TRACE: Target-State Risk-Aligned Conditional Execution for Embodied Tracking

Embodied visual tracking requires a robot to follow a language-specified person under occlusion, look-alike distractors, and ambiguous instructions. We show that improving tracking persistence can simultaneously increase safety violations. Using a compact $0.6$B vision-language-action (VLA) tracker with a frozen backbone, we update only the planner head with proximal policy optimization (PPO). PPO improves success and following rates across all three EVT-Bench settings, but increases the collision rate on distracted tracking because episodes that previously ended in target loss can instead end in contact. We introduce , an execution-time target-state risk gate that preserves the learned policy and intervenes selectively. A lightweight risk head processes $84$ cues, including candidate boxes, identity margins, a learned range-and-bearing probe, and the policy's commands. When the calibrated risk exceeds a threshold, rescales the translational component of the next waypoint while leaving yaw unchanged. The executed action is identical to PPO's on more than $98\%$ of steps. In paired evaluations on identical episodes, reduces the distracted-tracking collision rate by $3.96$ percentage points, while further improving success and following rates. Matched slowdown controls without target-state timing do not reproduce this improvement. Concurrent evaluation reproduces a collision reduction of $3.77$ points, compared with $0.29$ points for identical-arm null pairs, indicating that the effect exceeds evaluation variability. These results show that target-state-aware execution can recover the safety cost of policy post-training with minimal intervention.