Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

2023-02-19 · Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath

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.

📄 PDF Abstract BibTeX arXiv:2302.09450

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control

2024-01-30 · Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine 외

This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single locomotion skill, we develop a general co…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Height Control and Optimal Torque Planning for Jumping With Wheeled-Bipedal Robots

2026-05-05 · Yulun Zhuang, Yuan Xu, Binxin Huang, Mandan Chao 외 arxiv

This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinear estimation, and…

Continuous Versatile Jumping Using Learned Action Residuals

2023-04-17 · Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang 외

Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement learning to learn continuous jumping motion…

Agile and versatile bipedal robot tracking control through reinforcement learning

2024-04-12 · Jiayi Li, Linqi Ye, Yi Cheng, Houde Liu 외

The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. …

reinforcement-learning Reinforcement Learning

SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping

2026-06-17 · Xiaowen Hu, Linqi Ye, Yudi Zhu, Chenyue Shao 외 arxiv

Robotic jumping is pivotal in applications such as search and rescue and logistics, where crossing obstacles and enhancing mobility efficiency are critical. The Spring-Loaded Inverted Pendulum (SLIP) model leverages simp…

Reinforcement Learning