8 papers
VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation
Victor Kowalski, Chengxi Li, Dongheui Lee
When using reinforcement learning (RL) for contact-rich robotic manipulation, vision can provide task-relevant information that accelerates learning beyond what proprioception alon…
DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
Dongmyoung Lee, Chengxi Li, Dongheui Lee
Dexterous teleoperation via Mixed Reality (MR)-based interfaces offers a scalable paradigm for transferring human manipulation skills to dexterous robot hands. However, conventiona…
Efficiently Learning Robust Torque-based Locomotion Through Reinforcement with Model-Based Supervision
Yashuai Yan, Tobias Egle, Christian Ott +1
We propose a control framework that integrates model-based bipedal locomotion with residual reinforcement learning (RL) to achieve robust and adaptive walking in the presence of re…
Learning a Unified Latent Space for Cross-Embodiment Robot Control
Yashuai Yan, Dongheui Lee
We present a scalable framework for cross-embodiment humanoid robot control by learning a shared latent representation that unifies motion across humans and diverse humanoid platfo…
Variable Stiffness for Robust Locomotion through Reinforcement Learning
Dario Spoljaric, Yashuai Yan, Dongheui Lee
Reinforcement-learned locomotion enables legged robots to perform highly dynamic motions but often accompanies time-consuming manual tuning of joint stiffness. This paper introduce…
I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
Yashuai Yan, Esteve Valls Mascaro, Tobias Egle +1
Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical, physical execution remains a significant challeng…