14 citations · 27 across the 10 of their papers we have counts for
10 papers
RL Token: Bootstrapping Online RL with Vision-Language-Action Models
Charles Xu, Jost Tobias Springenberg, Michael Equi +4
Vision-language-action (VLA) models can learn to perform diverse manipulation skills "out of the box," but achieving the precision and speed that real-world tasks demand requires f…
: 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 i…
MEM: Multi-Scale Embodied Memory for Vision Language Action Models
Marcel Torne, Karl Pertsch, Homer Walke +14
Conventionally, memory in end-to-end robotic learning involves inputting a sequence of past observations into the learned policy. However, in complex multi-stage real-world tasks,…
Training-Time Action Conditioning for Efficient Real-Time Chunking
Kevin Black, Allen Z. Ren, Michael Equi +1
Real-time chunking (RTC) enables vision-language-action models (VLAs) to generate smooth, reactive robot trajectories by asynchronously predicting action chunks and conditioning on…
: 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…
: 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…
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…
: 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…
Precise Object Placement Using Force-Torque Feedback
Osher Lerner, Zachary Tam, Michael Equi
Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, de…
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Dhruv Shah, Michael Equi, Blazej Osinski +3
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly di…