161 citations · 468 across the 27 of their papers we have counts for
9 papers · 1 filter
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
Xingyu Liu, Deepak Pathak, Kris M. Kitani
The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the…
Legged Locomotion in Challenging Terrains using Egocentric Vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik +1
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…
Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion
Zipeng Fu, Xuxin Cheng, Deepak Pathak
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. Th…
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…
Adapting Rapid Motor Adaptation for Bipedal Robots
Ashish Kumar, Zhongyu Li, Jun Zeng +3
Recent advances in legged locomotion have enabled quadrupeds to walk on challenging terrains. However, bipedal robots are inherently more unstable and hence it's harder to design w…
RB2: Robotic Manipulation Benchmarking with a Twist
Sudeep Dasari, Jianren Wang, Joyce Hong +12
Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research g…
Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube
Aravind Sivakumar, Kenneth Shaw, Deepak Pathak
We build a system that enables any human to control a robot hand and arm, simply by demonstrating motions with their own hand. The robot observes the human operator via a single RG…
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
Xingyu Liu, Deepak Pathak, Kris M. Kitani
A popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but also often impractical for complex robots. In this wo…
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak +1
Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks,…