5 papers
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…