1 citations · 1 across the 3 of their papers we have counts for
4 papers
Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments
Jiahui Yang, Jason Jingzhou Liu, Yulong Li +3
Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally o…
TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking
Mengmeng Wang, Haonan Wang, Yulong Li +4
3D LiDAR-based single object tracking (SOT) relies on sparse and irregular point clouds, posing challenges from geometric variations in scale, motion patterns, and structural compl…
FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
Jason Jingzhou Liu, Yulong Li, Kenneth Shaw +3
Many contact-rich tasks humans perform, such as box pickup or rolling dough, rely on force feedback for reliable execution. However, this force information, which is readily availa…
Bimanual Dexterity for Complex Tasks
Kenneth Shaw, Yulong Li, Jiahui Yang +5
To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one…