2 citations · 5 across the 9 of their papers we have counts for
4 papers · 1 filter
: 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…
OmniVLA: An Omni-Modal Vision-Language-Action Model for Robot Navigation
Noriaki Hirose, Catherine Glossop, Dhruv Shah +1
Humans can flexibly interpret and compose different goal specifications, such as language instructions, spatial coordinates, or visual references, when navigating to a destination.…
CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models
Catherine Glossop, William Chen, Arjun Bhorkar +2
Generalist robots should be able to understand and follow user instructions. Despite providing a powerful architecture for mapping open-vocabulary language instructions to robot ac…
Learning to Drive Anywhere with Model-Based Reannotation
Noriaki Hirose, Lydia Ignatova, Kyle Stachowicz +3
Developing broadly generalizable visual navigation policies for robots is a significant challenge, primarily constrained by the availability of large-scale, diverse training data.…