Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Successful real-world deployment of legged robots would require them to adapt in real-time
to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper …

An attached arm can significantly increase the applicability of legged robots to several
mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. The …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

Deep reinforcement learning (RL) agents often fail to generalize to unseen environments
(yet semantically similar to trained agents), particularly when they are trained on high …

We exploit the complementary strengths of vision and proprioception to develop a point-goal
navigation system for legged robots, called VP-Nav. Legged systems are capable of …

Abstract Model-based reinforcement learning (RL) enjoys several benefits, such as data-
efficiency and planning, by learning a model of the environment's dynamics. However …

Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets,
pose particular challenges for future home-assistant robots performing daily tasks in human …

The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn
new tasks by leveraging prior experience on related tasks. Learning a new task often …