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Learning Perception-Aware Agile Flight in Cluttered Environments
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Learning Perception-Aware Agile Flight in Cluttered Environments
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Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minimum time. However, they are not perception aware, a crucial requirement in vision-based navigation due to the camera's limited field of view and the underactuated nature of a quadrotor. We propose a learning-based system that achieves perception-aware, agile flight in cluttered environments. Our method combines imitation learning with reinforcement learning (RL) by leveraging a privileged learning-by-cheating framework. Using RL, we first train a perception-aware teacher policy with full-state information to fly in minimum time through cluttered environments. Then, we use imitation learning to distill its knowledge into a vision-based student policy that only perceives the environment via a camera. Our approach tightly couples perception and control, showing a significant advantage in computation speed (10 times faster) and success rate. We demonstrate the closed-loop control performance using hardware-in-the-loop simulation.
Forward citations
Cited by 4 Pith papers
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PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientat...
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PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
A sim-to-real method that distills a privileged camera-based teacher policy into a tactile student policy, improving in-hand rotation and reorientation over proprioception-only policies.
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NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance ove...
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High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning
A reinforcement-learning quadrotor policy trained with Dijkstra and control-barrier rewards, plus a high-order CBF safety filter, is claimed to navigate cluttered indoor and forest environments at up to 7.5 m/s.
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