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

TL;DR

This paper introduces Approximate Imitation Learning, a novel framework enabling high-speed quadrotor flight using event cameras by training neural networks with simulated data, leading to efficient, robust control in cluttered environments.

Contribution

It presents a new imitation learning method that reduces training complexity for event-based control policies, facilitating fast quadrotor flight in cluttered spaces.

Findings

Outperforms standard imitation learning baselines in simulation.

Achieves real-world flight speeds up to 9.8 m/s in cluttered environments.

Enables efficient training without rendering event data during learning.

Abstract

Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional cameras suffer from image degradations such as motion blur. In addition, their low power consumption can enhance endurance, which is critical for resource-constrained platforms. Motivated by these properties, we present a novel approach that enables a quadrotor to fly through cluttered environments at high speed by perceiving the environment with a single event camera. Our proposed method employs an end-to-end neural network trained to map event data directly to control commands, eliminating the reliance on standard cameras. To enable efficient training in simulation, where rendering synthetic event data is computationally expensive, we propose Approximate Imitation Learning, a novel imitation learning framework. Our approach leverages a large-scale offline dataset to learn a task-specific representation space. Subsequently, the policy is trained through online interactions that rely solely on lightweight, simulated state information, eliminating the need to render events during training. This enables the efficient training of event-based control policies for fast quadrotor flight, highlighting the potential of our framework for other modalities where data simulation is costly or impractical. Our approach outperforms standard imitation learning baselines in simulation and demonstrates robust performance in real-world flight tests, achieving speeds up to 9.8 ms-1 in cluttered environments.