Make Bipedal Robots Learn How to Imitate
This addresses the challenge of enabling bipedal robots to learn basic movements more efficiently, though it appears incremental as it builds on existing imitation learning and DQN techniques.
The paper tackles the problem of bipedal robots performing poorly compared to humans by proposing a method to train them using imitation learning from a single video of an instructor, resulting in faster training while keeping joint angles within physical limits.
Bipedal robots do not perform well as humans since they do not learn to walk like we do. In this paper we propose a method to train a bipedal robot to perform some basic movements with the help of imitation learning (IL) in which an instructor will perform the movement and the robot will try to mimic the instructor movement. To the best of our knowledge, this is the first time we train the robot to perform movements with a single video of the instructor and as the training is done based on joint angles the robot will keep its joint angles always in physical limits which in return help in faster training. The joints of the robot are identified by OpenPose architecture and then joint angle data is extracted with the help of angle between three points resulting in a noisy solution. We smooth the data using Savitzky-Golay filter and preserve the Simulatore data anatomy. An ingeniously written Deep Q Network (DQN) is trained with experience replay to make the robot learn to perform the movements as similar as the instructor. The implementation of the paper is made publicly available.