VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2309.07832v2
• Date: Tue, 19 Sep 2023 21:22:19 GMT
• Title: VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
• Authors: Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Dinesh Manocha
• Abstract summary: We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments. Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. We observe that VAPOR's actions improve success rates by up to 40%, decrease the average current consumption by up to 2.9%, and decrease the normalized trajectory length by up to 11.2%.
• Score: 53.13393315664145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. Our policy uses height and intensity-based cost maps derived from 3D LiDAR point clouds, a goal cost map, and processed proprioception data as state inputs, and learns the physical and geometric properties of the surrounding obstacles such as height, density, and solidity/stiffness. The fully-trained policy's critic network is then used to evaluate the quality of dynamically feasible velocities generated from a novel context-aware planner. Our planner adapts the robot's velocity space based on the presence of entrapment inducing vegetation, and narrow passages in dense environments. We demonstrate our method's capabilities on a Spot robot in complex real-world outdoor scenes, including dense vegetation. We observe that VAPOR's actions improve success rates by up to 40%, decrease the average current consumption by up to 2.9%, and decrease the normalized trajectory length by up to 11.2% compared to existing end-to-end offline RL and other outdoor navigation methods.

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