Internal attention heads in VLA policies localize targets for a CBF safety filter that enables real-time collision avoidance with dynamic obstacles and outperforms init-time oracle identification by 43% on average.
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PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
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Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
Internal attention heads in VLA policies localize targets for a CBF safety filter that enables real-time collision avoidance with dynamic obstacles and outperforms init-time oracle identification by 43% on average.
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Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.