make it to the top of the heap, or the top of the league. You begin to believe the hype that everything depends on you. You may even begin to see yourself as a special case. You then justify actions that for most everyday people would be indefensible.
American society invites a pride and hubris in its successful people, and that is reflected in how Tom Brady and his agent have continued to oppose the NFL investigation. Pride and hubris aren't attractive to the public. Pride lets you think you can do things differently because you think you are special. It's easy to get sidetracked when you're in the spotlight and when you're trying to keep up expectations as the being the best. But Scripture brings us back to reality:
"Pride comes before destruction, and a haughty spirit before the fall."—Proverbs 16:18
"Also if anyone competes as an athlete, he does not win the prize unless he competes according to the rules."—2 Timothy 2:5
But then there's another scriptural reminder than keeps us from pointing the finger too much at others.
"Brothers, if anyone is caught in any transgression, you who are spiritual should restore him in a spirit of gentleness. Keep watch on yourself, lest you too be tempted."—Galatians 6:1
My teammate Eugene Robinson helped me to open my eyes and see the impact of my actions. Issues like "DeflateGate" help us check our own character to see if we are cutting corners, cheating, or taking ethical shortcuts. And it's a great opportunity to teach our kids valuable lessons about integrity and humility.
Accountability, Character, Cultural issues, Life lessons, Resisting sin, Spiritual life, Your work, Your work
be the man, being a man, ego, Eugene Robinson, familylife, godly man, humility, integrity, Jeff Kemp, Kansas City Chiefs, life lessons, man of God, man up, manhood, New England Patriots, NFL, Seattle Seahawks, step up, stepping up, taking responsibility, Tom Brady


The European growth will continue and will encourage the European economy and challenge for the longer term. Despite the taxes are high and the public sector is very large, on the short term European growth, as in the US, will be improving nicely. In the Eurozone the number of building permits is going up rapidly and the construction sector is recovering.
The 2016 wages in the Eurozone has increased and inflation increased by 2%. The nearest Eurozone Sentiment Indicator is at the highest level since the beginning of 2011 and means that this year we could expect GDP growth at around 2%.
The problems in Southern Europe are not over. It could be that markets will be too optimistic in the coming summer period. Rising interest rates and renewed political discussions with Greece could lead to a further deterioration of the situation in southern Europe. Besides we expect that the weak economies of Greece and Italy could become problems in the stock market.
So all by all we maintain invested generally in stocks with a small overweight for the coming months.


text:
\section{Introduction}
For decades, scientists all over the world have been endeavouring to develop clean, safe, and virtually limitless fusion energy. Over the last decade, it has become a clear realization that fusion power is a necessary solution regarding imminent climate change and energy security. Currently, ITER (International Thermonuclear Experimental Reactor) construction is underway \cite{iter}, and DEMO (DEMOstration Power Plant) is under the concept design \cite{demo}. Maintenance must be carried out periodically to guarantee the function of a fusion reactor, and abundant remote operations must be applied, which range from the handling of large structural payloads to assembling the small bolt components\cite{wilson1983remote}\cite{tada1995remote}\cite{buckingham2016remote}\cite{thomas2013demo}.

The Mascot robot\cite{hamilton2001development} equipped with twin arm manipulators, is teleoperated remotely by skilled operators to replace the small tiles of the "ITER-like Wall"\cite{matthews2011jet}. Such human-in-loop teleoperation has been operated for JET (Joint European Torus)\cite{jet} for decades and successfully upgraded JET in 2020. Nevertheless, the disadvantages of such human-in-loop manipulation are its low efficiency, highly skills dependency, a large number of well-trained operators, and long-term shut down of the reactor. For example, it took 17 months for engineers at JET to upgrade the inner wall of the reactor as well as the pipes, which involved handling 7000 assemblies. For the successful operation of the future DEMO reactor, a short shutdown period is critical. To achieve a short maintenance period, using autonomous assembly robots is a promising solution.

However, there are challenges in applying autonomous robotic solutions in a fusion reactor environment. To begin with, the inside of the vacuum vessel (VV) of a fusion reactor is a relatively unstructured environment; the background and components are all made of smooth-surfaced metal materials. The optical  sensor technology that could be applied there is limited. In addition, the inside vacuum vessel environment of the fusion reactor evolves over time due to the high temperature and large confinement forces.

To evaluate the feasibility of the autonomous assembly by robots in such fusion applications, we demonstrate a learning-based peg-in-hole assembly task by using a standard industrial robot, since it represents the most typical application in assemblies.

\subsection{Task Description}
In fusion applications, plasma-facing components require frequent maintenance and replacement. A large number of high-precision peg-in-hole assemblies are included in these tasks. Due to the particular environment inside the VV, the robot arm needs to pick up the peg from the toolbox, which is away from the hole, and then complete the assembly task, as shown in Figure\ref{fig:vv1}.

\begin{figure}[!h]
\centering

\subfigure[]{
\includegraphics[width=7.5cm]{figs/fig1_1}
}
\subfigure[]{

\includegraphics[width=7.5cm]{figs/fig1_2}

}
\centering
\caption{The peg-in-hole assembly task in fusion environment: (a) shows the Mascot twin arm system for grasping task and (b) shows the subsequent peg-in-hole assembly task.}
\label{fig:vv1}
\end{figure}

To accommodate a variety of pegs, the flexible 3-finger-gripper was chosen as the end-effector. At the same time, we needed to ensure that the system remained rigid throughout the assembly process, meaning that there should be no displacement between the peg and the gripper.

As a result, the peg-in-hole assembly task in this paper faces the following challenges:
\begin{itemize}
\item
Unlike some flexible assembly tasks, we are facing a rigid one. The tolerance in our application is less than 0.1mm, which means vision servo-based methods could hardly meet the requirement. Meanwhile, the backgrounds, the plasma-facing component, and the peg are both made of smooth-surfaced metal materials, so the optical sensors, such as RGB-D cameras, demonstrate significant errors in measuring these smooth surfaces.
\item
Due to errors in the peg grasping process, there is a certain randomness in the spatial position relationship between the gripper and the peg.
\item
Since the entire grasping system is very sensitive to collisions and contact, we need to be very gentle during the whole assembly process. Therefore, no strong collision and contact are allowed.
\end{itemize}

\subsection{Related Work}
Peg-in-hole assembly is an essential component of robotic research. It is a common task in the maintenance and manufacturing process. These methods can be divided into vision servo (VS)-based, contact model-based, learning from demonstration and Reinforcement Learning (RL)-based methods.
\subsubsection{VS-based methods}
VS-based methods have been implemented for a long time in automated peg-in-hole assembly tasks, including single\cite{jiang2020measurement} or multiple peg assembly. Researchers mainly use some traditional computer vision (CV) algorithms that rely on the extraction of the targets' geometric features to achieve the pose estimation, such as the Circular Hough Transform (CHT)\cite{duda1972use} and Canny algorithm\cite{canny1986computational}. Although researchers introduced a genetic algorithm-based method to eliminate the calibration error\cite{nagarajan2016vision}, even if using an eye-in-hand monocular camera and an eye-to-hand stereo camera to establish global and local observing systems respectively \cite{xu2022noncontact}, and adding markers such as a chessboard to help the pose estimation\cite{huang2017vision}, these works could still only achieve millimetre-level accuracy. Therefore, to improve the performance of the vision-based pose estimation system, vision markers have been set for both global and local observing systems\cite{jiang2020measurement}. However, these Vs-based methods are still limited by the low resolution of the cameras. Meanwhile, during most assembly processes, the contact between peg and hole is very common; monitoring the contact state to guide the robot to finish the assembly task is a natural idea.

\subsubsection{Contact model-based methods}
Contact model-based methods have been extensively and thoroughly researched in peg-in-hole assembly tasks. The concept of conventional contact model-based strategies relies on the contact model analysis and decomposes the peg-in-hole assembly into two steps: contact-state recognition and compliant control\cite{xu2019compare}. During the assembly task, the system has three kinds of states: searching, contact, and mated states. Since the contact state and the corresponding assembly strategy are the most complicated parts, the contact model-based methods are mainly focused on at this stage.
To complete the searching stage faster (the stage placing the peg center within the clearance region of the hole center), the spiral searching trajectory\cite{park2020compliant} has been introduced in some contact model-based methods. Meanwhile, some researchers have introduced vision sensors inspired by vision-based approaches for initial guidance\cite{zheng2017peg}.
Contact state recognition is designed to determine the boundaries of the contact state depending on the signals from different sensors. For example, monitoring the changes in force and torque due to the contact based on the F/T sensor is the most common way \cite{gai2021feature}\cite{jasim2017contact}. These studies usually distinguish between different contact situations by the different characteristics of force/torque signals, such as, one-point contact, two-point contact and three-point contact \cite{tang2016autonomous}. For single peg-in-hole assembly tasks, these three contact situations are enough, but for some dual peg-in-hole tasks, the jamming states \cite{zhang2018jamming} or the force data analyzing \cite{zhang2017force} would be more complicated, and the basic analytical model is no longer sufficient, so the contact state recognition is based on some statistical methods, such as the Hidden Markov Models (HMM)\cite{debus2004contact}, Support Vector Machine (SVM)\cite{jakovljevic2014fuzzy}, and the Gaussian Mixtures Model (GMM)\cite{jasim2014contact}\cite{song2021peg}.

The compliant control of the contact model-based methods could be separated into two parts: global planning and concrete realization. The global planning module's task is to generate appropriate force and moment value for the concrete realization module according to the geometrical constraints\cite{tang2016autonomous}\cite{zhang2018jamming} between the peg and hole, and for some of the sensor-less methods, an extra attractive region constraint \cite{su2017study}\cite{su2012sensor} would be introduced.
There are several ways to achieve concrete realization. Position control is the most common way—it has the advantages of high repeatable accuracy, but it is mainly used on some flexible assembly tasks\cite{jasim2014position}; while force control relies on the force and torque data from the joint by current detection. On most industrial robots, this data is inaccurate and has a significant impact on the actual performance of the force control. To handle the environmental uncertainties during the assembly process, some researchers implement the hybrid position/force control\cite{de2005sensorless}\cite{park2017compliance}, especially on rigid assembly tasks.

