Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps

• URL: http://arxiv.org/abs/2106.03665v1
• Date: Mon, 7 Jun 2021 14:42:51 GMT
• Title: Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
• Authors: Chengguang Xu, Christopher Amato, Lawson L.S. Wong
• Abstract summary: We propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning. Because the low-level controller is only trained with local behaviors, this framework allows us to generalize to novel environments. Experimental results demonstrate the effectiveness of the proposed framework in both seen and novel environments.
• Score: 21.245942227850733
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In robot navigation, generalizing quickly to unseen environments is essential. Hierarchical methods inspired by human navigation have been proposed, typically consisting of a high-level landmark proposer and a low-level controller. However, these methods either require precise high-level information to be given in advance or need to construct such guidance from extensive interaction with the environment. In this work, we propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning. In particular, we introduce a dynamic topological map that can be initialized from the rough 2-D map along with a high-level planning approach for proposing reachable 2-D map patches of the intermediate landmarks between the start and goal locations. To use proposed 2-D patches, we train a deep generative model to generate intermediate landmarks in observation space which are used as subgoals by low-level goal-conditioned reinforcement learning. Importantly, because the low-level controller is only trained with local behaviors (e.g. go across the intersection, turn left at a corner) on existing environments, this framework allows us to generalize to novel environments given only a rough 2-D map, without requiring further learning. Experimental results demonstrate the effectiveness of the proposed framework in both seen and novel environments.

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