# PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability

arXiv: https://arxiv.org/abs/2608.14082 · HTML: https://mlsanity.com/paper/2608.14082/

## 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 32 of its 51 references that are in the map.
- First posted on arXiv 14 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, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search](https://mlsanity.com/paper/1509.06791/) (2015)
- [Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight](https://mlsanity.com/paper/2403.12203/) (2024)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](https://mlsanity.com/paper/2010.11061/) (2020)
- [Fast, Autonomous Flight in GPS-Denied and Cluttered Environments](https://mlsanity.com/paper/1712.02052/) (2017)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing](https://mlsanity.com/paper/2210.14985/) (2022)
- [Learning Vision-based Flight in Drone Swarms by Imitation](https://mlsanity.com/paper/1908.02999/) (2019)
- [Motion Primitives-based Navigation Planning using Deep Collision Prediction](https://mlsanity.com/paper/2201.03254/) (2022)
- [Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments](https://mlsanity.com/paper/2209.01268/) (2022)
- [PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots](https://mlsanity.com/paper/2407.04224/) (2024)
- [Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved](https://mlsanity.com/paper/2110.02429/) (2021)
- [High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environments](https://mlsanity.com/paper/2402.19033/) (2024)
- [Collision Avoidance for Unmanned Aerial Vehicles in the Presence of Static and Moving Obstacles](https://mlsanity.com/paper/2106.13451/) (2021)
- [Sector Bounds for Vertical Cable Force Error in Cable-Suspended Load Transportation System](https://mlsanity.com/paper/2304.00193/) (2023)
- [Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along River](https://mlsanity.com/paper/2401.09332/) (2024)
- [Heuristic Predictive Control for Multi-Robot Flocking in Congested Environments](https://mlsanity.com/paper/2407.06812/) (2024)
- [Formation Control for Enclosing and Tracking via Relative Localization](https://mlsanity.com/paper/2410.14407/) (2024)
- [Autonomous Tail-Sitter Flights in Unknown Environments](https://mlsanity.com/paper/2411.15003/) (2024)

## Abstract (arXiv)

Autonomous navigation in cluttered environments is hampered by partial observability and dynamic constraints. This paper presents PILOT, a constraint-aware privileged imitation learning framework for vision-based end-to-end UAV motion planning under partial observability. The framework distills planning strategies from a computationally intensive optimal control expert into a student policy regularized toward safety and dynamic requirements via a dual-objective loss function. To mitigate partial observability, a spatiotemporal perception fusion module using a Temporal Convolutional Network (TCN) is developed to integrate historical depth images and odometry. This module infers task-relevant latent context from historical observations, enhancing spatial awareness beyond the instantaneous FOV without maintaining persistent map memory. A trajectory parameterization layer mapping network outputs to a structured trajectory, while enabling explicit continuity, dynamic-consistency, and obstacle soft penalties during training, encouraging constraint satisfaction for unseen observations without formal guarantees. Simulations on quadrotor and fixed-wing aircraft demonstrate that PILOT achieves performance comparable to the privileged expert while reducing computational overhead by over 80%. Successful indoor and outdoor zero-shot deployment confirms the practical feasibility and cross-domain generalization of the planner.