Approximate Imitation Learning for Event-based Quadrotor Flight
Overview
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.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · policy training time: 52.44 hours · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · policy training time: 1.86 hours · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · computational speedup: 28 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · quadrotor speed: 9.8 other · Basis not reported
“kable speeds of up to 9.8 m/s.”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T23:04:19.914Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T23:03:58.168Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalApproximate Imity 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.
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