Adding a history of recent actions to a vision-language model's prompt during frontier-based exploration yields 46% success and 24.8% SPL on HM3D zero-shot object navigation, on par with prior methods.
GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance
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abstract
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only objects are partially observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose \textit{Geometric-part and Affordance Maps} (GAMap), a novel method that integrates object parts and affordance attributes as navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on HM3D and Gibson benchmark datasets demonstrate improvements in Success Rate and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional object-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot.
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History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation
Adding a history of recent actions to a vision-language model's prompt during frontier-based exploration yields 46% success and 24.8% SPL on HM3D zero-shot object navigation, on par with prior methods.