2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
COWBOY: A Scalable Sim-to-Real Framework for Learning Contextual Whole-Body Manipulation
Jiahui Yang, Jason Jingzhou Liu, Ray Muxin Liu +4
Mobile manipulators must adapt not only to objects, but also to the spatial context in which they appear: where to position the base, how to manipulate, and where to look while nav…
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…
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…
Neural MP: A Generalist Neural Motion Planner
Murtaza Dalal, Jiahui Yang, Russell Mendonca +3
The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. For complex…