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- W4312360147 abstract "Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal (’where to go’) based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline." @default.
- W4312360147 created "2023-01-04" @default.
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- W4312360147 date "2022-08-20" @default.
- W4312360147 modified "2023-09-26" @default.
- W4312360147 title "Object Goal Navigation using Data Regularized Q-Learning" @default.
- W4312360147 doi "https://doi.org/10.1109/case49997.2022.9926452" @default.
- W4312360147 hasPublicationYear "2022" @default.
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