1 citations · 2 across the 11 of their papers we have counts for
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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 complements robot proprioception. However, vision-en…
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…
Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region
Tobias Egle, Yashuai Yan, Dongheui Lee +1
This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturba…
Know your limits! Optimize the robot's behavior through self-awareness
Esteve Valls Mascaro, Dongheui Lee
As humanoid robots transition from labs to real-world environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus…
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…
Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
Esteve Valls Mascaro, Yashuai Yan, Dongheui Lee
Integrating robots into populated environments is a complex challenge that requires an understanding of human social dynamics. In this work, we propose to model social motion forec…
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space
Yashuai Yan, Esteve Valls Mascaro, Dongheui Lee
This paper introduces a novel deep-learning approach for human-to-robot motion retargeting, enabling robots to mimic human poses accurately. Contrary to prior deep-learning-based w…