Approximate Imity Learning for Event-based Quadrotor Flight in Cluttered Environments
Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments
This paper presents Approximate Imitation Learning, a novel framework that reduces the computational cost of training event-based control policies for quadrotors in cluttered environments. By separating representation learning from policy search, the method achieves a 28x speedup in training time and enables real-world validation with high-speed flight.
This research introduces Approximate Imitation Learning, a framework that significantly reduces the computational cost of training event-based control policies for quadrotors in cluttered environments. By separating representation learning from policy search, the method achieves a 28x speedup in training time and enables real-world validation with high-speed flight.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
policy training time
52.44 hours
View original evidence
reducing policy training time for complex, agile drone flight from 52.44 hours to just 1.86 hours
Open source S3policy training time
1.86 hours
View original evidence
reducing policy training time for complex, agile drone flight from 52.44 hours to just 1.86 hours
Open source S3computational speedup
28
quadrotor speed
≤9.8
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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
Open source S3
kable speeds of up to 9.8 m/s.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org