Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation

• URL: http://arxiv.org/abs/2311.07992v1
• Date: Tue, 14 Nov 2023 08:45:32 GMT
• Title: Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation
• Authors: Jiaming Wang and Harold Soh
• Abstract summary: We introduce a novel framework centered around the Probable Object Location (POLo) score. We further enhance the framework's practicality by introducing POLoNet, a neural network trained to approximate the computationally intensive POLo score. Our experiments, involving the first phase of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet significantly outperforms a range of baseline methods.
• Score: 15.623723522165731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To advance the field of autonomous robotics, particularly in object search tasks within unexplored environments, we introduce a novel framework centered around the Probable Object Location (POLo) score. Utilizing a 3D object probability map, the POLo score allows the agent to make data-driven decisions for efficient object search. We further enhance the framework's practicality by introducing POLoNet, a neural network trained to approximate the computationally intensive POLo score. Our approach addresses critical limitations of both end-to-end reinforcement learning methods, which suffer from memory decay over long-horizon tasks, and traditional map-based methods that neglect visibility constraints. Our experiments, involving the first phase of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet significantly outperforms a range of baseline methods, including end-to-end RL techniques and prior map-based strategies. To provide a comprehensive evaluation, we introduce new performance metrics that offer insights into the efficiency and effectiveness of various agents in object goal navigation.

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