10 papers
VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
Jean Mercat, Sedrick Keh, Kushal Arora +5
We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage,…
Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison
David Snyder, Apurva Badithela, Nikolai Matni +4
Generalist robot manipulation policies are becoming increasingly capable, but are limited in evaluation to a small number of hardware rollouts. This strong resource constraint in r…
Impact of Different Failures on a Robot's Perceived Reliability
Andrew Violette, Zhanxin Wu, Haruki Nishimura +5
Robots fail, potentially leading to a loss in the robot's perceived reliability (PR), a measure correlated with trustworthiness. In this study we examine how various kinds of failu…
A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
Fanqi Lin, Kushal Arora, Jean Mercat +9
Large behavior models have shown strong dexterous manipulation capabilities by extending imitation learning to large-scale training on multi-task robot data, yet their generalizati…
SAFE: Multitask Failure Detection for Vision-Language-Action Models
Qiao Gu, Yuanliang Ju, Shengxiang Sun +4
While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on no…
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…