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Research questionHow can vision-language-action policies follow execution details beyond a robot task’s goal?Robot trajectories are often labeled only with coarse task goals, leaving choices such as the active arm, approach direction, and contact region unspecified. Without language grounding for these choices, policies are difficult to steer during otherwise successful task execution.
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Robotics
Latest papersRecent research connected to this question, newest first.FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action PoliciesThe source addresses vision-language-action policies for robotics using data unified from 10 open-source robot datasets, including 47,159 human-verified fine-grained trajectories. Evidence includes a 500-video benchmark, simulation results, and real-world dual-arm manipulation results; the source also describes scalable annotation and training with mixtures of fine-grained and raw goal-level instructions.research paper · Sep 2, 2026
Related questions
How can robotic vision-language-action models generalize across backbones without losing hierarchical manipulation structure?How can vision-language-action systems reliably execute long-horizon manipulation while tracking state and conditional dependencies?When should world-model imagination guide vision-language-action post-training to reduce costly real-world exploration without producing unreliable supervision?How can flow-based vision-language-action policies generate reliable robot actions with few sampling steps for real-time control?
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