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Urgent Actions Go First: Urgency-Aware Denoising for Real-Time VLA Control
Zibo Wang, Haochen Han, Pengzhen Ren +2
Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference latency that severely limits r…
Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation
Yongjie Bai, Hanting Wang, Mingtong Dai +5
General-purpose VLA models leverage large-scale vision-language priors to understand scenes and instructions, but primarily generate actions directly from the current observation.…
SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
Mingtong Dai, Guanqi Peng, Yongjie Bai +5
Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact-rich operation phases. This uniform treatment…
GraspView: Active Perception Scoring and Best-View Optimization for Robotic Grasping in Cluttered Environments
Shenglin Wang, Mingtong Dai, Jingxuan Su +4
Robotic grasping is a fundamental capability for autonomous manipulation, yet remains highly challenging in cluttered environments where occlusion, poor perception quality, and inc…
RoVer: Robot Reward Model as Test-Time Verifier for Vision-Language-Action Model
Mingtong Dai, Lingbo Liu, Yongjie Bai +6
Vision-Language-Action (VLA) models have become a prominent paradigm for embodied intelligence, yet further performance improvements typically rely on scaling up training data and…
Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation
Yongjie Bai, Zhouxia Wang, Yang Liu +8
Recent vision-language-action (VLA) models for multi-task robot manipulation often rely on fixed camera setups and shared visual encoders, which limit their performance under occlu…