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Make Bipedal Robots Learn How to Imitate

arXiv 2105.07193 ↗First posted 15 May 2021cs.RO cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 15 May 2021.

Builds on

15 of 45 references.

References · 15

  1. 2017Deep reinforcement learning from human preferencescited by 3,912
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2016OpenAI Gymcited by 1,736
  4. 2016Generative Adversarial Imitation Learningcited by 1,597
  5. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  6. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  7. 2018Behavioral Cloning from Observationcited by 492
  8. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  9. 2018An Algorithmic Perspective on Imitation Learningcited by 459
  10. 2020Primal Wasserstein Imitation Learningcited by 121
  11. 2019Camera-to-Robot Pose Estimation from a Single Imagecited by 48
  12. 2019Imitation Learning for Human Pose Predictioncited by 44
3 more
  1. 2018Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segwaycited by 20
  2. 2020Imitating Unknown Policies via Explorationcited by 7
  3. 2017Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flowcited by 1

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  1. 2021Adaptive Mimic: Deep Reinforcement Learning of Parameterized Bipedal Walking from Infeasible References ↗
  2. 2020Deep Imitation Learning for Bimanual Robotic Manipulation
  3. 2024Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey
  4. 2024I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
  5. 2023From Knowing to Doing: Learning Diverse Motor Skills through Instruction Learning
  6. 2024Bi-Level Motion Imitation for Humanoid Robots
  7. 2024Data-Efficient Approach to Humanoid Control via Fine-Tuning a Pre-Trained GPT on Action Data
  8. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control