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Motion planning under sensing uncertainty is critical for robots inunstructured environments to guarantee safety for both the robot and any nearbyhumans. Most work on planning under uncertainty does not scale tohigh-dimensional robots such as manipulators, assumes simplified geometry ofthe robot or environment, or requires per-object knowledge of noise. Instead,we propose a method that directly models sensor-specific aleatoric uncertaintyto find safe motions for high-dimensional systems in complex environments,without exact knowledge of environment geometry. We combine a novel implicitneural model of stochastic signed distance functions with a hierarchicaloptimization-based motion planner to plan low-risk motions without sacrificingpath quality. Our method also explicitly bounds the risk of the path, offeringtrustworthiness. We empirically validate that our method produces safe motionsand accurate risk bounds and is safer than baseline approaches.

Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty