Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation

TL;DR

Air Learning is an open-source simulation environment for deep reinforcement learning in UAV navigation, enabling research on resource constraints, hardware-in-the-loop training, and sensor failure robustness.

Contribution

The paper introduces Air Learning, a versatile simulator with domain randomization and hardware-in-the-loop techniques to improve UAV policy robustness on embedded platforms.

Findings

Embedded UAV trajectories are significantly longer than desktop predictions.

Artificial delays in training reduce hardware-induced performance gaps.

Hardware-in-the-loop effectively characterizes compute-related performance discrepancies.

Abstract

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on the aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at:http://bit.ly/2JNAVb6.