51 citations · 307 across the 33 of their papers we have counts for
35 papers
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,…
On-Demand Human Assistance for Task Continuation under Physical Action Failures in LLM-based Planning
Shoichi Hasegawa, Akira Taniguchi, Lotfi El Hafi +2
While robot action planning based on large language models (LLMs) has advanced remarkably, continuing a task after a physical action failure during execution remains a key challeng…
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…
SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning
Nguyen Le Hoang, Tadahiro Taniguchi, Tianwei Fang +2
Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language. Recent frameworks, such as the Metropolis--Ha…
Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map
Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara +3
Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user's desired objects. This task is known as i…
Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning
Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara +3
Improving instance-specific image goal navigation (InstanceImageNav), which locates the identical object in a real-world environment from a query image, is essential for robotic sy…
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning
Tomoaki Nakamura, Akira Taniguchi, Tadahiro Taniguchi
This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic infer…
Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality
Yoshinobu Hagiwara, Kazuma Furukawa, Takafumi Horie +2
We present a computational model for a symbol emergence system that enables the emergence of lexical knowledge with combinatoriality among agents through a Metropolis-Hastings nami…
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study
Ryota Okumura, Tadahiro Taniguchi, Yosinobu Hagiwara +1
In this study, we explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies investigate how humans organize…
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models
Jun Inukai, Tadahiro Taniguchi, Akira Taniguchi +1
In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. A…
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation
Akira Taniguchi, Yoshiki Tabuchi, Tomochika Ishikawa +3
Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic in…
Brain-inspired probabilistic generative model for double articulation analysis of spoken language
Akira Taniguchi, Maoko Muro, Hiroshi Yamakawa +1
The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in w…
Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word
Kazuma Furukawa, Akira Taniguchi, Yoshinobu Hagiwara +1
In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic gener…
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models
Tadahiro Taniguchi, Yuto Yoshida, Akira Taniguchi +1
Constructive studies on symbol emergence systems seek to investigate computational models that can better explain human language evolution, the creation of symbol systems, and the…
Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping
Akira Taniguchi, Shuya Ito, Tadahiro Taniguchi
Assisting individuals in their daily activities through autonomous mobile robots, especially for users without specialized knowledge, is crucial. Specifically, the capability of ro…
Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues
Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki +1
Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other…
Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference
Yoshinobu Hagiwara, Kazuma Furukawa, Akira Taniguchi +1
This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce th…
StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition
Shoki Sakamoto, Akira Taniguchi, Tadahiro Taniguchi +1
Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently deve…
Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances
Rikunari Sagara, Ryo Taguchi, Akira Taniguchi +4
This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be ab…