Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots
Overview
This paper introduces PS-PRM, a GPU-parallelized perception-score-guided probabilistic roadmap planner that jointly handles motion planning and continuous perception constraints (e.g., object detection, human activity recognition) for high-degree-of-freedom robots in static and dynamic environments. The method uses a neural surrogate model to approximate perception scores, integrates them into roadmap search, and enables efficient online replanning. Experiments in simulation and on real robots show that PS-PRM outperforms reinforcement learning and trajectory-optimization baselines.
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Environment: simulation
“nto roadmap planning, and leverages GPU parallelism for efficient online replanning. We demonstrate that our planner outperforms RL- and trajectory-optimization-based baselines in static and dynamic environments in simulation and real-robot experiments.”
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- arXiv Robotics — research abstractsSignalLook as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots
This paper introduces PS-PRM, a GPU-parallelized perception-score-guided probabilistic roadmap planner that jointly handles motion planning and continuous perception constraints (e.g., object detection, human activity recognition) for high-degree-of-freedom robots in static and dynamic environments. The method uses a neural surrogate model to approximate perception scores, integrates them into roadmap search, and enables efficient online replanning. Experiments in simulation and on real robots show that PS-PRM outperforms reinforcement learning and trajectory-optimization baselines.
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