TRACE: Target-State Risk-Aligned Conditional Execution for Embodied Tracking
Overview
This research presents TRACE, a method for embodied visual tracking that balances persistence with safety. By updating only the planner head of a frozen 0.6B vision-language-action (VLA) model with proximal policy optimization (PPO), the authors improve tracking success but observe increased collisions due to continued pursuit under distraction. TRACE addresses this by introducing an execution-time risk gate that processes 84 cues and rescales translational waypoints when calibrated risk exceeds a threshold, leaving yaw unchanged. In paired evaluations, TRACE reduces the distracted-tracking collision rate by 3.96 percentage points while further improving success and following rates, demonstrating that target-state-aware execution can recover safety costs with minimal intervention.
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- arXiv Robotics — research abstractsSignalTRACE: Target-State Risk-Aligned Conditional Execution for Embodied Tracking
This research presents TRACE, a method for embodied visual tracking that balances persistence with safety. By updating only the planner head of a frozen 0.6B vision-language-action (VLA) model with proximal policy optimization (PPO), the authors improve tracking success but observe increased collisions due to continued pursuit under distraction. TRACE addresses this by introducing an execution-time risk gate that processes 84 cues and rescales translational waypoints when calibrated risk exceeds a threshold, leaving yaw unchanged. In paired evaluations, TRACE reduces the distracted-tracking collision rate by 3.96 percentage points while further improving success and following rates, demonstrating that target-state-aware execution can recover safety costs with minimal intervention.
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