The contact model-based method could handle higher accuracy peg-in-hole assembly tasks in a structured environment than the VS-based methods. However, the model-based methods do not perform well in an uncertain environment, mainly because the model-based methods could hardly recognize the contact states well and were not generalizable enough for variable task environments. Therefore, some model-free methods such as learning from demonstration methods (LFD) and reinforcement learning (RL)-based methods are gradually gaining in popularity\cite{kober2013reinforcement}.

\subsubsection{LFD methods}

LFD methods are based on the idea that the robot follows human behavior to handle the assembly tasks. The general way is to teach the robot to perform industrial assembly tasks by defining key positions and motions using a control box called "teach pendant"\cite{b1}. The LFD methods contain three phases: the sensing phase, the encoding phase and the reproducing phase. The sensing phase is designed to record human motion trajectories with different sensors, and we can divide LFD methods into two different categories based on different sensing
system: kinesthetic demonstration\cite{calinon2007learning} and motion capture-based demonstration\cite{lin2014peg}. During the kinesthetic demonstration, the robot is guided by a human. The acceleration, velocity, and position of each joint would be recorded by the built-in joint sensors\cite{paxton2015incremental} in this process. Meanwhile, the motion capture-based methods depend on the motion capture system, which is mostly motion capture cameras\cite{tang2016teach}, and sometimes the vision mark\cite{kulic2012incremental} would also be introduced for better accuracy. Once the motion has been captured, the next stage is to encode the motion into a mathematical model. Three kinds of methodologies are widely used in this phase, namely hidden Markov models (HMMs)\cite{calinon2010probabilistic}\cite{niekum2013incremental}, Gaussian mixture models (GMMs)\cite{tang2015learning}\cite{kyrarini2019robot}, and dynamic movement primitives (DMPs)\cite{abu2014solving}. DMPs are time-invariant and robust to spatial perturbation, but there is a delay and pause of motion. HMMs could model both spatial and temporal motions well, and GMMs are more robust but are easily overfitted under fewer sample data situations. The reproducing phase is designed to reproduce the motions after the model is optimized. LFD methods perform well in most certainty environments, such as the factory assembly lines, but as online methods, when LFD methods are deployed in a new work environment, it is quite time-consuming.

\subsubsection{RL-based methods}

Along with the rapid development of deep learning, the RL-based approaches have become the most popular method in peg-in-hole assembly tasks. Unlike any other methods, RL-based methods do not split the assembly process into different stages or design different strategies for these stages, and this enables the robot to perform high-precision assembly tasks in multiple cases.
The RL-based methods have two categories: off-policy methods and on-policy methods\cite{luo2018deep}. The off-policy methods turn the assembly task into numbers of discrete state-action pairs, adjusting the value of each state-action pair in the learning process and optimizing the choice of action in each state to maximize the total value. When the number of state-action pairs is too large, it is very time- and space-consuming to store and maintain them. Then, the RL methods would combine the deep neural networks (DNNs) to parameterize the value functions, such as an off-policy method with a long short-term memory (LSTM)\cite{b1}. The on-policy methods add a scoring network for policy on the basis of off-policy methods, such as the deep deterministic policy gradient (DDPG)\cite{ren2018learning} methods or a model-driven deep deterministic policy gradient (MDDPG)\cite{b2}.

From the sensor system, F/T sensors are still the most popular used, while some researchers start to combining mulit-sensors together, such likes the RGB-D sensor with the F/T sensor\cite{wang2021alignment}, but this research has just been carried out in a simulation environment. Some researchers also combine the RGB-D camera with the joint information and achieve the peg-in-hole assembly task in a real environment, but it is a flexible assembly task and only at centimeter-level accuracy\cite{bogunowicz2020sim2real}.
Since the training of RL-based methods could take millions of steps, it would be time-consuming in reality due to the limitations of the physical world, so most training process are carried out in a simulation environment\cite{hou2020fuzzy}, and the model would be deployed and further optimized in reality after been optimized in simulation. However, it is not easy to overcome the problem of sim-real gaps\cite{stan2020reinforcement}.

\begin{figure}[!b]
\centering

\subfigure[]{
\includegraphics[width=3.9cm]{figs/fig2_1.JPG}
}
\subfigure[]{

\includegraphics[width=3.9cm]{figs/fig2_2.JPG}

}
\subfigure[]{
\includegraphics[width=3.9cm]{figs/fig2_3.JPG}
}
\subfigure[]{

\includegraphics[width=3.9cm]{figs/fig2_4.JPG}

}
\centering
\caption{The complete  task of the assembly task in Fusion application. Pictures (a) to (d) show the identification phase, the grasping phase, the approaching phase, and the assembly phase.}
\label{fig:vv00}
\end{figure}

\subsection{Contribution}
Our complete task is shown in Figure 2. To simulate the inner environment of the VV, we have the following setups in our training and experiment scenario:
\begin{itemize}
\item
All the components and background in the training and experiment are made of metal materials.
\item
The peg-in-hole assembly task starts immediately after the robotic gripping phase, which is in line with the actual situation in the VV.
\end{itemize}

The first three phases have been completed in the previous paper \cite{Yin2022}. In this paper, we are focusing on the most challenging phase, the assembly phase. To overcome the previously mentioned difficulties, we propose a deep reinforcement learning (DRL) method that combines the RGD image from the camera with the force/torque data from the F/T sensor. Our method is quite similar to \cite{bogunowicz2020sim2real} and \cite{wang2021alignment}. However, first, we accomplish this task in reality instead of just in the simulation environment; and second, what we are facing is a rigid assembly task instead of a flexible one. So we are facing many more difficulties in our task. The method we proposed in this paper has the following advantages:

\begin{itemize}
\item
We built a DRL network that can accept both the RGB image from the camera and the force/torque data from the F/T sensor, and this makes the method insensitive to the initial positional relationship between the peg and the hole. The RGB data could help drive the robot arm to make the peg center align with the hole center faster. In other words, it accelerates the searching process.
\item
We clearly know that the RGB camera's accuracy could not meet this task's requirement, so we also introduced the force/torque data from the F/T sensor to our DRL network. It plays a crucial role during the insertion process.
\item
Due to the strict anti-collision requirement and taking into account control accuracy, we developed a hybrid position/force control strategy that contains a three-layer cushioning structure (TCS) to make sure there will not be any violent collisions or contact during the whole process.
\end{itemize}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=17.5cm]{figs/fig3.png}
    \caption{The basic structure of our method.}
    \label{fig:3}
\end{figure*}

\section{Algorithm}
\label{section:2}

Most previous works on peg-in-hole assembly tasks are only based on the force data from the F/T sensor. This is only feasible if the ground truth of the hole center's position is known. AS in the work from \cite{b1}, random error is added within the tolerance range to the ground truth to simulate position error. However, we do not know the ground truth of the hole center position in our case. We could only get a rough result from the RGB-D camera, which has an error of over 3mm. So we introduced the image from an RGB camera to our deep reinforcement learning network and then combined it with the force and torque data from the F/T sensor, thus allowing the robotic arm to finish the task as a human worker through a hand-eye combination. The structure of our approach is shown in Figure \ref{fig:3}, where UR\_RTDE\footnote{\underline{https://sdurobotics.gitlab.io/ur\_rtde/index.html}} is an interface package that could receive status information from the robot and send controlling commands to the UR robot, and TCP stands for tool center position. During the assembly process, before each movement, we will judge the current TCP pose to see whether we should quit the current episode (the termination conditions will be explained later). If the answer is yes, the robot will return to the starting position and move into the next training episode; otherwise, the TCP pose will be sent into the RL network together with the data from the camera and the F/T sensor. Then, the RL network would output an action, and this action would transform into a control signal through the UR\_RTDE module and drive the robot arm. This is one complete step in the circle.

\subsection{N-step Q Learning}
N-step Q learning (\cite{mnih2016asynchronous}) is a variant of Q learning. This means it is also an off-policy method. We chose N-step Q learning as our RL model because the off-policy methods would converge more quickly and allow us to iterate more frequently to improve our network structure.
\subsubsection{Problem Statement}
In our case, the hole is fixed to the metal table, and the end-effector, the 3-finger gripper, needs to grasp the peg from somewhere on the table and approach the hole; these works have been described in the previous paper.
The situation now is that the starting position of the center point at the bottom of the peg (CBP) is about 3mm away, on the X-Y plane, from the hole center, because of the data error from the RGB-D camera.

Most RL-based peg-in-hole methods divide the task into the search and insert phases and take different artificially designed strategies to handle these two phases.

In this work, the target is to develop an agent that learns how to finish the task. This process could be fomulated as a Markov decision process (MDP). At each time step $t$, the RL agent observes a state $\mathbf{S}_t$ from the environment, and chooses an action $a_k$ according to the Q-value $Q(\mathbf{S}_t,\mathcal{A})$, where $\mathcal{A} = {a_1,a_2,...,a_n}$ and $a_k\in \mathcal{A} $, then obtains reward $\mathcal{R}_{t+1}$ and moves into the next time state $\mathbf{S}_{t+1}$.

In our case, $\mathbf{S}$ is defined as:
\begin{equation}
    \begin{split}
        & \mathbf{P} = \left \{P_x,P_y,P_z,R_x,R_y,R_z\right \} \\
        & \mathbf{F} = \left \{F_x,F_y,F_z, M_x,M_y\right \} \\
        & \mathbf{S} =\left \{I,\mathbf{P},\mathbf{F}\right \}
    \end{split}
\end{equation}

where $\left \{P_x,P_y,P_z\right \}$ in $\mathbf{P}$ are the 3D spatial coordinates in the robot arm base coordinate system, and $\left \{R_x,R_y,R_z\right \}$ in $\mathbf{P}$ represent the rotation of the peg relative to the robot arm base coordinate system. $\mathbf{F}$ is the data from the F/T sensor, where $\left \{F_x,F_y,F_z\right \}$ are the forces data and $\left \{M_x,M_y\right \}$ are the moment data; it is essential to note that the F/T sensor is installed close to the end-effector, so the subscript $x,y,z$ in $\mathbf{F}$ represents the axis of the end-effector's coordinate system. The $I$ is the grayscale image from the camera, and $\mathbf{S}$ is the set of $I$, $\mathbf{P}$, and $\mathbf{F}$.

