1,901 research outputs found
Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots
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 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 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, \airl enables a broad class of deep RL
research on UAVs. The source code is available
at:~\texttt{\url{http://bit.ly/2JNAVb6}}.Comment: To Appear in Springer Machine Learning Journal (Special Issue on
Reinforcement Learning for Real Life
- …