103 citations · 152 across the 20 of their papers we have counts for
22 papers · 1 filter
MessyMem: Learning-from-Doing Memory for Mobile Manipulation
Anuva Banwasi, William Muckelroy, Priya Sundaresan +3
Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an object in a drawer, the robot sho…
VIA: Visual Interface Agent for Robot Control
Hengyuan Hu, Priya Sundaresan, Jensen Gao +1
Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control. General-purpose foundation models (FMs) have grown r…
HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao +7
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained m…
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…
Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning
Juntao Ren, Priya Sundaresan, Dorsa Sadigh +2
Teaching robots to autonomously complete everyday tasks remains a challenge. Imitation Learning (IL) is a powerful approach that imbues robots with skills via demonstrations, but i…
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…