Robust and Versatile Bipedal Jumping Control through Reinforcement
Learning
- URL: http://arxiv.org/abs/2302.09450v2
- Date: Thu, 1 Jun 2023 03:03:22 GMT
- Title: Robust and Versatile Bipedal Jumping Control through Reinforcement
Learning
- Authors: Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth,
Koushil Sreenath
- Abstract summary: This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world.
We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions.
We develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history.
- Score: 141.56016556936865
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work aims to push the limits of agility for bipedal robots by enabling a
torque-controlled bipedal robot to perform robust and versatile dynamic jumps
in the real world. We present a reinforcement learning framework for training a
robot to accomplish a large variety of jumping tasks, such as jumping to
different locations and directions. To improve performance on these challenging
tasks, we develop a new policy structure that encodes the robot's long-term
input/output (I/O) history while also providing direct access to a short-term
I/O history. In order to train a versatile jumping policy, we utilize a
multi-stage training scheme that includes different training stages for
different objectives. After multi-stage training, the policy can be directly
transferred to a real bipedal Cassie robot. Training on different tasks and
exploring more diverse scenarios lead to highly robust policies that can
exploit the diverse set of learned maneuvers to recover from perturbations or
poor landings during real-world deployment. Such robustness in the proposed
policy enables Cassie to succeed in completing a variety of challenging jump
tasks in the real world, such as standing long jumps, jumping onto elevated
platforms, and multi-axes jumps.
Related papers
- 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) - Grow Your Limits: Continuous Improvement with Real-World RL for Robotic
Locomotion [66.69666636971922]
We present APRL, a policy regularization framework that modulates the robot's exploration over the course of training.
APRL enables a quadrupedal robot to efficiently learn to walk entirely in the real world within minutes.
arXiv Detail & Related papers (2023-10-26T17:51:46Z) - Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots.
Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism.
We present two methods for tackling the benchmark.
arXiv Detail & Related papers (2023-05-24T02:49:43Z) - Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks.
We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
arXiv Detail & Related papers (2023-04-19T17:37:54Z) - Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement
Learning [18.873152528330063]
We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world.
Soccer goalkeeping using quadrupeds is a challenging problem, that combines highly dynamic locomotion with precise and fast non-prehensile object (ball) manipulation.
We deploy the proposed framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness of our framework for various agile interceptions of a fast-moving ball in the real world.
arXiv Detail & Related papers (2022-10-10T04:54:55Z) - Advanced Skills by Learning Locomotion and Local Navigation End-to-End [10.872193480485596]
In this work, we propose to solve the complete problem by training an end-to-end policy with deep reinforcement learning.
We demonstrate the successful deployment of policies on a real quadrupedal robot.
arXiv Detail & Related papers (2022-09-26T16:35:00Z) - Learning Agile Locomotion via Adversarial Training [59.03007947334165]
In this paper, we present a multi-agent learning system, in which a quadruped robot (protagonist) learns to chase another robot (adversary) while the latter learns to escape.
We find that this adversarial training process not only encourages agile behaviors but also effectively alleviates the laborious environment design effort.
In contrast to prior works that used only one adversary, we find that training an ensemble of adversaries, each of which specializes in a different escaping strategy, is essential for the protagonist to master agility.
arXiv Detail & Related papers (2020-08-03T01:20:37Z)
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.