JSAI2025

Presentation information

General Session

General Session » GS-2 Machine learning

[3S5-GS-2] Machine learning:

Thu. May 29, 2025 3:40 PM - 5:20 PM Room S (Room 701-2)

座長:山口 真弥(NTT)

4:40 PM - 5:00 PM

[3S5-GS-2-04] Interagent knowledge transfer for continuing learners of object-goal navigation

〇Kouki Terashima1, Daiki Iwata1, Kanji Tanaka1, Tomoe Hiroki1 (1. University of Fukui)

[[Online]]

Keywords:knowledge transfer

To enhance object-object navigation (ON) of robots in unknown environments, the possibility of short knowledge transfer (KT) between agents is explored. Using the analogy of a situation where a human traveller efficiently explores a destination (e.g. accommodation) in an unknown location by acquiring local knowledge from locals, we propose a framework where a traveller robot (student) communicates with a local robot (teacher) to acquire ON knowledge with minimal interaction. Unlike traditional zero-shot ON approaches (large-scale language models), learning-based ON methods that utilise frontier-driven object-function maps and neural state action maps involve complex challenges to be solved in data-free KT. In this study, a lightweight plug-and-play KT module was designed for a non-cooperative black-box teacher in an open-world environment. By assuming the vision and mobility capabilities of the teacher robot and using its state-action history as a knowledge base, we developed a query-based occupancy map that dynamically represents the location of the target object Through experiments in a Habitat environment, we have demonstrated the effectiveness of this approach.

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