524 citations · 749 across the 14 of their papers we have counts for
5 papers · 1 filter
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal +48
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specif…
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
Kuang-Huei Lee, Ted Xiao, Adrian Li +3
The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement lear…
Token Turing Machines
Michael S. Ryoo, Keerthana Gopalakrishnan, Kumara Kahatapitiya +6
We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the semi…
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
Thomas Lew, Sumeet Singh, Mario Prats +11
We propose a framework to enable multipurpose assistive mobile robots to autonomously wipe tables to clean spills and crumbs. This problem is challenging, as it requires planning w…
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Michael Ahn, Anthony Brohan, Noah Brown +42
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extend…