1 research outputs found
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation
Reinforcement Learning (RL), among other learning-based methods, represents
powerful tools to solve complex robotic tasks (e.g., actuation, manipulation,
navigation, etc.), with the need for real-world data to train these systems as
one of its most important limitations. The use of simulators is one way to
address this issue, yet knowledge acquired in simulations does not work
directly in the real-world, which is known as the sim-to-real transfer problem.
While previous works focus on the nature of the images used as observations
(e.g., textures and lighting), which has proven useful for a sim-to-sim
transfer, they neglect other concerns regarding said observations, such as
precise geometrical meanings, failing at robot-to-robot, and thus in
sim-to-real transfers. We propose a method that learns on an observation space
constructed by point clouds and environment randomization, generalizing among
robots and simulators to achieve sim-to-real, while also addressing partial
observability. We demonstrate the benefits of our methodology on the point goal
navigation task, in which our method proves to be highly unaffected to unseen
scenarios produced by robot-to-robot transfer, outperforms image-based
baselines in robot-randomized experiments, and presents high performances in
sim-to-sim conditions. Finally, we perform several experiments to validate the
sim-to-real transfer to a physical domestic robot platform, confirming the
out-of-the-box performance of our system.Comment: Accepted to IROS'202