# Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight

arXiv: https://arxiv.org/abs/2608.20948 · HTML: https://mlsanity.com/paper/2608.20948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 11 of 11)

- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [Geometrically Constrained Trajectory Optimization for Multicopters](https://mlsanity.com/paper/2103.00190/) (2021)
- [Deep Learning for Optimization of Trajectories for Quadrotors](https://mlsanity.com/paper/2309.15191/) (2023)
- [FAPP: Fast and Adaptive Perception and Planning for UAVs in Dynamic Cluttered Environments](https://mlsanity.com/paper/2312.08743/) (2023)
- [Constrained Imitation Learning for a Flapping Wing Unmanned Aerial Vehicle](https://mlsanity.com/paper/2206.03987/) (2022)
- [LiCS: Navigation using Learned-imitation on Cluttered Space](https://mlsanity.com/paper/2406.14947/) (2024)
- [Primitive-Swarm: An Ultra-lightweight and Scalable Planner for Large-scale Aerial Swarms](https://mlsanity.com/paper/2502.16887/) (2025)
- [BPMP-Tracker: A Versatile Aerial Target Tracker Using Bernstein Polynomial Motion Primitives](https://mlsanity.com/paper/2408.04266/) (2024)

## Abstract (arXiv)

Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this paper, we propose an efficient end-to-end local planner via imitation learning. A lightweight offline-primitive-based dataset collection framework is designed to produce safe and high-quality trajectory primitives in non-convex environments. A compact neural network directly maps sensory inputs to polynomial coefficients that inherently encode higher-order dynamical information. The learned policy generates smooth, empirically collision-free and dynamically feasible trajectories in real time without back-end solving. It achieves ultra-fast computation (below 1ms on a standard desktop and average 3.68ms during onboard flight), while maintaining low onboard memory requirements (less than 1.5MiB). Extensive simulation benchmarks demonstrate superiority in both planning latency and target-reaching progress quality. Zero-shot deployment in real-world experiments further validates the robust sim-to-real transfer capability of the proposed method.