Public articles linked to the same research event.
arXiv For the Person Goal Navigation (PersonNav) problem, the work proposes a planning framework that combines distance and semantic information about the recipient (habits and intent) weighted by the trust of user-provided information, and builds a synthetic benchmark of scenarios with actors, items, and requests to evaluate performance before real-world deployment while simulating natural-language human-robot interaction; results show the informed search outperforms classical distance-based graph baselines, semantics alone leads to ungrounded and sporadic search, an LLM-based variant performs comparably, and hardware tests show the method supports real-world embodiment.
For the Person Goal Navigation (PersonNav) problem, the work proposes a planning framework that combines distance and semantic information about the recipient (habits and intent) weighted by the trust of user-provided information, and builds a synthetic benchmark of scenarios with actors, items, and requests to evaluate performance before real-world deployment while simulating natural-language human-robot interaction; results show the informed search outperforms classical distance-based graph baselines, semantics alone leads to ungrounded and sporadic search, an LLM-based variant performs comparably, and hardware tests show the method supports real-world embodiment.
For the Person Goal Navigation (PersonNav) problem, the work proposes a planning framework that combines distance and semantic information about the recipient (habits and intent) weighted by the trust of user-provided information, and builds a synthetic benchmark of scenarios with actors, items, and requests to evaluate performance before real-world deployment while simulating natural-language human-robot interaction; results show the informed search outperforms classical distance-based graph baselines, semantics alone leads to ungrounded and sporadic search, an LLM-based variant performs comparably, and hardware tests show the method supports real-world embodiment.
For the Person Goal Navigation (PersonNav) problem, the work proposes a planning framework that combines distance and semantic information about the recipient (habits and intent) weighted by the trust of user-provided information, and builds a synthetic benchmark of scenarios with actors, items, and requests to evaluate performance before real-world deployment while simulating natural-language human-robot interaction; results show the informed search outperforms classical distance-based graph baselines, semantics alone leads to ungrounded and sporadic search, an LLM-based variant performs comparably, and hardware tests show the method supports real-world embodiment.