Reinforcement learning enabled robot Cassie to learn new tricks

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Reinforcement learning enabled robot Cassie to learn new tricks
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Cassie ran quarter miles on different terrains while performing long and high jumps, without being trained on each movement

Thanks to a new method based on the Al technique named reinforcement learning, robots can learn to deal with unpredictable situations encountered in this way. Reinforcement learning is like pet training; it involves rewards and penalties .Cassie is a human-sized robot. He has a height of 3,2 feet and weighs 68 pounds , and his two legs got the backup, so he ran quarter miles on different terrains while performing long and high jumps without being trained on each movement.

As described, they used a simulation to train Cassie because that speeds up the time it takes to learn and enables the robot to perform those same skills in the real world.They trained the neural network that controlled Cassie to master simple skills like jumping on the spot and walking forward. They presented the model with new commands encouraging the robot to perform tasks using its new movement skills.

So, when it became good with new tasks in a simulated environment, they then used a method of task randomization to diversify the tasks he had been trained on. That could help him be more prepared for unexpected situations. As described in the study, they incorporated a task completion term into the reward to ensure the robot accomplishes the assigned tasks using the acquired locomotion skill. Within this term, they motivated the robot to align its movement with the desired velocity and turning rate…

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