5 citations · 11 across the 11 of their papers we have counts for
6 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…
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Steven Oh, Jason Jingzhou Liu, Tony Tao +5
Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT),…
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…
Local Policies Enable Zero-shot Long-horizon Manipulation
Murtaza Dalal, Min Liu, Walter Talbott +4
Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we in…
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…
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation
Quanting Xie, So Yeon Min, Pengliang Ji +7
There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented genera…