# Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

arXiv: https://arxiv.org/abs/1904.11102 · HTML: https://mlsanity.com/paper/1904.11102/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 23 of 23)

- [Potential Based Diffusion Motion Planning](https://mlsanity.com/paper/2407.06169/) (2024)
- [Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2412.19948/) (2024)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Coarse-to-Fine Q-attention with Learned Path Ranking](https://mlsanity.com/paper/2204.01571/) (2022)
- [Imitating Graph-Based Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/2303.11166/) (2023)
- [Neural Field Dynamics Model for Granular Object Piles Manipulation](https://mlsanity.com/paper/2311.00802/) (2023)
- [PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms](https://mlsanity.com/paper/2105.01777/) (2021)
- [Physics-informed Temporal Difference Metric Learning for Robot Motion Planning](https://mlsanity.com/paper/2505.05691/) (2025)
- [Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding](https://mlsanity.com/paper/2210.08408/) (2022)
- [DiffCo: Auto-Differentiable Proxy Collision Detection with Multi-class Labels for Safety-Aware Trajectory Optimization](https://mlsanity.com/paper/2102.07413/) (2021)
- [Waypoint Planning Networks](https://mlsanity.com/paper/2105.00312/) (2021)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction](https://mlsanity.com/paper/2203.06963/) (2022)
- [Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner](https://mlsanity.com/paper/2208.11287/) (2022)
- [Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature](https://mlsanity.com/paper/2207.13181/) (2022)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [Combining Optimal Path Search With Task-Dependent Learning in a Neural Network](https://mlsanity.com/paper/2201.11104/) (2022)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [TSPDiffuser: Diffusion Models as Learned Samplers for Traveling Salesperson Path Planning Problems](https://mlsanity.com/paper/2406.02858/) (2024)
- [Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone Connectivity](https://mlsanity.com/paper/2410.24205/) (2024)
- [A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model](https://mlsanity.com/paper/2510.19128/) (2025)

## Abstract (arXiv)

Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot's actuation or configuration space, where popular pathfinding methods such as A*, RRT*, get exponentially more computationally expensive to execute as the dimensionality increases or the spaces become more cluttered and complex. On the other hand, if one were to save the entire set of paths connecting all pair of locations in the configuration space a priori, one would run out of memory very quickly. In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet. OracleNet uses Recurrent Neural Networks to determine end-to-end trajectories in an iterative manner that implicitly generates optimal motion plans with minimal loss in performance in a compact form. The algorithm is straightforward in implementation while consistently generating near-optimal paths in a single, iterative, end-to-end roll-out. In practice, OracleNet generally has fixed-time execution regardless of the configuration space complexity while outperforming popular pathfinding algorithms in complex environments and higher dimensions