Our experiments are performed on a humanoid robot Baxter from RethinkRobot [cite?]. The arm has 7 DoF and 2 grippers in the end, and can be controlled by position, velocity or effort at the endpoint at 100 Hz. In the simulator Gazebo, the world is initialized with table, plates and wood board for the tasks, and the tools (eg. fork, knife, brush) are initialized in left hand gripper to perform the action sequence.
In the execution cycle of our experiments, Baxter can execute 5 action primitives: Stay(S), Left(L), Right(R), Forward(F), backward(B), and each moves one step unit \delta (\delta=0.08m ). We control the arm of Baxter by sending the desired end effector to inverse kinematics (IK) solver to obtain the joints angles (7 joints: left_s0, s1, e0, e1, w0, w1, w2) for joint positional control. The end effector position may be slightly unstable though IK is using an initialized neutral arm position as the seed. Actions are executed at 2 Hz and image frames are refreshed at the same frequency. At each time step, the cropped RGB image from head camera is resized to 80\times80
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Then DQN takes it as input and generates action that maximizes the long term expected rewards. Baxter follows \epsilon -greedy policy (\epsilon\in[0.05,0.5] decreasing with episodes as [#DQN:mnih2013playing]) till an action length time-out, which roughly takes 10 seconds. Robot is performing a “test” episode every 50 episodes, by following learned policy exactly without random exploration. Once each episode finished, the cached image frames are fed in CNN-LSTM to obtain the terminal reward and the robot is reset to the neutral position. All the joints, end-effector states, values and rewards tuples (s,a,r,s',a') are stored in replay memory for training, and “test” episodes are recorded for later evaluation. The replay memory is of size 200k due to machine memory constraints. We then randomly sampled 50k tuples from replay memory to train the DQN during each training
After the experiment had been conducted, the investigator concluded that the original hypothesis did not fit the outcome of the testing of the Beetle Robot. While it was hypothesized that the device would move and react in the same
from figure 3 that input is feeding to the network in different time steps, not at once like feed forward case. However the parameters the network learns throughout different
The problem in this domain is developing a simple AI agent in our Tekken Moves Predictor. According to book AI techniques in games is cheating. [O’Reilly, 2004]
Continuing with the development and improvement of the assembly line, in the 1960s, new machines were invented that allowed for five axes of motion. These devices were called the “Versatran”, and were installed a Ford factory in Ohio. But later in the decade, robots became even more complex adding another axis it can work
Those consistent behaviors are known as steady-state behaviors (L. Taglialatela, personal communication, October 17, 2015). For each schedule of reinforcement it is important to obtain these consistent behaviors in order for the organism to learn that based on their actions and elapsed time intervals, reinforcement will be distributed (L. Taglialatela, personal communication, October 17, 2015). Within the real world example, it is crucial for the steady-state behaviors to be immediate, especially during the continuous reinforcement schedules. This not only allows the order fulfillment to be quick, but it also keeps the guest engaged and prepares the guest to deliver immediate responses back to the employee as reinforcement (L. Taglialatela, personal communication, October 17,
can be changed quickly, and this change can make a major difference almost overnight. The reward structure, the
In the article “In the Future, Warehouse Robots Will Learn on Their Own” talks about an automatic robot in Berkeley, California that can better advance the speed and technology of retail stores everywhere. Jeff Mahler and a group of engineers are working on a robot, Berkeley robot that can operate on its own. Mr. Mahler used a software that's placed inside the robot called neural network, or a network that's similar to the human brain and can absorb information by observation of an object. Attached to the berkeley robot is a two fingered hand with suction cup grip that can hold almost any object. The group of scientists investigated in computer-aided design (CAD) or digital depiction of physical objects. Eventually the team was able to create
Design vs. Programming - In your opinion, which item (design or programming) proved to be more valuable? Defend your answer. The program is more important than the build if good bot not good program you would fall off the edge.
Because of the increasing number of machines with smarts, we are facing “the inflection point” (301). However, our fixed views on intelligent robots such as demands that artificial intelligence should look like human, prevent us from realizing what is already happening around us. He states that to see how far artificial intelligence has changed our lives, we need to get rid of the fixed views. For example, an industrial robot named Baxter is designed for people to easily work right next to it and easily train it, which is the noteworthy feature that other robots fail to implement. Baxter is also cheaper than other robots, which means it is easy to install it. Although Baxter is not humanlike, it represents how robots are becoming advanced as stated
Two researchers from the University of Rochester have found a way to reduce the process of completing actions. Bodies complete actions through signals which are broadcasted in the visual center in the back of the brain then redefined and sent to the Premotor Cortex. The shortened system directly plants electrodes in the Premotor Cortex. These electrodes in the Premotor Cortex are sent bursts of electricity which send signals to the brain. The two analysts were able to discover a more efficient way of completing movements.
Figure 5.9 Basic execution of TurtleMouse over time [l-r, t-b], using above initialization weights. All options are set to initialization states. “Learning” is characterized by periods of oscillatory states (long, straight lines) separated by “interesting behavior” where the turtle responds to the position and presence of the
The Robotic mechanical arms purpose is to capture, collect and retrieve samples for the P.T.P. It is located in forward department of the R.M.M. It is equipped to deal with objects at 360 degrees and allows simple tasks like grasping, sorting, cutting, digging, examining, and moving objects. It can grasp mid size objects and has lifting capacity up to 90 kg if needed and deliver it into the testing platform of the PTP component on the rover. The robotic arm is designed with 12 servo motors to move and rotate the arm in any direction. The robotic
A major impetus to improving artificial limbs started when the United States encouraged companies to improve prosthetics instead of munitions (Norton, 2007). The combination of lighter materials and robotics assist has created huge advancements in functionality and has dramatically improved quality of life and potential for independent living. Even with the advancement of these limbs, the basic mechanical principals are still the same. Modern times allow for many different types of limbs to be created. Limbs can be created to match skin tone, freckles and fingerprints. There are three many ways a limb can be made to move. The first is attaching the limb to a moving body part to act as a gear shifter. Another variation is a motor attached and the person can switch modes by a mechanical toggle shift. The most advanced movement is the myoelectric capability. This is when electrodes are placed on the muscles of the residual limb. When contracted the arm will move according to which electrode fired. A microprocessor can also be attached to learn exactly how the person walks (Clements, 2008). Modern prosthetics offer valuable life skills, yet are very
Production : Observers must be physically and/intellectually capable of producing the act. In many cases the observer possesses the necessary responses. But sometimes, reproducing the model 's actions may involve skills the observer has not yet acquired. It is one thing to carefully watch a circus juggler, but it is quite another to go home and repeat those acts.
The design of the robot and how it behaves is important to how people perceive and interact with humanoid robots (Fink).