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Yang, Haoyu

University of British Columbia

We introduce ROS-X-Habitat, a software interface that bridges the AI Habitat plat- form for embodied reinforcement learning agents with other robotics resources via ROS. This interface not only offers standardized communication protocols between embodied agents and simulators, but also enables physics-based simulation. With this interface, roboticists are able to train their own Habitat RL agents in another simulation environment or to develop their own robotic algorithms inside Habitat Sim. Through in silico experiments, we demonstrate that ROS-X-Habitat has minimal impact on the navigation performance and simulation speed of Habitat agents; that a standard set of ROS mapping, planning and navigation tools can run in the Habitat simulator, and that a Habitat agent can run in the standard ROS simulator Gazebo. Furthermore, to show how ROS-X-Habitat can be used in data collection and RL training, we present the training and evaluation of an agent we train to perform a multiple point goal navigation task we define.

Text

2022-04-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81124

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/