# Learning Obstacle Representations for Neural Motion Planning

arXiv: https://arxiv.org/abs/2008.11174 · HTML: https://mlsanity.com/paper/2008.11174/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation](https://mlsanity.com/paper/2309.15596/) (2023)
- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation](https://mlsanity.com/paper/2111.06383/) (2021)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding](https://mlsanity.com/paper/2210.08408/) (2022)
- [Predicting Motion Plans for Articulating Everyday Objects](https://mlsanity.com/paper/2303.01484/) (2023)
- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)
- [Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space](https://mlsanity.com/paper/2303.03364/) (2023)
- [A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction](https://mlsanity.com/paper/2303.12274/) (2023)
- [RoboArm-NMP: a Learning Environment for Neural Motion Planning](https://mlsanity.com/paper/2405.16335/) (2024)
- [SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning](https://mlsanity.com/paper/2411.17293/) (2024)
- [Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields](https://mlsanity.com/paper/2507.09383/) (2025)

## Abstract (arXiv)

Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based motion planning in new and dynamic environments remains difficult. In this work we address sensor-based motion planning from a learning perspective. Motivated by recent advances in visual recognition, we argue the importance of learning appropriate representations for motion planning. We propose a new obstacle representation based on the PointNet architecture and train it jointly with policies for obstacle avoidance. We experimentally evaluate our approach for rigid body motion planning in challenging environments and demonstrate significant improvements of the state of the art in terms of accuracy and efficiency.