The $\mathbf{a}$ is defined as:
\begin{equation}
    \begin{split}
        & \mathbf{a} =\left \{a_{t}^{x},a_{t}^{y},a_{t}^{z},a_{r}^{x},a_{r}^{y}\right \}
    \end{split}
\end{equation}

where $a_t$ is the translation actions and $a_r$ is the rotation actions, and $x,y,z$ in the superscript stands for the axis of the coordinate system. These actions are based on the peg's coordinate system.

\begin{figure}[!h]
\centering

\subfigure[]{
\includegraphics[width=3.5cm]{figs/fig4_1}
}
\subfigure[]{

\includegraphics[width=3.5cm]{figs/fig4_2}

}
\centering
\caption{The searching region of the peg, (a) is the cross-section of side view, (b) is the top view.}
\label{fig:4}
\end{figure}

\subsubsection{Termination Conditions and Basic Reward Setting}
In this work, we designed several termination condition as follows:
\begin{itemize}
\item
The robot successfully completed the assembly task; in our case this means $P_z$ in the current pose should be 1cm lower than the $P_z$ in the starting pose, as shown in Figure~\ref{fig:4}(a).
\item
The distance from CBP to the center point of the hole (CPH) is over 1cm. Then the agent would quit the current episode and return to starting pose.
\item
When the agent's moving step is above the maximum number $Step_{max}=5000$, we will consider this a failure to complete the task. The agent will end the episode and return to the starting point.
\end{itemize}

For each step, we set the general reward as $r_{gen}= -0.001$. The reason for setting the general reward to negative is that the robot is expected to complete the task with the fewest steps. Meanwhile, for each step, we also introduce an extra reward that corresponds to the value of $P_z$ (in meters). Its formula is:
\begin{equation}
    r_{ext} = 75 \times (P_{z}^{t} - P_{z}^{t+1} )
\end{equation}

where $P_{z}^{t}$ and $P_{z}^{t+1}$ represent the $P_z$ value at the current the step and the next step. Finally, if the agent finishes the task, it will get a reward for its success:
\begin{equation}
    r_{suc} = 1 - \frac{Step_f}{Step_{max}}
\end{equation}

where $Step_f$ is the number of steps taken when the agent finishes the task.
Meanwhile, in order to avoid, as far as possible, any violent contact or collision above a threshold during the assembly process, we add a punishment $r_{pun}$ each time such contact or collision happens.
So the final reward $\mathcal{R}$ is:
\begin{equation}
    \mathcal{R}= r_{gen} + r_{ext} + r_{suc} + r_{pun}
\end{equation}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=17cm]{figs/fig5}
    \caption{The basic structure of the Multi-Sensor-Based  Dueling Deep Q-learning Network(Dueling-DQN).}
    \label{fig:5}
\end{figure*}

\subsubsection{Search Strategy and Q-value Updating}
At time step $t$, the agent chose the action according to the Q-value of all actions $Q(\mathbf{S}_t,\mathcal{A})$; in our case, we use the $\epsilon-greedy$ strategy.
\begin{equation}
a = \left\{\begin{matrix}
   {\underset{a\in\mathcal{A}}{\mathrm{argmax}}}Q(\mathbf{S}_t,\mathcal{A}) & \mathrm{with\ probalility\ } 1-\epsilon  \\
   \mathrm{randomly\ chose\ } a_k \in \mathcal{A} & \mathrm{with\ probalility\ }\  \epsilon
\end{matrix}\right.
\end{equation}

where $\epsilon$ is a manual setting hyperparameter that could control the search of the agent.

The Q-value update follows the equation:
\begin{equation}
\begin{split}
    Q(S_t,a) =
      &Q(S_t,a) + \alpha [\mathcal{R}_{t+1} + \lambda\mathcal{R}_{t+2} + ... \\
      &+\lambda^{k-1}\mathrm{argmax}Q(S_{t+k},\mathcal{A})- Q(S_t,a)]
\end{split}
\label{formula:7}
\end{equation}
where $\alpha$ is the learning rate (LR), $\lambda$ is the discount factor, and $k$ is the steps for the Q-learning network to look forward.

\subsection{Multi-Sensor-Based Dueling Deep Q-learning Network}
For simple reinforcement learning tasks, all Q-values $Q(\mathbf{S},\mathcal{A})$ can be stored in a Q-table and reinforcement learning can be achieved by querying and updating this Q-table. However, when the states are too complicated, it is hugely space- and time-consuming to maintain such a huge Q-table. Therefore, we need to build a neural network whose input is a state $\mathbf{S}_t$, and output is the Q-value of all actions under this state $Q(\mathbf{S}_t,\mathcal{A})$. The neural network achieves the same functionality as the Q-table, but is more compact and efficient.

In this paper, the structure of the proposed Multi-Sensor-Based Dueling Deep Q-learning Network (MS-Dueling-DQN) is shown in Figure \ref{fig:5}, the grayscale image $I$ from the camera will be inputted into a CNN branch after a few rounds of convolution, and the feature map will be flattened as a Fully Connected Layer (FCL). Then it will be concatenated with $\mathbf{P},\mathbf{F}$, which come from the F/T sensor and the UR\_RTDE.

\subsubsection{Calculation of Q-value}
Unlike normal DQN, Dueling-DQN has an extra branch structure at the output end. Before the output layer, the Multi-layer Perceptron (MLP) structure is split into two branches: one is called the Advantage Function $Q_{Adv}$ and the other is called Value Function $Q_{Val}$, while the $\oplus$ operator in Figure 11 stands for the calculation of the prediction Q-value $Q_{pre}(S_t,\mathcal{A})$:
\begin{equation}
\begin{split}
    Q_{pre}(S_t,\mathcal{A} )= Q_{Val} + Q_{Adv} - Mean(Q_{Adv})
\end{split}
\end{equation}

where the $Mean(Q_{Adv})$ is the mean value of $Q_{Adv}$. As designed, in all kinds of Q learning, the Q-value is the value of a state-action pair, but in Dueling-DQN, the Q-value is considered as the sum of the value of the state itself, which is $Q_{Val}$ and the value of the different actions in this state, which is $Q_{Adv} - Mean(Q_{Adv})$.

\begin{figure}[!h]
    \centering
    \includegraphics[width=8cm]{figs/fig6.png}
    \caption{The actions in our case: translation actions and rotation actions. These actions are all referenced to the peg coordinate system, whose origin point is at CBP. And during the rotation actions, the position of CBP is fixed, and the rotation is described in form of Euler Angles.}
    \label{fig:6}
\end{figure}

\subsubsection{Output of the Network}
In a deep reinforcement learning network, each node of the output layer corresponds to an action. In our case, the actions happen in five dimensions: translation actions on axes $x, y, z$, and the rotation actions about the axis $x, y$, as shown in Figure \ref{fig:6}. All actions have positive and negative directions, except for the translation action on the z-axis. Meanwhile, we designed three different step lengths for each action. For the translation actions, the step lengths are $\{0.1, 0.5, 1\}$ in millimeters, and for the rotation actions the step lengths are $\{0.05,0.08,0.1\}$ in degrees. Therefore, there are 27 nodes in the output layer, as shown in Figure \ref{fig:5}.

It should be noted that we chose a hybrid position/force control strategy for this task; this is why the translation actions are represented by $\{P_x, P_y, P_z \}$ in Figure \ref{fig:6}. The control strategy is fully explained in detail below.

\subsubsection{Loss Function and Optimizer}
The loss function is defined as:
\begin{equation}
\begin{split}
    & L = \frac{1}{2} {(Q_{tar}(S_t,a) - Q_{pre}(S_t,a) )} ^{2} \\
    & Q_{pre}(S_t,a) =  {\mathrm{argmax}}Q_{pre}(S_t,\mathcal{A} )
\end{split}
\label{formula:9}
\end{equation}

where $Q_{pre}(S_t,\mathcal{A})$ is outputted by the network, $Q_{tar}(S_t,a)$ could be calculated as:

\begin{equation}
\begin{split}
    Q_{tar}(S_t,a) &=  Q_{pre}(S_t,a) + \alpha (\mathcal{R}_{t+1} + \lambda\mathcal{R}_{t+2} + ... \\
      & +\lambda^{k-1}\mathrm{argmax}Q_{pre}(S_{t+k},\mathcal{A})- Q_{pre}(S_t,a) )
\end{split}
\label{formula:10}
\end{equation}

where the $Q_{pre}(S_{t+k},\mathcal{A})$ is also outputted by the network just like $Q_{pre}(S_t,\mathcal{A})$.

For the DRL network, training is the process of getting the network to fit Formula \ref{formula:7} better.
The optimizer in our network is Nesterov-accelerated Adaptive Moment Estimation (Nadam) \cite{Nadam}.

\subsubsection{Parameter Setting}
Of all the parameters, the parameter $\epsilon$ of the $\epsilon-greedy$ strategy is the one of the most important and interesting. The $\epsilon$ would decay with the training steps, as per the formula below.
\begin{equation}
\begin{split}
    \epsilon = \epsilon_{min} + (\epsilon_{max}-\epsilon_{min}) \times(1-\frac{Steps_{accu}}{Indx_{decay}})
\end{split}
\end{equation}

where the $\epsilon_{min}=0.1$, $\epsilon_{max}=1$, the $Indx_{decay}=30000$ and the $Steps_{accu}$ indicates the number of moving steps accumulated in all episodes during the current training. From the formula, we can find that $\epsilon$ is close to 1, which means the robot is essentially moving at random. This is because, at the very beginning of the training, the robot had not explored the entire environment, and the states it has experienced are still very limited, so the lessons it learns at this point are not reliable, and we need to increase the randomness of movement to allow the robot to explore more unknown states. However, since our training and experiment are carried out in a real environment and are very time-consuming, several interruptions are inevitable. At this point, $Steps_{accu}$ will be reset to 0. Therefore, we will set the $\epsilon$ to a fixed small value if the robot has undergone sufficient training and performed well at this point.

Some other important parameters are shown in the table blow.

\begin{table}[!h]
\caption{Some important parameter in our algorithm}\label{table:1}
\begin{threeparttable}
\begin{tabular}{ccl}
\bottomrule
Symbol & Value & \multicolumn{1}{c}{Description} \\ \midrule
    $\lambda$        &      $0.95  $         &  The discount factor.                \\
    $\alpha$        &       $1\times10^{-4}$        &      The learning rate.       \\
    $k$      &    $ 4 $           & The looking forward steps.           \\
    $Step_{max}$       &      $5000 $        & \begin{tabular}[c]{@{}l@{}}The maximum number of steps\\ in each episode.\end{tabular}      \\
    Batch Size       &      $32$         &  Batch Size during the training.   \\
    Buffer Size       &      $1\times10^{5}$         &  The size of the buffer zone.   \\
    Update Rate      &      $1\times10^{3}$    & \begin{tabular}[c]{@{}l@{}}The number of the steps between \\ the target network's updating.\end{tabular}                     \\ \bottomrule

\end{tabular}
\end{threeparttable}
\end{table}

\subsection{Control Strategy}
This subsection will thoroughly explain the hybrid position/force control strategy and the three-layer cushioning structure designed to prevent violent collision or contact in this task.

