# RRT*former: Environment-Aware Sampling-Based Motion Planning using Transformer

arXiv: https://arxiv.org/abs/2511.15414 · HTML: https://mlsanity.com/paper/2511.15414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [Differentiable Spatial Planning using Transformers](https://mlsanity.com/paper/2112.01010/) (2021)
- [STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems](https://mlsanity.com/paper/1809.08345/) (2018)
- [When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention](https://mlsanity.com/paper/2407.20042/) (2024)
- [Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees](https://mlsanity.com/paper/1903.00070/) (2019)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges](https://mlsanity.com/paper/2402.13075/) (2024)
- [An Abstraction-Free Method for Multi-Robot Temporal Logic Optimal Control Synthesis](https://mlsanity.com/paper/1909.00526/) (2019)
- [Motion Planning Transformers: A Motion Planning Framework for Mobile Robots](https://mlsanity.com/paper/2106.02791/) (2021)
- [Potential Functions based Sampling Heuristic For Optimal Path Planning](https://mlsanity.com/paper/1704.00264/) (2017)
- [Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers](https://mlsanity.com/paper/2306.00851/) (2023)
- [Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments](https://mlsanity.com/paper/1807.08325/) (2018)
- [Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints](https://mlsanity.com/paper/2309.14595/) (2023)
- [Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2501.13457/) (2025)
- [KG-Planner: Knowledge-Informed Graph Neural Planning for Collaborative Manipulators](https://mlsanity.com/paper/2405.07962/) (2024)
- [Sampling-Based Optimal Control Synthesis for Multi-Robot Systems under Global Temporal Tasks](https://mlsanity.com/paper/1706.04216/) (2017)
- [NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots](https://mlsanity.com/paper/2309.14050/) (2023)

## Abstract (arXiv)

We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called RRT*former, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.