2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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,…
Structured World Models from Human Videos
Russell Mendonca, Shikhar Bahl, Deepak Pathak
We tackle the problem of learning complex, general behaviors directly in the real world. We propose an approach for robots to efficiently learn manipulation skills using only a han…
Affordances from Human Videos as a Versatile Representation for Robotics
Shikhar Bahl, Russell Mendonca, Lili Chen +2
Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it…
ALAN: Autonomously Exploring Robotic Agents in the Real World
Russell Mendonca, Shikhar Bahl, Deepak Pathak
Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While i…