11 papers
SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping
Xiaowen Hu, Linqi Ye, Yudi Zhu +4
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 Inver…
Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
Yulai Zhang, Yinrong Zhang, Ting Wu +1
Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, as well ascontrol switching iss…
Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior
Yuanye Wu, Keyi Wang, Linqi Ye +1
Learning diverse locomotion skills for humanoid robots in a unified reinforcement learning framework remains challenging due to the conflicting requirements of stability and dynami…
A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models
Yifei Yan, Yankai Liao, Linqi Ye
Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model acquisition, and model trai…
Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning
Kaixuan Chen, Linqi Ye
With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment configuration. Existing centr…
Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks
Yihan Zhang, Ziyun Huang, Linqi Ye
Collecting embodied interaction data at scale remains costly and difficult due to the limited accessibility of conventional interfaces. We present a gamified data collection framew…
Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots
Yifei Yan, Linqi Ye
As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become…
PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors
Chenxi Han, Shilu He, Yi Cheng +2
Training perceptive humanoid locomotion policies that traverse complex terrains with natural gaits remains an open challenge, typically demanding multi-stage training pipelines, ad…
CTBC: Contact-Triggered Blind Climbing for Wheeled Bipedal Robots with Instruction Learning and Reinforcement Learning
Rankun Li, Hao Wang, Qi Li +5
In recent years, wheeled bipedal robots have garnered significant attention due to their exceptional mobility on flat terrain. However, while stair climbing has been achieved in pr…
Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots
Linqi Ye, Rankun Li, Xiaowen Hu +4
This paper introduces Unity RL Playground, an open-source reinforcement learning framework built on top of Unity ML-Agents. Unity RL Playground automates the process of training mo…
MBC: Multi-Brain Collaborative Control for Quadruped Robots
Hang Liu, Yi Cheng, Rankun Li +3
In the field of locomotion task of quadruped robots, Blind Policy and Perceptive Policy each have their own advantages and limitations. The Blind Policy relies on preset sensor inf…