\subsubsection{Hybrid Position/Force Control Strategy}
As mentioned, we can control the TPC pose of the robot arm to move in the 3D Cartesian space linearly using the UR\_RTDE package. To ensure that the CBP remains constant during rotation actions, we need to get the correct TCP pose.

As designed, the angle would only change in one dimension during each rotation action. Assuming the rotation action is a rotation of $\Delta\gamma$ degrees around the X-axis, since the rotation sequences is following the roll-pitch-yaw (R-P-Y) angles. Then the TCP $P_{new}$ could be calculated as:

\begin{equation}
\begin{split}
&M_{old}  = R_{z}(\alpha)R_{y}(\beta)R_{x}(\gamma) \\
        & = \begin{bmatrix}
 C_{\alpha}  & -S_{\alpha}  & 0 \\
 S_{\alpha} & C_{\alpha}  & 0 \\
 0 & 0 & 1
\end{bmatrix}
 \begin{bmatrix}
 C_{\beta}   & 0  & S_{\beta}   \\
 0 & 1  & 0 \\
 -S_{\beta} & 0 & C_{\beta}
\end{bmatrix}
 \begin{bmatrix}
 1   & 0  & 0  \\
 0 & C_{\gamma } & -S_{\gamma } \\
 0 & S_{\gamma } & C_{\gamma }
\end{bmatrix} \\
\\
&M_{new} = R_{z}(\alpha)R_{y}(\beta)R_{x}(\gamma +\Delta\gamma) \\
        & = \begin{bmatrix}
 C_{\alpha}  & -S_{\alpha}  & 0 \\
 S_{\alpha} & C_{\alpha}  & 0 \\
 0 & 0 & 1
\end{bmatrix}
 \begin{bmatrix}
 C_{\beta}   & 0  & S_{\beta}   \\
 0 & 1  & 0 \\
 -S_{\beta} & 0 & C_{\beta}
\end{bmatrix}
 \begin{bmatrix}
 1   & 0  & 0  \\
 0 & C_{\gamma +\Delta\gamma} & -S_{\gamma +\Delta\gamma} \\
 0 & S_{\gamma +\Delta\gamma} & C_{\gamma +\Delta\gamma}
\end{bmatrix} \\
\\
&P_{new} = M_{old}{\begin{bmatrix}
 0 & 0 & z_{peg}
\end{bmatrix}
} ^ T + P_{old} - M_{new}{\begin{bmatrix}
 0 & 0 & z_{peg}
\end{bmatrix}
}^ T
\end{split}
\end{equation}

where $\gamma,\beta,\alpha$ are the R-P-Y angles and the $P_{old}$ is the original TCP, which they could all easily get from the UR\_RTDE. $C_x, S_x$ are abbreviations for $Cos x, Sin x$, and so are the other similar symbols. The $z_{peg}$ is the Z-axis coordinates of the CBP in the end-effector coordinate system.

The translation actions are also referenced to the peg coordinate system, and it could be easily implemented through forward kinematics.

The core of our control strategy is to avoid violent contact and collisions. Therefore, we installed an F/T sensor between the last wrist of the robot arm and the end-effector; through it, we can monitor the feedback force and torque of contact during each action. Meanwhile, we established a three-layer cushioning structure (TCS) to handle different contact situations.

\subsubsection{Three-layer Cushioning Structure}
The basic structure of TCS is shown in Figure \ref{fig:7}. The TCS monitors the data from the F/T sensors in real-time during every movement of the robot arm; based on the data from the F/T sensor, we classify the current state of the robot arm as safe, warning, or dangerous. Throughout the process, we maintain two global variables, which are \textbf{Last State} and \textbf{Latest Safe Pose}. Based on these states, the TCS would make the decision according to the logic in Table \ref{table:2} to avoid violent contact and collisions.

\begin{figure}[!h]
    \centering
    \includegraphics[width=8cm]{figs/fig7}
    \caption{The structure of TCS, where F and M indicate the force and moment (torque). The green zone is the safe state, the orange zone is the warning state, and the red zone is the dangerous state. The TCS would monitor the force and torque data and make the decision in real-time to avoid violent contact and collisions from happening. After each movement, TCS will update the \textbf{Last State} and \textbf{Latest Safe Pose}.}
    \label{fig:7}
\end{figure}

\begin{table*}[!h]
\caption{The decision logic of TCS}\label{table:2}
\begin{threeparttable}
\begin{tabular}{cclc}
\bottomrule
Last State & Current State & \multicolumn{1}{c}{Decision} & Punishing Reward $r_{pun}$ \\ \midrule
    {\color{green!}Safe }        &      {\color{green!}Safe }          &  Do nothing.            &       0     \\
    {\color{green!}Safe }        &      {\color{orange!}Waring }         &       Stop the movement and stay at current pose. &           0          \\
    {\color{orange!}Waring }       &     {\color{green!}Safe }            &  Do nothing.      &        0          \\
    {\color{orange!}Waring }       &      {\color{orange!}Waring }         & Do nothing and hope the robot will save itself.         &       0          \\
    {\color{orange!}Waring }       &      {\color{red!}Dangerous }         &  Cancel the movement and move back to the \textbf{Latest Safe Pose}.
                &      -0.003      \\
    {\color{red!}Dangerous }        &      {\color{red!}Dangerous }    & When this appears ten times in a row, quit this episode.\tnote{1}
                 &       -0.01     \\ \bottomrule

\end{tabular}
\begin{tablenotes}
        \footnotesize
        \item[1] This is a rare but occurring situation. It means that the robot is trapped in some peculiar pose and cannot rescue itself through other measures in the TCS, at which point we consider the task a failure. The robot will quit this episode, return to the starting position and start the next episode.
      \end{tablenotes}
\end{threeparttable}
\end{table*}

\section{Training and Experiment}
Unlike some other DRL tasks, we implement the training and experiment on the agent in reality instead of in a simulation environment. This has the advantage of not having to deal with the sim-real gap problem except for the fact that it is time-consuming.
\begin{figure}[!h]
    \centering
    \includegraphics[width=8cm]{figs/fig8.png}
    \caption{The platform in our task.}
    \label{fig:8}
\end{figure}

\subsection{Experiment platform}
The platform in our experiment contains a host computer (HC), multiple sensors, and robot arm. The training and experiment are carried out on an HC with an Nvidia RTX 2080Ti GPU, Intel i7-10700k CPU, and 64GB of RAM. The robot arm and multiple sensors are as shown in Figure \ref{fig:8}. The robot arm and end-effector communicates with the HC through the TCP/IP protocol, the F/T sensor is connected to the HC through the serial port, and the RGB-D camera is connected to the HC through the USB3.0 port.

\subsection{Training process}
The training for this task took over 2000 hours. We have carefully tuned the various hyperparameters of the DRL model during the training. In this subsection, we will retrace the vital points of the training process that allow us to improve the model.

\subsubsection{Data Pre-processing}
We will restore the data from F/T sensor at the beginning of the training as $\mathbf{F}_{star}$. Then all the input F/T data $\mathbf{F}_{nor}$ are calculated as:
\begin{equation}
\begin{split}
    \mathbf{F}_{nor} = \mathbf{F} - \mathbf{F}_{star}
\end{split}
\label{formula:12}
\end{equation}

where $\mathbf{F}$ is the raw data from the F/T sensor.

As shown in Figure 2 (d), we used an eye-in-hand camera for this task, while the position relationship between the peg and the robot arm is fixed. Therefore, We were able to select four fixed points in the image and use the rectangular area formed by these four points as the region of interest (ROI). Then the cropped RGB image was turned to a grayscale image and was normalized as:
\begin{equation}
\begin{split}
    I_{nor}(u,v) =  1 - \frac{I_{gray}(u,v)}{256}
\end{split}
\label{formula:13}
\end{equation}

Meanwhile, the camera we chose is an RGB-D camera that can supply 3D data. In the previous paper, we proposed an instance segmentation method; depending on this, we could get the CPH approximate location $\mathbf{P}_{hc}= \{P^{'}_x,P^{'}_y,P^{'}_z\}$ which has an error of about 3 to 4 mm. Therefore, the input pose data $\mathbf{P}_{nor}$ could be calculated as:
\begin{equation}
\begin{split}
    & \mathbf{P}_t = \{P_x, P_y, P_z\} - \{P^{'}_x,P^{'}_y,P^{'}_z\} \\
    & \mathbf{P}_r = \{R_x, R_y, R_z\}   \\
    & \mathbf{P}_{nor} = \{\mathbf{P}_t, \mathbf{P}_r\}
\end{split}
\label{formula:14}
\end{equation}

where $P_x, P_y, P_z,R_x, R_y, R_z$ are the data get from \\ UR\_RTDE.

\subsubsection{From One-step to N-step}
Even though we decided to use the N-step Q-learning from the every beginning, we still compared the performance of the one-step and N-step Q-learning in this task.

\begin{figure}[!h]
\centering

\subfigure[]{
\includegraphics[width=3.8cm]{figs/fig9_1}
}
\subfigure[]{

\includegraphics[width=3.8cm]{figs/fig9_2}

}
\centering
\caption{The episode reward (ER) and loss comparison between the N-step and one step DRL models. Please note that the data in (a) is not recorded from the very beginning of training but when the model is relatively stable after some training.}
\label{fig:9}
\end{figure}

From Figure \ref{fig:9}, we can find that compared with the N-step model, the one-step model has both lower loss and episode reward during the training. It means that one-step model is more easily to converge, but the N-step model shows better performance in the course of carrying out the task. In other words, the upper bound on the best model for N-step is higher, but the network could not quickly converge to the best model.

Obviously, when the network can converge well to the ideal model, the upper limit of the model is more important, but a large step number could make the network hard to fit the ideal model and has a bad influence on the final performance, so the chosen step number should not be too large or too small. After careful comparison, we settled on the looking forward steps number as four, which could reach peak performance for the task.

