Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2210.07506v1
• Date: Fri, 14 Oct 2022 04:23:27 GMT
• Title: Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
• Authors: Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas H. Li, Mingkui Tan, Chuang Gan
• Abstract summary: We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
• Score: 87.52136927091712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. However, enabling a robot to build a map that well represents the environment is extremely challenging as the environment often involves diverse objects with various attributes. In this paper, we propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively. Moreover, we propose a weakly-supervised auxiliary task, which requires the agent to localize instruction-relevant objects on the map. Through this task, the agent not only learns to localize the instruction-relevant objects for navigation but also is encouraged to learn a better map representation that reveals object information. We then feed the learned map and instruction to a waypoint predictor to determine the next navigation goal. Experimental results show our method outperforms the state-of-the-art by 4.0% and 4.6% w.r.t. success rate both in seen and unseen environments, respectively on VLN-CE dataset. Code is available at https://github.com/PeihaoChen/WS-MGMap.

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