6 papers
: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Physical Intelligence, Bo Ai, Ali Amin +85
We present a new robotic foundation model, called , that can enable strong out-of-the-box performance in a wide range of scenarios. can follow diverse language…
: A Vision-Language-Action Flow Model for General Robot Control
Kevin Black, Noah Brown, Danny Driess +21
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artif…
: a VLA That Learns From Experience
Physical Intelligence, Ali Amin, Raichelle Aniceto +53
We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience…
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…
: a Vision-Language-Action Model with Open-World Generalization
Physical Intelligence, Kevin Black, Noah Brown +33
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated im…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…