Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots

摘要

Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived, with many of them explicitly requiring objects or persons to be within view, i.e., for monitoring or safety. However, solving motion and perception tasks simultaneously is challenging, as these objectives often impose conflicting requirements. Furthermore, while robots must react quickly to changes in the environment, directly evaluating the quality of perception (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe and reliable operation. In this work, we address the challenge of solving motion planning problems for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints under both static and dynamic environments. We propose a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model (PS-PRM). Unlike existing active perception-, visibility-aware or learning-based planners, our work integrates perception tasks and constraints directly into the motion planning formulation. Our method uses a neural surrogate model to approximate perception scores, incorporates them into the roadmap, and leverages GPU parallelism to enable efficient online replanning in dynamic settings. We demonstrate that our planner, evaluated on high-DoF robots, outperforms baseline methods in both static and dynamic environments in both simulation and real-robot experiments.