activity
20242026
collaborators

7 papers

cs.RO2026

Whose Is This?: Context-Aware Object Ownership Inference with Uncertainty-Guided Questioning

Saki Hashimoto, Akira Taniguchi, Shoichi Hasegawa +2

Service robots must infer object ownership to correctly interpret instructions such as "bring me my cup." However, ownership is a latent attribute that cannot be directly observed,…

cs.RO2025

Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models

Kento Murata, Shoichi Hasegawa, Tomochika Ishikawa +4

It is crucial to efficiently execute instructions such as "Find an apple and a banana" or "Get ready for a field trip," which require searching for multiple objects or understandin…

cs.RO2025

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model

Saki Hashimoto, Shoichi Hasegawa, Tomochika Ishikawa +4

Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as ``Bring me my cup.'' However, ownership cannot be re…

cs.RO2025

Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions

Akira Oyama, Shoichi Hasegawa, Akira Taniguchi +2

Daily life support robots must interpret ambiguous verbal instructions involving demonstratives such as ``Bring me that cup,'' even when objects or users are out of the robot's vie…

cs.HC2025

Public Evaluation on Potential Social Impacts of Fully Autonomous Cybernetic Avatars for Physical Support in Daily-Life Environments: Large-Scale Demonstration and Survey at Avatar Land

Lotfi El Hafi, Kazuma Onishi, Shoichi Hasegawa +18

Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. Whi…

cs.HC2025

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI

Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi +1

We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowled…