Learning Bipedal Walking On Planned Footsteps For Humanoid Robots

• URL: http://arxiv.org/abs/2207.12644v1
• Date: Tue, 26 Jul 2022 04:16:00 GMT
• Title: Learning Bipedal Walking On Planned Footsteps For Humanoid Robots
• Authors: Rohan Pratap Singh, Mehdi Benallegue, Mitsuharu Morisawa, Rafael Cisneros, Fumio Kanehiro
• Abstract summary: Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction. In this paper, we tackle this problem by learning a policy to follow a given step sequence. We show that simply feeding the upcoming 2 steps to the policy is sufficient to achieve omnidirectional walking, turning in place, standing, and climbing stairs.
• Score: 5.127310126394387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command. In this paper, we tackle this problem by learning a policy to follow a given step sequence. The policy is trained with the help of a set of procedurally generated step sequences (also called footstep plans). We show that simply feeding the upcoming 2 steps to the policy is sufficient to achieve omnidirectional walking, turning in place, standing, and climbing stairs. Our method employs curriculum learning on the complexity of terrains, and circumvents the need for reference motions or pre-trained weights. We demonstrate the application of our proposed method to learn RL policies for 2 new robot platforms - HRP5P and JVRC-1 - in the MuJoCo simulation environment. The code for training and evaluation is available online.

Related papers

• Octo: An Open-Source Generalist Robot Policy [88.14295917143188]
  We introduce Octo, a large transformer-based policy trained on 800k trajectories from the Open X-Embodiment dataset. It can be effectively finetuned to robot setups with new sensory inputs and action spaces within a few hours on standard consumer GPU. We also perform detailed ablations of design decisions for the Octo model, from architecture to training data, to guide future research on building generalist robot models.
  arXiv  Detail & Related papers  (2024-05-20T17:57:01Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning [4.260312058817663]
  We build upon the Reset-Free QD (RF-QD) algorithm to learn controllers directly on a physical robot. This method uses a dynamics model, learned from interactions between the robot and the environment, to predict the robot's behaviour. RF-QD also includes a recovery policy that returns the robot to a safe zone when it has walked outside of it, allowing continuous learning.
  arXiv  Detail & Related papers  (2023-04-24T13:24:00Z)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning [86.06110576808824]
  Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments. Recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped in only 20 minutes in the real world.
  arXiv  Detail & Related papers  (2022-08-16T17:37:36Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)
• PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning [4.314956204483074]
  This paper introduces a generic training algorithm teaching generalized PPMC in rough environments to any robot. We show through experiments that the robot learns to generalize to new rough terrain maps, retaining a 100% success rate. To the best of our knowledge, this is the first paper to introduce a generic training algorithm teaching generalized PPMC in rough environments to any robot.
  arXiv  Detail & Related papers  (2020-03-02T10:14:52Z)
• Learning to Walk in the Real World with Minimal Human Effort [80.7342153519654]
  We develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention.
  arXiv  Detail & Related papers  (2020-02-20T03:36:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.