イベントカメラを用いた混雑環境でのクアッドローター飛行のための近似模倣学習
Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments
イベントカメラの高周波データをシミュレーションする計算コストを回避するため、表現学習と方策探索を分離し、オフラインデータで表現を学習後、軽量な状態情報のみで方策を微調整する近似模倣学習を提案。訓練時間を28倍短縮し、実機でも有効性を実証した。
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著者: Nico Messikommer, Jiaxu Xing, Leonard Bauersfeld, Marco Cannici, Elie Aljalbout, Davide Scaramuzza
分類: cs.RO
原文アブストラクト
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.
