# Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

arXiv: https://arxiv.org/abs/2210.08408 · HTML: https://mlsanity.com/paper/2210.08408/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Generalization and Representational Limits of Graph Neural Networks](https://mlsanity.com/paper/2002.06157/) (2020)
- [Value Iteration Networks](https://mlsanity.com/paper/1602.02867/) (2016)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning](https://mlsanity.com/paper/2011.13219/) (2020)
- [Robot Motion Planning in Learned Latent Spaces](https://mlsanity.com/paper/1807.10366/) (2018)
- [Gated Path Planning Networks](https://mlsanity.com/paper/1806.06408/) (2018)
- [Graph Policy Gradients for Large Scale Robot Control](https://mlsanity.com/paper/1907.03822/) (2019)
- [Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation](https://mlsanity.com/paper/1904.11102/) (2019)
- [Harnessing Reinforcement Learning for Neural Motion Planning](https://mlsanity.com/paper/1906.00214/) (2019)
- [Learning a Decentralized Multi-arm Motion Planner](https://mlsanity.com/paper/2011.02608/) (2020)
- [Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees](https://mlsanity.com/paper/1903.00070/) (2019)
- [Learning-Based Proxy Collision Detection for Robot Motion Planning Applications](https://mlsanity.com/paper/1902.08164/) (2019)
- [Generalized Value Iteration Networks: Life Beyond Lattices](https://mlsanity.com/paper/1706.02416/) (2017)
- [Learning Implicit Sampling Distributions for Motion Planning](https://mlsanity.com/paper/1806.01968/) (2018)
- [Learning Obstacle Representations for Neural Motion Planning](https://mlsanity.com/paper/2008.11174/) (2020)
- [Graph Neural Networks for Motion Planning](https://mlsanity.com/paper/2006.06248/) (2020)
- [Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning](https://mlsanity.com/paper/2012.14906/) (2020)
- [CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces](https://mlsanity.com/paper/2201.09467/) (2022)

## Built on by (most cited first, 5 of 5)

- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)
- [Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality](https://mlsanity.com/paper/2301.08451/) (2023)
- [Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization](https://mlsanity.com/paper/2502.05526/) (2025)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning](https://mlsanity.com/paper/2411.17293/) (2024)

## Abstract (arXiv)

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles and reason about temporal-spatial interactions in very large state spaces. We propose a GNN-based approach that uses temporal encoding and imitation learning with data aggregation for learning both the embeddings and the edge prioritization policies. Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms. The learned models can often reduce costly collision checking operations by more than 1000x, and thus accelerating planning by up to 95%, while achieving high success rates on hard instances as well.