Object-Goal Visual Navigation via Effective Exploration of Relations Among Historical Navigation States
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, 00, pp. 2563-2573
- Issue Date:
- 2023
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1666963.pdf | Published version | 1 MB |
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Object goal visual navigation aims at steering an agent toward an object via a series of moving steps Previous works mainly focus on learning informative visual representations for navigation but overlook the impacts of navigation states on the effectiveness and efficiency of navigation We observe that high relevance among navigation states will cause navigation inefficiency or failure for existing methods In this paper we present a History inspired Navigation Policy Learning HiNL framework to estimate navigation states effectively by exploring relationships among historical navigation states In HiNL we propose a History aware State Estimation HaSE module to alleviate the impacts of dominant historical states on the current state estimation Meanwhile HaSE also encourages an agent to be alert to the current observation changes thus enabling the agent to make valid actions Furthermore we design a History based State Regularization HbSR to explicitly suppress the correlation among navigation states in training As a result our agent can update states more effectively while reducing the correlations among navigation states Experiments on the artificial platform AI2 THOR i e iTHOR and RoboTHOR demonstrate that HiNL significantly outperforms state of the art methods on both Success Rate and SPL in unseen testing environments
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