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

arXiv: https://arxiv.org/abs/2309.16862 · HTML: https://mlsanity.com/paper/2309.16862/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [Neural Configuration-Space Barriers for Manipulation Planning and Control](https://mlsanity.com/paper/2503.04929/) (2025)
- [From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representations](https://mlsanity.com/paper/2509.23555/) (2025)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation](https://mlsanity.com/paper/2509.20681/) (2025)

## Abstract (arXiv)

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