630 citations · 2.3k across the 46 of their papers we have counts for
6 papers · 1 filter
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…
Auto-Tuned Sim-to-Real Transfer
Yuqing Du, Olivia Watkins, Trevor Darrell +2
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual pr…
Instance-Aware Predictive Navigation in Multi-Agent Environments
Jinkun Cao, Xin Wang, Trevor Darrell +1
In this work, we aim to achieve efficient end-to-end learning of driving policies in dynamic multi-agent environments. Predicting and anticipating future events at the object level…
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
Xu Shen, Ivo Batkovic, Vijay Govindarajan +3
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneu…
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning
Richard Li, Allan Jabri, Trevor Darrell +1
Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements. Many practical tasks require…
Deep Object-Centric Representations for Generalizable Robot Learning
Coline Devin, Pieter Abbeel, Trevor Darrell +1
Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a met…