5 papers
VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
Dhia Naouali, Minghan Wu, Claudia Wong +2
Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away much of th…
Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation
Jacob Dang, Brian Y. Xie, Omar G. Younis
Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those traits. However, it remains u…
CUBE: A Standard for Unifying Agent Benchmarks
Alexandre Lacoste, Nicolas Gontier, Oleh Shliazhko +23
The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires substantial custom integration, creating…
Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search
Cyrus Neary, Omar G. Younis, Artur Kuramshin +2
Pre-trained vision-language-action (VLA) models offer a promising foundation for generalist robot policies, but often produce brittle behaviors or unsafe failures when deployed zer…
Emergent World Representations in OpenVLA
Marco Molinari, Leonardo Nevali, Saharsha Navani +1
Vision Language Action models (VLAs) trained with policy-based reinforcement learning (RL) encode complex behaviors without explicitly modeling environmental dynamics. However, it…