2 citations · 3 across the 6 of their papers we have counts for
6 papers
HRP: Human Affordances for Robotic Pre-Training
Mohan Kumar Srirama, Sudeep Dasari, Shikhar Bahl +1
In order to *generalize* to various tasks in the wild, robotic agents will need a suitable representation (i.e., vision network) that enables the robot to predict optimal actions g…
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…
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…
Human-to-Robot Imitation in the Wild
Shikhar Bahl, Abhinav Gupta, Deepak Pathak
We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real worl…