papers

Publications (5)

cs.RO2025

Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Lucy Xiaoyang Shi, Brian Ichter, Michael Equi +12

Generalist robots that can perform a range of different tasks in open-world settings must be able to not only reason about the steps needed to accomplish their goals, but also proc…

cs.LG2026

: 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…

cs.LG2025

: 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…

cs.LG2025

: 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…

cs.LG2026

: 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…