\subsubsection{Reward Setting}
Choosing the appropriate reward $\mathcal{R}$ is the key to designing a successful reinforcement learning model. Researchers could influence the agent's behaviour by setting different reward value.

For example, in our case, the general reward $r_{gen}$ is settled as a negative value that could drive the agent to finish the task as soon as possible. Moreover, for the punishing reward $r_{pun}$, when the punishment is too light, the robot would be too aggressive and thus are frequently involved in violent collisions and contact. Conversely, if the punishment is too heavy, the robot will become very conservative or even refuse any contact, and finishing the assembly task will not be possible.
So we spent a lot of time tuning $r_{pun} = -0.003$, which keeps a balance between efficiency and safety.

\subsubsection{Solving the Blind Spot Problem}

During the training, to prevent overfitting, we randomly selected the initial pose of each episode within 4mm of the ground truth position of the CPH. However, since we were using an eye-in-hand camera, sometimes the peg would obscure the hole and create a blind spot for the camera, as shown in the left part of Figure \ref{fig:10}.
\begin{figure}[!h]
    \centering
    \includegraphics[width=8cm]{figs/fig10}
    \caption{This is the top view of the camera's blind spot problem. It also contains the process of solving this problem. The grey translucent circles are the peg, and orange circles are the hole. The BCS indicates the base coordinate system, and the EECS indicates the end-effector coordinate system.}
    \label{fig:10}
\end{figure}

As shown in Figure \ref{fig:10}, we would, of course, hope the agent would understand that it should move in the negative direction of the X-axis of the BCS in this case, as a human would, but this is too idealistic. A simpler but efficient solution is to rotate the last wrist of the robot arm 90° clockwise, so we can easily eliminate the blind spot. Meanwhile, the rotation does not influence the performance of the network. This is because all the actions are made under the EECS. Just like in the left part of Figure \ref{fig:10}, the network should make the movement decision in the positive direction of the X-axis of the old EECS, and after the rotation, according to the image data, the hole is on the right side of the peg, so according to the logic, the network should make the movement decision in a positive direction of the Y-axis of the new EECS. However, these two movements are exactly the same under the BCS, so the rotation would not disrupt the network's decision logic.

Therefore, we added an automatic angle selection module (AASM). The primary function of the AASM is to rotate the last wrist counterclockwise and clockwise by 45 degrees, twice each. Then, the pose corresponding to the grayscale image with the most black pixels is selected as the initial pose.

\subsection{Experiment}
During the experiment, we will compare the agent's performance on task finishing and violent contact avoidance before and after the entire training. Some performances are shown in the attached video (\url{https://youtu.be/Vna5jrJm85I}).

\begin{figure}[!h]
\centering

\subfigure[]{
\includegraphics[width=3.9cm]{figs/fig11_1}
}
\subfigure[]{

\includegraphics[width=3.9cm]{figs/fig11_2}

}
\centering
\caption{The ER and completion steps comparisons. Red lines: fixed initial position with well-trained agent (FT). Blue lines: random initial position with well-trained agent (RT). Green lines:  fixed initial position with untrained agent (FNT).}
\label{fig:11}
\end{figure}
\subsubsection{Performance on Assembly Task}

\begin{table*}[h]
\caption{Comparison of performance on assembly task}\label{table:3}
\begin{threeparttable}
\begin{tabular}{lcccc}
\bottomrule
     & \textbf{Average ER} &  \textbf{No. of successes/No. of attempts}     & \textbf{Success rate}& \textbf{Average  completion steps}\\ \midrule
FNT & -0.4325  &  0/24  &  0\%    &  - \\
RT & 1.5071   &  23/24  &  96\%    &  42 \\
FT & 1.7120   &  24/24  &  100\%    & 19 \\ \bottomrule
\end{tabular}
\end{threeparttable}
\end{table*}

The performances are shown in Figure \ref{fig:11}, after organizing the data therein we obtain Table~\ref{table:3}. In Figure \ref{fig:11} (a), we compared the ER value of the FT, RT, and FNT. For the RT, there was one low ER value, which means that the agent failed to finish the assembly task in this episode. In the first 13 episodes, the ER of the FNT was quite stable and with low value; however, after the 13th episode, the ER values started to oscillate. This is because the network will only start updating the parameters by gradient descent if the number of agent's movement steps exceed a specific value. For those episodes with ER values under 0, the fewer steps the agent moves when it fails, the higher the ER value are. This could explain why the highest value of FNT is even higher than the minimum value of RT in Figure \ref{fig:11} (a). Meanwhile, for those episodes with ER values greater than 1, the fewer steps the agent moves before its success, the higher the ER value is.
Obviously, the well-trained agent's performance with the fixed initial position is best, which never failed and achieved the relative highest average ER value, which is $1.7120$. The well-trained agent failed once with the random initial position, and the average ER value was a litter bit lower. As for the agent without training, it failed in all the episodes.

In Figure \ref{fig:11} (b), we compare the completion steps of the FT and RT.
It is important to know that we only counted the agent's completion step if it has successfully completed the assembly task, since the FNT could hardly finish the assembly task, and we do not have completion step statistics of the FNT in Figure \ref{fig:11} (b). The data shows that the FT has fewer and more stable completion steps then RT, which is consistent with Figure \ref{fig:11} (a). There is also a linear positive correlation between the completion steps and the completion time, with one step taking around one second.

From these analyses, we can draw the following conclusions: the well-trained agent could handle both the FT and RT situations well. Although there is once failure in RT situations, the agent could come out of the failure immediately and complete the following episode. Moreover, compared with the agent without training, the well-trained agent shows great advantages.

\subsubsection{Performance on Avoiding Violent Collision or Contact}
To verify the agent's performance on avoiding violent collision or contact, the data from the F/T sensor during the assembly task is shown in Figure \ref{fig:12}.
\begin{figure}[!h]
    \centering
    \includegraphics[width=8.5cm]{figs/fig12}
    \caption{The data from the F/T sensor. Blue lines: the whole assembly process of the agent with training (WPT). Red lines: the searching process of the agent without training (SPNT). Green lines: the insert process of the agent without training (IPNT).}
    \label{fig:12}
\end{figure}

In Figure \ref{fig:12}, we compare the performances of the agent with and without training; it should be noted that we separate the assembly process of the agent without training into two phases: the searching process—the agent places the peg center within the clearance region of the hole center—and the insert process—the agent pushes the peg to the desired position. The reason for this is that it is difficult for the less-well–trained agent to complete the searching process automatically and thus start the insert process. We need to align the peg center with the hole center manually so that the agent can start the insert process; meanwhile, the data shows a big difference between these two phases, so we think it fairer and more objective to separate and compare the performances in these two phases.

From Figure \ref{fig:12}, we could find that the SPNT data looks even better than the WPT data except for the $F_{z}$. This is because the agent could move freely and did not meet any obstacles except for the Z-axis downward movement during the searching process. However, the IPNT data shows the state of the agent is basically in the warning zone and often comes to the edge of a dangerous situation, and the $F_{z}$ value has come to $-10 N$ several times and the $M_{z}$ has even come to $-1.78 Nm$, which is already in a dangerous situation according to the standard of TCS. Moreover, there is every reason to believe that if the TCS had not intervened in time, the agent would have been put into dangerous situations more frequently. On the other hand, from the WPT data, we can see the agent is attempting to remain in the safety zone during the whole assembly task and rarely comes into the warning zone.

Therefore, through these analyses, we can state that the agent would perform more wisely to avoid the violent collision or contact after being well trained. Meanwhile, the TCS also works perfectly.

\section{Conclusion}
In this paper, we propose an N-step Dueling-DQN method to fuse data from multiple sensors through a network to enable robotic arms to handle the peg-in-hole assembly task, in a similar way to human hand-eye collaboration. Even though the peg-in-hole task in this paper is full of challenges—a rigid assembly task, high perception requirement, sensitivity to contact forces, randomness and uncertainty of initial relative position relationships between the peg and hole—the experiment, in reality, shows our method could successfully put an end to such kinds of challenging tasks and meet all the strict requirements; meanwhile, the TCS works well in preventing violent collision or contact, and the blind spot problem is also solved perfectly. All these designs make our method more general.

However, we also have some regrets. To ensure the feasibility of our approach in realistic environments, we carry out all of our training and experiments in realistic environments, which are really time-consuming. So in our future works, we will follow the most popular way: the first step is to build a virtual environment in which we perform a preliminary screening of the algorithms, especially the network structure; the selected algorithm is then deployed to the realistic environment, and we will focus on solving the sim-to-real problem.
\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


Airbus and Rolls Royce have signed a deal with South East Asia airline Garuda Indonesia worth £4bn.
The deal to upgrade Garuda's fleet of Airbus A330 to A330NEO aircraft is estimated to be worth around £1bn to the UK economy with the wings being made at Airbus Broughton in North Wales.
The upgrade deal secures jobs at the Airbus Broughton and Airbus Filton plants as well as at Rolls Royce in Derby and Bristol.


