5 citations · 11 across the 11 of their papers we have counts for
12 papers
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…
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Yunhai Han, Jianuo Qiu, Linhao Bai +14
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception e…
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),…
IFG: Internet-Scale Guidance for Functional Grasping Generation
Ray Muxin Liu, Mingxuan Li, Kenneth Shaw +1
Large Vision Models trained on internet-scale data have demonstrated strong capabilities in segmenting and semantically understanding object parts, even in cluttered, crowded scene…
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…
DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies
Tony Tao, Mohan Kumar Srirama, Jason Jingzhou Liu +2
Large-scale, diverse robot datasets have emerged as a promising path toward enabling dexterous manipulation policies to generalize to novel environments, but acquiring such dataset…
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…
SPIN: Simultaneous Perception, Interaction and Navigation
Shagun Uppal, Ananye Agarwal, Haoyu Xiong +2
While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or sta…
Adaptive Mobile Manipulation for Articulated Objects In the Open World
Haoyu Xiong, Russell Mendonca, Kenneth Shaw +1
Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings,…
Dexterous Functional Grasping
Ananye Agarwal, Shagun Uppal, Kenneth Shaw +1
While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real wo…
DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
Aditya Kannan, Kenneth Shaw, Shikhar Bahl +2
Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper,…