103 citations · 117 across the 10 of their papers we have counts for
12 papers
WAMJET: A Harness for World Action Model Acceleration
Le Chen, Lixin Liu, Jan Schneider +4
World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-prediction are expensive. Although exis…
Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
Jan Schneider, Mridul Mahajan, Le Chen +4
Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice du…
Dexterous Robotic Piano Playing at Scale
Le Chen, Yi Zhao, Jan Schneider +7
Endowing robot hands with human-level dexterity has been a long-standing goal in robotics. Bimanual robotic piano playing represents a particularly challenging task: it is high-dim…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Identifying Policy Gradient Subspaces
Jan Schneider, Pierre Schumacher, Simon Guist +4
Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optim…