PAC: Risk-Aware Planner-Augmented Control for Safe Manipulation in Dynamic Environments

Dynamic obstacles create a fundamental challenge for robotic manipulation: the controller must react rapidly to imminent collisions without losing progress toward the task goal. Motion planners provide structured and goal-consistent references, but their solutions can quickly become outdated when the environment changes during execution. In contrast, learned reactive policies can respond to local disturbances, yet may produce unstable or excessively conservative actions without global guidance. We introduce Planner-Augmented Control (PAC), a hybrid framework that continuously combines planner-generated references with learned reactive corrections. A differentiable, risk-aware gate adjusts their relative contributions online, favoring nominal trajectory tracking in low-risk states and stronger corrective behavior as collision risk increases. PAC further incorporates a lightweight model-predictive safety shield that evaluates short-horizon motion and modifies potentially unsafe commands before execution. The framework is trained through a two-stage procedure consisting of behavior-cloning initialization and danger-aware optimization with explicit gate supervision. We evaluate PAC on a dynamic manipulation benchmark covering static obstacles, moving obstacles, sudden obstacle appearance, goal-blocking interactions, and out-of-distribution conditions. Experimental results show that PAC preserves reliable goal-directed behavior in static scenes while improving collision avoidance and task completion under dynamic disturbances compared with classical planning and purely learning-based baselines. These results demonstrate that risk-aware planner–policy fusion provides an effective approach to robust manipulation in changing environments.