text:
This Is Why Drinking Diet Soda Is So Bad for You
Megan Parry · Jun 5, 2017
We already knew the artificial sweeteners in diet soda could cause you to gain weight and be unhealthy for your unborn baby. It's why we're always so pumped about finding healthy drink recipes to replace soda! So it doesn't come as a surprise to learn that a recently published study in the American Heart Association journal Stroke confirms that diet soda is bad for you.
Diet soda is so unhealthy, in fact, that the researchers from Boston University found people who drink just one diet soda per day were three times more likely to suffer a stroke or develop signs of dementia. Conducted over the course of 10 years, the study followed 2,888 adults over the age of 45 and 1,484 adults over the age of 60. While the evidence is there, the team can't officially determine causation just yet. As a result, they've said more research needs to be done, much like the conclusion of the 2014 study that linked heart disease in women with diet soda.
Of course, the negative health effects of diet soda aren't always so dire. In fact, diet soda can cause all kinds of everyday issues that are annoying at best and detrimental at worst. We spoke with Brooke Alpert, nutrition expert and author of The Sugar Detox, to get her take on just what makes diet soda so terrible and what you can do to break your habit. Bonus: Alpert shares two ways to make drinking water way more fun, promise.
artificial sweet-and-low down
1. Less calories don't equal healthier. "Health is so much more than calories — in fact, the whole 'calories in, calories out' concept is quite antiquated," Alpert points outs. She reminds us that diet soda is loaded with artificial sweeteners and chemicals, which have been shown to be harmful to our health. Even worse, she says that "studies have shown that people who drink diet soda tend to overeat, and our bodies still react to these fake sugars like regular sugar so they actually still cause weight gain."
2. Fake sugar is genuinely bad for you. "I can't say it clearly enough — artificial sweeteners are terrible for you." Alpert does not mince words when it comes to all the ways "those pretty colored packets" can harm your health. From dementia to heart disease, skin problems, and bloating, artificial sweeteners mess with you in myriad ways with no discernible benefit to your waistline or your energy levels.
3. You've just got to stop RN. Bad news, diet soda addicts. Alpert says the only real way to kick the habit is to give it up cold turkey. To help soften the bubbly blow, she recommends "loading up on unsweetened ice tea and flavored sparkling waters to grin and bear it for a few days. My clients have found once they make it past day three, they're over the worst of it."
4. Water is where it's at. Honestly, there's no replacement for H2O. "Every cell in your body depends on adequate levels of water intake to keep your body functioning as well as it can be. Dehydration is linked to everything from fatigue, false hunger, weight gain, poor skin, and stomach problems," Alpert says. Of course, these awful effects sound eerily similar to those that come with diet soda drinking — convinced yet?
two ways to stay hydrated af
1. There's an app for that. We use apps to remind us to meditate, pay our bills, and book flights — so why not use one to remind yourself to drink more water? Alpert says she's "currently obsessed with WaterMinder right now." The free app lets you set hydration goals, sends you daily reminders, tracks your water intake, and even gives you badges when you reach your goals.
2. Spice it up. Literally! Add cayenne pepper and lemon juice to your water for an extra kick. Alpert is all about that La Croix life RN and says the lime and grapefruit flavors are her current faves. She also suggests adding fruit, cucumber, or mint to a cute water bottle to make the hydration game feel more fun.
Have you kicked your diet soda habit for good? Tweet us @BritandCo and let us know what worked for you!
(Photos via Getty)
Megan Parry
Megan Parry is a lifestyle writer living in Philadelphia where she's making generous use of her library card, obsessively reading friends' natal charts, and learning how to differentiate between "dried apricot" and "juicy apricot" notes in wine.
Diet, Health, Lifestyle

redpajama_set_name: RedPajamaCommonCrawl


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Showing posts with label NatGeo's MARS TV series. Show all posts
NatGeo's MARS TV series: drama part of full season 2
Most of the criticism against National Geographic Channel's MARS TV series is related to the concept of joining drama with documentary. In a result the viewer can't fully enjoy any of the two parts without interruptions from the other part every few minutes. So here you can watch only the drama part of full season 2 of the show without documentary cut-ins.
In season 1 the series depicted first human mission to Mars and hardships of building a base on Mars in a lava tube. Season 2 is digging deeper into the challenges of life on Mars, covering everything from the first Martian-born baby to surviving the common cold to the first commercial efforts by Lukrum Corporation to exploit the planet's resources.
Watch full episodes of the show here:
Tags: Mars Base, Mars Colonization, Mars Colony, NatGeo's MARS TV series, National Geographic, TV Series
Mars colony concept by Dmitry Azarov for National Geographic's MARS TV series
Russian 3D artist Dmitry Azarov has made some concept renders of human colony on Mars for National Geographic's "MARS" TV series (season 3 ?). More of his art here.
Tags: Dmitry Azarov, Mars Colonization, Mars Colony, NatGeo's MARS TV series, National Geographic, TV Series
Images from NatGeo's MARS episode 6 of season 2
Sixth episode "The Shake Up" ( s02e06 ) of National Geographic's MARS TV series season 2 has been aired. Here are some images from the episode.
Storyline in short: Using explosives, miners from Lukrum colony finally get to an underground water aquifer. But water is under pressure and causes a severe earthquake, damaging Olympus Town but completely destroying Lukrum colony, leaving a dozen miners dead. While inspecting his ruined base the commander of Lukrum colony is killed in an accident. The season ends on a positive note - Earth is introduced with the first child born on Mars and Olympus Town is repaired and is open for future expansion.
Images from other episodes of season 2: ► Episode 1 ► Episode 2 ► Episode 3 ► Episode 4 ► Episode 5
@ 06:45 1 comment:
Tags: Astronaut on Mars, Mars Base, Mars Colonization, Mars Rover, NatGeo's MARS TV series, National Geographic, Space Commerce, TV Series
Fifth episode "Power Play" ( s02e05 ) of National Geographic's MARS TV series season 2 has been aired. Here are some images from the episode.
► Watch full episode 5 of season 2 here
Storyline in short: Miners from Lukrum colony are prospecting for resources and require additional power from Olympus Town. Meanwhile on Earth Lukrum Industries struck deal with Russia to get exclusive mining rights on Mars. While Olympus Town's commander is inspecting potential water site her deputy cuts power to Lukrum colony causing power outage there and nearly killing some of the miners.
Fourth episode "Contagion" ( s02e04 ) of National Geographic's MARS TV series season 2 has been aired. Here are some images from the episode.
Storyline in short: Deadly Martian pathogen strikes both Lukrum colony and Olympus Town. It's discovered only a Penicillin can fight it and it's available only on Chinese orbital station which drops it to the ground.
Tags: Astronaut on Mars, Mars Base, Mars Colonization, NatGeo's MARS TV series, National Geographic, Space Commerce, TV Series
Third episode "Darkness falls" ( s02e03 ) of National Geographic's MARS TV series season 2 has been aired today. Here are some images from the episode.
Storyline in short: Massive solar flare knocks out electrical grid and communications on Mars in time when one of the scientists is taking samples from Lukrum's water drilling site on her own, without informing anyone. Miners from Lukrum colony rescue her life, thus restoring relations between the two settlements.
Second episode "Worlds apart" ( s02e02 ) of National Geographic's MARS TV series season 2 has been aired. Here are some images from the episode, apart from those seen in the trailer and promotional collection.
Storyline in short: While scientists from Olympus Town and miners from Lukrum colony are making connections, miners discover a liquid water pocket bellow the surface of Mars. Now the Lukrum colony can be independent from the resources of Olympus Town but scientists are not happy with miners contaminating possible shelter of Martian life-forms. Meanwhile one of the original crew discovers she is pregnant but Olympus Town commander's twin sister dies from tumor during a flight to Mars.
Images from other episodes of season 2: ► Episode 1 ►Episode 3 ► Episode 4 ► Episode 5 ► Episode 6
NatGeo's MARS TV series: full episodes of season 2
Watch full season 2 of National Geographic Channel's MARS TV series here. In season 1 the part documentary, part fiction series depicted first human mission to Mars and hardships of building a base on Mars in a lava tube. The six-episode season 2 is digging deeper into the challenges of life on Mars, covering everything from the first Martian-born babies to surviving the common cold to the first commercial efforts by Lukrum Corporation to exploit the planet's resources.
DRAMA PART of full season 2 without documentary cut-ins:
► Watch full episodes of season 1 here
More content from season 2: ► HD images ► Trailer & images from it ► Cover art
Episode 1 - We are not alone s02e01
Images from Episode 1
Watch full episode HERE.
Tags: Mars Base, Mars Colonization, Mars Colony, Mars Documentary, NatGeo's MARS TV series, National Geographic, Science Fiction, Space Commerce, TV Series
The first episode "We are not alone" ( s02e01 ) of National Geographic's MARS TV series season 2 has been aired today. Here are some (mostly exterior) images from the episode, apart from those seen in the trailer and promotional collection.
Storyline in short: Beginning of season 2 is set in April 2042, 5 years after the end of season 1. In episode 1 the scientists at Olympus Town are accompanied by a new neighbor - miners at Lukrum colony 25 kilometers apart. Miners demand to be connected to the water source of Olympus Town and rush building the pipeline, causing an accident and concerns in Olympus Town.
Tags: Mars Base, Mars Colonization, Mars Rover, NatGeo's MARS TV series, National Geographic, Space Commerce, Spaceship, TV Series
NatGeo's "Mars" TV series - cover art for season 2
Season 2 of National Geographic's MARS part documentary, part fiction TV series is starting today. In season 1 the series depicted first human mission to Mars and hardships of building a base on Mars. The six-episode season 2 will dig deeper into the challenges of life on Mars, from the first Martian-born babies to first commercial efforts to exploit the planet's resources.
More content from season 2: ► FULL EPISODES ► HD images ► Trailer and images from it
Here are some cover art images and posters for season 2. Open link in new tab to view images in full resolution.
Tags: Astronaut on Mars, Mars Colonization, NatGeo's MARS TV series, National Geographic, Poster, TV Series, Wallpaper
NatGeo's "Mars" TV series - HD images from season 2
National Geographic's "Mars" part documentary, part fiction TV series depicted first human mission to Mars and hardships of building a base on Mars in a lava tube in season 1. The six-episode season 2 promises to dig deeper into the challenges of life on Mars, covering everything from the first Martian-born babies to surviving the common cold to the first commercial efforts by Lukrum Corporation to exploit the planet's resources. Season 2 will be broadcasted worldwide starting on November 12th.
In July National Geographic released a trailer for the season 2. You can watch and see some HD images from the trailer here.
Here are more images from the season 2. Open link in new tab to view images in full resolution.
Mars base infographic by National Geographic
Mars base infographic by National Geographic accompanying the season 1 (2016) of its "MARS" TV series.
Season 2 of the TV series will be broadcasted worldwide starting on November 12th.
You can watch the full season 1 episodes of the TV series without geographic restrictions here.
Closeup of the underground section of the base:
Tags: Infographics, Mars Base, Mars Colonization, NatGeo's MARS TV series, National Geographic, Underground colony
HD images from the season 2 trailer of NatGeo's "Mars" TV series
National Geographic has released a season 2 trailer for its "Mars" TV series. The fictional documentary series depicted first human mission to Mars and hardships of building a base on Mars in a lava tube in season 1. The six-episode season 2 promises to dig deeper into the challenges of life on Mars, covering everything from the first Martian-born babies to surviving the common cold to the first commercial efforts to exploit the planet's resources. Season 2 will be broadcasted worldwide starting on November 12th.
Here are some HD images from the trailer. Open link in new tab to view images in full resolution.
Tags: Astronaut on Mars, Mars Base, Mars Colonization, Mars Colony, Mars Documentary, Mars Rover, NatGeo's MARS TV series, National Geographic, TV Series
Olympus Town Mars colony by Pixoloid Studios
Picture of the Day 7/10/2017 - "Olympus Town" (a human colony in a Martian lava tube) concept art by Pixoloid Studios for National Geographic's 'Mars' TV series.
Tags: Concept Art, Mars Colonization, Mars Colony, NatGeo's MARS TV series, National Geographic, Picture of the Day, Pixoloid Studios, Underground colony
NatGeo's "Mars" TV series: full episodes of season 1
Watch the full season 1 episodes of National Geographic Channel's "Mars" TV series here. The fictional documentary series are depicting the first human mission to Mars, the subsequent building of the first human base on Mars in a lava tube and the further steps for colonization of Mars till the end of 21st century.
Content from season 2:
► Full episodes ► HD images ► Trailer & images from it ► Cover art
Images from: ► Episode 1 ► Episode 2 ► Episode 3 ► Episode 4 ► Episode 5 ► Episode 6
Episode 01 - Novo Mundo ( s01e01 )
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Tags: Mars Base, Mars Colonization, Mars Colony, Mars Documentary, Mars Mission, NatGeo's MARS TV series, National Geographic, Science Fiction, TV Series, Underground colony
HD images from NatGeo's "Mars" TV series
Here are some HD images from National Geographic Channel's upcoming "Mars" TV series which will be broadcasted worldwide starting on November 14th. The fictional documentary series will depict the first human mission to Mars, the subsequent building of the first human base on Mars in a lava tube and the further steps for colonization of Mars till the end of 21st century.
Here are also a bunch of trailers and video clips from the upcoming series. Open link in new tab to view in full resolution:
Tags: Mars Base, Mars base interior, Mars Colonization, Mars Colony, Mars Mission, NatGeo's MARS TV series, National Geographic, TV Series, Underground colony


