# Neural Potential Field for Obstacle-Aware Local Motion Planning

arXiv: https://arxiv.org/abs/2310.16362 · HTML: https://mlsanity.com/paper/2310.16362/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 68 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021)
- [Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods](https://mlsanity.com/paper/2202.11762/) (2022)
- [acados: a modular open-source framework for fast embedded optimal control](https://mlsanity.com/paper/1910.13753/) (2019)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms](https://mlsanity.com/paper/2203.07747/) (2022)
- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [Theta*: Any-Angle Path Planning on Grids](https://mlsanity.com/paper/1401.3843/) (2014)
- [KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots](https://mlsanity.com/paper/2109.04821/) (2021)
- [Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search](https://mlsanity.com/paper/1707.01888/) (2017)
- [Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking](https://mlsanity.com/paper/2206.03305/) (2022)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers](https://mlsanity.com/paper/2212.11730/) (2022)
- [Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions](https://mlsanity.com/paper/2109.12313/) (2021)
- [Policy Optimization to Learn Adaptive Motion Primitives in Path Planning with Dynamic Obstacles](https://mlsanity.com/paper/2212.14307/) (2022)
- [TOAST: Trajectory Optimization and Simultaneous Tracking using Shared Neural Network Dynamics](https://mlsanity.com/paper/2201.08321/) (2022)
- [Differentiable Collision Avoidance Using Collision Primitives](https://mlsanity.com/paper/2204.09352/) (2022)
- [A Generalized Continuous Collision Detection Framework of Polynomial Trajectory for Mobile Robots in Cluttered Environments](https://mlsanity.com/paper/2206.13175/) (2022)
- [Safe Interval Path Planning With Kinodynamic Constraints](https://mlsanity.com/paper/2302.00776/) (2023)
- [An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance](https://mlsanity.com/paper/1909.08267/) (2019)
- [CIAO^star: MPC-based Safe Motion Planning in Predictable Dynamic Environments](https://mlsanity.com/paper/2001.05449/) (2020)
- [GraphDistNet: A Graph-based Collision-distance Estimator for Gradient-based Trajectory Optimization](https://mlsanity.com/paper/2206.01517/) (2022)
- [Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw](https://mlsanity.com/paper/2103.06141/) (2021)
- [MPC with Sensor-Based Online Cost Adaptation](https://mlsanity.com/paper/2209.09451/) (2022)

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

- [CoFL: Continuous Flow Fields for Language-Conditioned Navigation](https://mlsanity.com/paper/2603.02854/) (2026)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [AeroDPO: Unleashing Lightweight UAV Navigation with High-Fidelity Perception and Automated Preference Optimization](https://mlsanity.com/paper/2608.07557/) (2026)
- [Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles](https://mlsanity.com/paper/2410.06819/) (2024)

## Abstract (arXiv)

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://github.com/cog-isa/NPField together with demo video.