46 citations · 62 across the 16 of their papers we have counts for
5 papers · 1 filter
EgoMI: Learning Active Vision and Whole-Body Manipulation from Egocentric Human Demonstrations
Justin Yu, Yide Shentu, Di Wu +3
Imitation learning from human demonstrations offers a promising approach for robot skill acquisition, but egocentric human data introduces fundamental challenges due to the embodim…
SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
Qianzhong Chen, Justin Yu, Mac Schwager +3
Large-scale robot learning has made progress on complex manipulation tasks, yet long horizon, contact rich problems, especially those involving deformable objects, remain challengi…
Train Robots in a JIF: Joint Inverse and Forward Dynamics with Human and Robot Demonstrations
Gagan Khandate, Boxuan Wang, Sarah Park +7
Pre-training on large datasets of robot demonstrations is a powerful technique for learning diverse manipulation skills but is often limited by the high cost and complexity of coll…
VibeCheck: Using Active Acoustic Tactile Sensing for Contact-Rich Manipulation
Kaidi Zhang, Do-Gon Kim, Eric T. Chang +6
The acoustic response of an object can reveal a lot about its global state, for example its material properties or the extrinsic contacts it is making with the world. In this work,…
RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation
Philipp Wu, Yide Shentu, Qiayuan Liao +5
Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demon…