51 citations · 307 across the 33 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…
Generative Emergent Communication: Large Language Model is a Collective World Model
Tadahiro Taniguchi, Ryo Ueda, Tomoaki Nakamura +2
Large Language Models (LLMs) have demonstrated a remarkable ability to capture extensive world knowledge, yet how this is achieved without direct sensorimotor experience remains a…