Navigating to Objects in Unseen Environments by Distance Prediction

• URL: http://arxiv.org/abs/2202.03735v1
• Date: Tue, 8 Feb 2022 09:22:50 GMT
• Title: Navigating to Objects in Unseen Environments by Distance Prediction
• Authors: Minzhao Zhu, Binglei Zhao, Tao Kong
• Abstract summary: We propose an object goal navigation framework, which could directly perform path planning based on an estimated distance map. Specifically, our model takes a birds-eye-view semantic map as input, and estimates the distance from the map cells to the target object. With the estimated distance map, the agent could explore the environment and navigate to the target objects based on either human-designed or learned navigation policy.
• Score: 16.023495311387478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object Goal Navigation (ObjectNav) task is to navigate an agent to an object instance in unseen environments. The traditional navigation paradigm plans the shortest path on a pre-built map. Inspired by this, we propose an object goal navigation framework, which could directly perform path planning based on an estimated distance map. Specifically, our model takes a birds-eye-view semantic map as input, and estimates the distance from the map cells to the target object based on the learned prior knowledge. With the estimated distance map, the agent could explore the environment and navigate to the target objects based on either human-designed or learned navigation policy. Empirical results in visually realistic simulation environments show that the proposed method outperforms a wide range of baselines on success rate and efficiency.

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