103 citations · 253 across the 13 of their papers we have counts for
8 papers · 1 filter
Learning Humanoid Locomotion over Challenging Terrain
Ilija Radosavovic, Sarthak Kamat, Trevor Darrell +1
Humanoid robots can, in principle, use their legs to go almost anywhere. Developing controllers capable of traversing diverse terrains, however, remains a considerable challenge. C…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
Humanoid Locomotion as Next Token Prediction
Ilija Radosavovic, Bike Zhang, Baifeng Shi +5
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive p…
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…
Robot Learning with Sensorimotor Pre-training
Ilija Radosavovic, Baifeng Shi, Letian Fu +3
We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a seq…
Real-World Humanoid Locomotion with Reinforcement Learning
Ilija Radosavovic, Tete Xiao, Bike Zhang +3
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new pl…
Real-World Robot Learning with Masked Visual Pre-training
Ilija Radosavovic, Tete Xiao, Stephen James +3
In this work, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our visual representations ar…
State-Only Imitation Learning for Dexterous Manipulation
Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto +1
Modern model-free reinforcement learning methods have recently demonstrated impressive results on a number of problems. However, complex domains like dexterous manipulation remain…