Orange growers in the north central province of Ha Tinh are likely to reap a bumper crop with trees heavy with fruits waiting for harvest and sale in Tet Holiday, when consumption demand and prices traditionally move up.
This year, favorable weather condition and right farming process have brought a good crop for many farms in the province.
The largest orange growing districts comprise Can Loc, Huong Son, Huong Khe, Vu Quang, Ky Anh, Duc Tho and Cam Xuyen.


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22 killed in Hebei chemical plant blast
China | 28 Nov 2018 11:30 am
At least 22 people were killed and 22 others wounded in an explosion near a chemical factory in Qiaodong district, in northern Hebei province today, according to local authorities. The explosion occurred at 0:41 a.m. local time and triggered a massive blaze. Preliminary investigation indicates that 38 trucks and 12 cars were involved in the accident, Xinhua reports. The injured have been hospita...
At least 22 people were killed and 22 others wounded in an explosion near a chemical factory in Qiaodong district, in northern Hebei province today, according to local authorities.
The explosion occurred at 0:41 a.m. local time and triggered a massive blaze. Preliminary investigation indicates that 38 trucks and 12 cars were involved in the accident, Xinhua reports.
The injured have been hospitalized and the fire has been contained. A search operation is on to look for possible survivors.
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NEW YORK (Reuters ) – Women who develop high blood pressure during pregnancy may be more likely to experience it again later or be diagnosed with other risk factors for heart disease like high cholesterol or diabetes, a US study suggests.
High blood pressure in pregnancy and afterwards, as well as diabetes and high cholesterol, are more likely when women are obese, Fraser noted.
"Therefore, maintaining a healthy weight throughout life – before pregnancy and after – is important," Fraser advised.


Following a successful reduction in property damage and injuries in the first year of a citywide fireworks ban, the City of Marysville and Marysville Fire District once again remind residents that all fireworks are illegal. The fine for discharging consumer fireworks in the city will increase this year to $257, as previously announced.
With the ban in place in 2017, Marysville saw a decrease in the number of fireworks-related injuries, fires and other incidents. The Marysville Fire District did not respond to a single fireworks-related injury call within city limits during the 2017 July 4th holiday.
"We are already seeing the positive effects of the fireworks ban in the reduction of property damage and injuries," Fire Marshal Tom Maloney said. "We thank all residents for helping with this effort and ask that you choose to celebrate safely again this year by avoiding the use of fireworks."
The fireworks ban prohibits the use, discharge, sale or trade of any fireworks, including sparklers and "consumer" fireworks within city limits. People who violate the ban will face a $257 fine (increased from $103 in 2017) and possible jail time, depending on the violation. Any fireworks found will also be confiscated by Marysville Police, who will be conducting extra patrols on and around the July 4th holiday.
City residents are encouraged to report illegal fireworks by calling the non-emergency line at (425) 407-3999. To keep emergency phone lines open, please avoid calling 911 unless there is an immediate threat to life or property.


The range of premium ingredients Kilo Grill offers makes them more of an international BBQ joint. Expect to see Iberico pork, sashimi grade Argentinian sea prawns, Australian rib eye and Irish oysters on the menu. If these sound expensive, then their recently launched all you can eat BBQ buffet will be music to your ears and wallet. Priced at RM49.90 nett per pax (RM59.90 on Fri and weekends), you can enjoy unlimited servings of fresh meat and seafood and best of all, there's no time limit too.


During this discussion Delegates From Eppendorf Company Mr. Romie augustino, Dianne Tan and Mr. Akshay Kadalilae From UAE were introducing their Companies working profile and worldwide Brand Laboratory Instruments.
They explained that Eppendorf is a leading life science company that develops and sells instruments, consumables, and services for liquid- sample and cell handling in laboratories worldwide. Its product range includes pipettes and automated pipetting systems, dispensers, centrifuges, mixers, spectrometers, and DNA amplification equipment as well as ultra-low temperature freezers, fermentors, bioreactors, CO2 incubators, shakers, and cell manipulation systems as well as Consumables such as pipette tips, test tubes, microtiter plates, and disposable bioreactors complement the range of highest-quality premium products.
Dr Kassahun, on is talk, Explained the institutional interest in light with the need for Quality laboratory to conduct critical discoveries as the national biotech institute, and plans to build a national laboratory and regional laboratories that will serve as a hub for dedicated biotechnology R&D. Dr kasshun said Knowing the profile of worldwide scientific Research Company helps to identify the brand equipments currently underway and to build standard scientific research laboratories.
It is noticed that the discussion is concluded by sharing information each others about eppendorf's Company Experience working across globe that in line with Working situation, Priorities area of Ethiopian Biotechnology Institute.


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National A Quest Diagnostics Discloses Breach of Patient Records
A Quest Diagnostics Discloses Breach of Patient Records
Monday, June 3, 2019 at 5:30 pm | כ"ט אייר תשע"ט
(The Washington Post) -
(Google)
Quest Diagnostics, the medical testing company, said a data breach has affected about 11.9 million patients, after an "unauthorized user" gained access to financial data, Social Security numbers, and medical data, but not laboratory test results.
A collections agency called American Medical Collection Agency notified Quest about a potential intrusion on May 14 and then reported on the scope of the breach on Friday.
AMCA provides services to Optum360, a Quest billing contractor. Quest said it does not have details about which patients were affected and what data was stolen.
Quest "has not been able to verify the accuracy of the information received from AMCA," Quest said in a statement posted on its website Monday. Quest has suspended collections requests through the agency, it said.
"Quest is taking this matter very seriously and is committed to the privacy and security of our patients' personal information," the company said. It indicated that plans are in the works to begin notifying individual patients but did not give a timeline.
AMCA provided few details of the breach. It said in a prepared statement that it learned its security had been penetrated from a consultant working for credit-card companies.
It moved its payment portal to a third-party vendor and took other steps to beef up security, the agency said.
Optum360, which is part of UnitedHealth Group, did not respond to requests for comment.
A data security consultant said hackers are not interested in health care information, which is not easily monetized, but are hunting down firms that handle financial information for bank account and Social Security numbers.
"Hackers target financial companies, like this billing collection company, as they often store sensitive financial information that can be turned into immediate gains," said Dr. Giovanni Vigna, co-founder of network security provider Lastline. "This kind of information is much more lucrative than personal health information, that, at the moment, is not readily marketable by criminals."
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My wife asked me to find out what happened to Walker's Studios?
Hi Keith Do you mean my namesake's photography business? Last time I saw it, they were in the Morris Ave / Longfellow Rd shops (below): I think they were in what is now the "Foot Clinic", but that would be twenty years ago. I believe the business was started by John Walker, the father. I was at Ravensdale Primary with John Walker, the son, who had taken over the business by the 1980s. He would be in his mid-60s now, so probably retired and sold up.
Yes, Johnnie. It's a small world; my wife went to Ravensdale too with his daughter, and she was at the art College with John Walker junior on a photography course in the early 60s. She had forgotten the shops at the bottom of Morris Avenue; her parents lived in Morris Avenue, and the Painted Lady was her father's local. (I didn't really know, because I'd always lived the other side of the city) Many thanks for that!!


JYM Pro JYM is an up-and-coming protein supplement that promises to aid you build muscles quickly.
Choosing the ideal brand of protein supplement to start with might be rather confusing at first, and like other male in his right mind would do, you 'd wish to ask professionals for suggestions. Among those experts online is Jim Stoppani, PhD.
Jim Stoppani PhD is understood in bodybuilding circles as one of the professionals in Exercise Physiology. In fact, he authored "Jim Stoppani's Encyclopedia of Muscle and Stamina". You may have checked out a few of his posts, as he has actually authored hundreds of posts about workout and fitness.
In all righteousness, Jim Stoppani is a professional in his craft, however how would you react if an individual with a PhD would offer you a formula that he established himself?
Most would buy it in a heartbeat, and that's what JYM Pro JYM wants to achieve. Nevertheless, a little digging would expose that the company behind JYM Pro JYM, Stoppani Supplement Science, has yet to acquire its GMP certification.
One thing that might be hampering the success of JYM Pro JYM is the sensible idea that if a fitness specialist that would suggest a supplement, it would be a fundamental requirement to have at least a GMP accreditation. GMP accreditations are provided to combat supplement scams, a widespread trend that's consuming the supplement industry away. GMP certifications are released to business that exhibit state-of-the-art quality control.
Of course, the absence of the certificate doesn't mean that the item is no good. That's why we took a better check out JYM Pro JYM to see if it's appropriate to think about it one of the very best.
The idea behind JYM Pro JYM is to enable your muscles to establish faster by providing it with adequate nutrients pre-, intra-, and post- exercise. That's why JYM Pro JYM is equipped with various proteins that break down at various rates, to make sure that there's constantly sufficient protein in your body to aid establishing muscle groups.
The item also claims that this mixture of various proteins would assist reduce catabolic activity, to enhance the ratio of body fat vs muscle mass.
According to the individuals who tried utilizing JYM Pro JYM, the taste was OKAY however not remarkable. Majority of those who have actually tried JYM Pro JYM has mentioned that there were no immediate results with the product.
Proteins build muscles. What the outstanding physician got right is the mix of protein that breaks down in a different way to provide your muscles with protein for longer durations of time.
Given that the protein is likewise absorbed over a longer duration of time, many question its effectiveness because 24 grams of protein is spread out thin over numerous hours of digestion. Traditional reasoning recommends that the quantity of protein you absorb would be less in terms of concentration, but the protein would be processed, and you still get a portion of the nutrient over a duration of time.
It would be reasonable to question the absence of a GMP accreditation. Let's just hope that the Stoppani Supplement Science gets it in time. For the meantime, you might wish to have a look at NitroGenix 365— an item that has the best blend of proteins, energy-boosting nutrients, and hormonal agent level boosters to build the body you have actually always desired.
JYM Pro JYM Score: D-.


