Multi-Robot Deep Reinforcement Learning for Mobile Navigation

• URL: http://arxiv.org/abs/2106.13280v1
• Date: Thu, 24 Jun 2021 19:07:40 GMT
• Title: Multi-Robot Deep Reinforcement Learning for Mobile Navigation
• Authors: Katie Kang, Gregory Kahn, Sergey Levine
• Abstract summary: We propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt) At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.
• Score: 82.62621210336881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such datasets with a single robot can be prohibitively expensive. Collecting data with multiple different robotic platforms with possibly different dynamics is a more scalable approach to large-scale data collection. But how can deep reinforcement learning algorithms leverage such heterogeneous datasets? In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt). At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. This method of planning with hierarchically integrated models allows the algorithm to train on datasets gathered by a variety of different platforms, while respecting the physical capabilities of the deployment robot at test time. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.

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