18 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Efficient RL via Disentangled Environment and Agent Representations
Kevin Gmelin, Shikhar Bahl, Russell Mendonca +1
Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input. We propose an a…
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…