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Юрий Васильевич Болденков — советский и российский , преподаватель музыки. Заслуженный деятель искусств Удмуртской Республики (1994). Член Союза композиторов СССР и России (1972).

Биография
Юрий Васильевич Болденков родился 10 января 1945 года в селе Макарье Котельнического района Кировской области. В 10-летнем возрасте с семьёй переехал в Киров, где начал заниматься музыкой: сначала в любительском духовом оркестре, затем — в музыкальной школе по классу трубы. В 1959 году поступил в класс валторны Кировского музыкального училища. В 1962 году по семейным обстоятельствам был вынужден переехать во Владимир, в котором в 1963 году окончил местное музыкальное училище по специальности «Духовые инструменты» по классу валторны. После училища в течение года преподавал фортепиано в детской музыкальной школе города Меленки Владимирской области.

В 1964 году Юрий Васильевич поступил учиться в Казанскую государственную консерваторию в класс валторны профессора Анатолия Петровича Колпинского, однако через месяц после начала учёбы был призван в армию; службу проходил в штабном оркестре дивизии. Вернувшись из армии в 1967 году поступил на композиторский факультет Казанской консерватории в класс композиции профессора Альберта Семёновича Лемана, в дальнейшем обучался у профессора Анатолия Борисовича Луппова.

После окончания Казанской консерватории в 1972 году Юрий Васильевич Болденков был принят в Союз композиторов СССР и по распределению направлен на работу в Ижевск. Здесь в числе первых наряду с другими выпускниками консерватории Леонидом Васильевым и Андреем Руденко, а также классиками удмуртской музыки Германом Корепановым и Геннадием Корепановым-Камским стал членом созданного 13 марта 1973 года Союза композиторов УАССР.

Дальнейшая жизнь Юрия Васильевича неразрывно связана с музыкальной жизнью Удмуртии: с 1972 по 1986 он работал преподавателем музыкально-теоретических дисциплин в Республиканском музыкальном училище, с 1986 по 1987 годы — руководителем духового оркестра в Дворце культуры «Октябрь» Ижевска, с 1987 по 1993 годы — заведующим музыкальной частью Государственного русского драматического театра им. В. Г. Короленко, с 1994 по 1996 годы — композитором-аранжировщиком в Государственном духовом оркестре Удмуртии.

В 1994 году Юрию Васильевичу Болденкову было присвоено почетное звание «Заслуженный деятель искусств УАССР». Скончался 21 марта 1996 года в Ижевске.

Творчество
Юрий Васильевич Болденков начал сочинять свою собственную музыку в годы учёбы в музыкальном училище: одним из его первых законченных произведений стало сочинение для хора а cappella «Утро в горах» (1963). За время прохождения службы в армии сочинил свою Первую симфонию (1966). За годы учёбы в консерватории им были написаны Вторая симфония для малого симфонического оркестра, виолончельная соната, вокальный цикл на стихи Мусы Джалиля «Из Моабитских тетрадей», оратория «Лонжюмо», опера «Последний лист».

Первым произведениям, написанным Болденковым в Удмуртии, присуща опора на удмуртский фольклор. Среди них следует отметить фортепианную сюиту «Удмуртские картинки», токкату для фортепиано. В 1975 году он сочинил сюиту для камерного оркестра «Ижевск», посвящённую жизни большого города, а в 1980 году — кантату «Моя Удмуртия» на стихи И.С. Токарева, удостоенную первой премии в конкурсе к 60-летию создания государственности Удмуртии.

Наиболее значительным произведениям композитора присущи философская глубина замысла, сочетание гражданского пафоса и лирики, диалектика рационального и эмоционального, благородство музыкального языка. Для его стиля характерны яркая индивидуальность, черты русского музыкального мышления, жанровое многообразие.

Избранные произведения

 три симфонии (1966, 1972, 1979)
 концерт для фортепиано с оркестром (1969)
 сюита «Ижевск» (1975)
 увертюра «Победа» (1985)
 эпитафия памяти Ф. Васильева (для 11 медных, ударных и контрабаса) (1978)
 «Акварели Востока» для духового оркестра (1990)
 вокально-симфонические и хоровые произведения:
 оратория «Лонжюмо» (1970, стих. А. Вознесенского)
 кантата «Моя Удмуртия» для хора (1980, стих. И. Токарева)
 кантата «Родная земля» (1984, стих. В. Ивашкина)
 3 хора на стихи A. Блока (1969)
 сюита «Девять японских пятистиший» (1989)
 камерно-инструментальные произведения (сюиты, сонаты, циклы для всех духовых инструментов, циклы пьес для детей и др.)
 вокальные циклы на стихи Мусы Джалиля, Шандора Петёфи, Блейка
 25 песен для голоса и фортепиано на стихи удмуртских поэтов (1986)
 опера «Последний лист» (1971)
 музыка к спектаклям, в т.ч.:
 «Карусель счастливых» — для Государственного национального драмтеатра (1984)
 «Лекарь поневоле» — для Государственного национального драмтеатра (1984)
 «Самоубийца» — для Русского драмтеатра им. B. Г. Короленко (1987)
 «Буря» — для Русского драмтеатра им. B. Г. Короленко (1989)

Примечания

Литература

Ссылки
 Произведения Ю. В. Болденкова на сайте Национальной библиотеки Удмуртской Республики

Выпускники Казанской консерватории
Заслуженные деятели искусств Удмуртской АССР
Члены Союза композиторов СССР
Члены Союза композиторов России
Персоналии:Государственный национальный театр Удмуртской Республики

redpajama_set_name: RedPajamaWikipedia


Project Euclid is a collaborative partnership between Cornell University Library and Duke University Press which seeks to advance scholarly communication in theoretical and applied mathematics and statistics. It was created to provide a platform for not-for-profit, society, and independent publishers of scholarly journals to move from print to electronic in a cost-effective and sustainable way.
By becoming part of the Project Euclid community, not-for-profit and small publishers can stay competitive with the larger commercial organizations. Publishers on Project Euclid join forces to meet the business and technical challenges of online publishing without sacrificing their intellectual or economic independence.


text:
Tessa Seymour (born June 9, 1993) is an American cellist.

Early life
On June 9, 1993, Seymour was born in Berkeley, California to Norwegian and Russian parents. She started learning to play the cello at the age of six.

Education
At age 16, she moved to Philadelphia to study at the prestigious Curtis Institute of Music, where she was under the tutelage of Carter Brey and Peter Wiley.

Career
Seymour made her Carnegie Hall debut in 2007 and has performed in numerous other distinguished venues throughout the world, including Davies Symphony Hall and the Kennedy Center. She frequently performs on NPR's radio show From the Top with Christopher O'Riley, as well as the Emmy-nominated PBS television series, Live from Carnegie Hall. In 2009, she attended the Verbier Festival Academy and was awarded the "Prix Jean-Nicolas Firmenich de Violoncelle." In March 2014, she performed the U.S. premiere of Polish composer Krzysztof Penderecki's Suite for Solo Cello at Carnegie Hall. Seymour is also a Jack Kent Cooke Foundation Scholar. She plays on the Carlo Giuseppe Testore "Camilla" cello from 1720.

References

External links
 Tessa Seymour Official Website
 Tessa Seymour on Twitter

American classical cellists
1993 births
Living people
American women classical cellists
21st-century American women musicians
21st-century cellists


text:
Epstein Accomplice Ghislaine Maxwell Hires Former Prosecutor Who Took Down 'El Chapo'
Convicted pedophile Jeffrey Epstein's former girlfriend/best friend and accomplice, Ghislaine Maxwell, has retained the former prosecutor who helped take down drug kingpin Joaquin "El Chapo" Guzman to try to get her off child sex-trafficking charges.
Recent court filings for Maxwell, the 58-year-old British media heiress, reveals that she is being prepped by Christian Everdell, who spent over a decade as an assistant US attorney for the Southern District of New York.
Everdell was once awarded a "True American Hero Award" by the Federal Drug Agent Foundation for "their work in the investigation and apprehension of the former head of the Sinaloa drug cartel in Mexico."
Former colleagues believe he might use his experience in such high-profile cases to get Maxwell to turn on others allegedly in Jeffrey Epstein's inner circle to strike a deal. Hopefully he gets paid before she is "Epsteined" in her jail cell.
"He will, of course, do what is in the best interests of his client, but no doubt he will be talking to her about the benefits of becoming a cooperating witness," tweeted Jennifer Rodgers, who used to be his supervisor as a fellow federal Southern District of New York prosecutor.
Maxwell was arrested on a six-count Manhattan federal indictment for allegedly recruiting underage girls for Epstein, who killed himself (wink wink) in August in a Manhattan jail cell, where his cellmate just prior to his death was transferred out of the cell leaving him alone, and all the security cameras just happened to stop working at the same time, and the guards who were charged with checking in on Epstein every half hour both, not one, but both fell asleep as he was awaiting a new sex-trafficking case.
On Monday, Maxwell was moved to the Metropolitan Detention Center in Brooklyn. Officials say they're taking every possible measure to make sure she doesn't wind up at room temperature the way her former boyfriend, Mr. Epstein did.
Rich Welsh
Rich is syndicated opinion columnist for David Harris Jr. and owner of Maga-Chat.com. He writes about politics, culture, liberty and faith.
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