# Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments

arXiv: https://arxiv.org/abs/2603.07578 · HTML: https://mlsanity.com/paper/2603.07578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 40 of its 57 references that are in the map.
- First posted on arXiv 8 Mar 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 40)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics](https://mlsanity.com/paper/2407.10648/) (2024)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [Deep Drone Racing: Learning Agile Flight in Dynamic Environments](https://mlsanity.com/paper/1806.08548/) (2018)
- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight](https://mlsanity.com/paper/2403.12203/) (2024)
- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative Event Pretraining with Foundation Model Alignment](https://mlsanity.com/paper/2603.23032/) (2026)

## Abstract (arXiv)

Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional cameras suffer from motion blur. However, their widespread adoption in robot learning is severely bottlenecked by the computational cost of simulating high-frequency event data during online training. In this work, we present Approximate Imitation Learning, a novel framework that fundamentally resolves this bottleneck, reducing policy training time for complex, agile drone flight from 52.44 hours to just 1.86 hours - a 28x computational speedup. Our key insight is to separate representation learning from policy search. We first leverage a large-scale offline dataset to learn a task-specific representation space. Subsequently, the policy is fine-tuned through online interactions that rely solely on lightweight state information, completely eliminating the need to render events during the active policy search phase. This training paradigm drastically reduces development overhead and enables event-based control policies to scale to complex environments. Furthermore, our approach eliminates the reliance on standard cameras or intermediate representations during deployment, mapping events directly to control commands. In simulation, our method matches or exceeds the performance of standard imitation learning baselines that require full online event rendering. Finally, we successfully validate the framework in the real world, demonstrating that a policy trained via this ultra-efficient paradigm enables a quadrotor to fly through highly cluttered environments at remarkable speeds of up to 9.8 m/s.