Training VLAs to perform embodied chain-of-thought reasoning about plans, sub-tasks, motions, and grounded visual features before acting raises OpenVLA success rates by 28% on challenging generalization tasks without new robot data.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2024 2roles
background 1polarities
background 1representative citing papers
MirrorCheck detects adversarial attacks on VLMs via T2I regeneration for semantic consistency checks, using stochastic model selection and one-time perturbations for robustness against adaptive attacks.
citing papers explorer
-
Robotic Control via Embodied Chain-of-Thought Reasoning
Training VLAs to perform embodied chain-of-thought reasoning about plans, sub-tasks, motions, and grounded visual features before acting raises OpenVLA success rates by 28% on challenging generalization tasks without new robot data.
-
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
MirrorCheck detects adversarial attacks on VLMs via T2I regeneration for semantic consistency checks, using stochastic model selection and one-time perturbations for robustness against adaptive attacks.