1 citations · 2 across the 11 of their papers we have counts for
11 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 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…
HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
Esteve Valls Mascaro, Daniel Sliwowski, Dongheui Lee
Robots are becoming increasingly integrated into our lives, assisting us in various tasks. To ensure effective collaboration between humans and robots, it is essential that they un…
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…