# Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

arXiv: https://arxiv.org/abs/2008.05112 · HTML: https://mlsanity.com/paper/2008.05112/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [Neural MP: A Generalist Neural Motion Planner](https://mlsanity.com/paper/2409.05864/) (2024)
- [Motion Planning Transformers: A Motion Planning Framework for Mobile Robots](https://mlsanity.com/paper/2106.02791/) (2021)
- [Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments](https://mlsanity.com/paper/2509.06953/) (2025)
- [MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints](https://mlsanity.com/paper/2101.06798/) (2021)
- [Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers](https://mlsanity.com/paper/2306.00851/) (2023)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Waypoint Planning Networks](https://mlsanity.com/paper/2105.00312/) (2021)
- [Learning Continuous Cost-to-Go Functions for Non-holonomic Systems](https://mlsanity.com/paper/2103.11168/) (2021)

## Abstract (arXiv)

Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.