51 citations · 174 across the 22 of their papers we have counts for
24 papers
Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots
Lucas Da Mota Bruno, Jiahao Sim, Yoshinobu Hagiwara
General Purpose Service Robot (GPSR) tasks, as defined in the RoboCup@Home benchmark, require robots to interpret diverse natural language commands and generate multi-step action s…
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,…
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…
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…
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…
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…
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…
Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information
Yoshinobu Hagiwara, Keishiro Taguchi, Satoshi Ishibushi +2
This paper proposes a hierarchical Bayesian model based on spatial concepts that enables a robot to transfer the knowledge of places from experienced environments to a new environm…
Map completion from partial observation using the global structure of multiple environmental maps
Yuki Katsumata, Akinori Kanechika, Akira Taniguchi +3
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultan…
Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model
Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi +1
Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multim…
Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robots
Akira Taniguchi, Shota Isobe, Lotfi El Hafi +2
Tidy-up tasks by service robots in home environments are challenging in robotics applications because they involve various interactions with the environment. In particular, robots…
Symbol Emergence as an Interpersonal Multimodal Categorization
Yoshinobu Hagiwara, Hiroyoshi Kobayashi, Akira Taniguchi +1
This study focuses on category formation for individual agents and the dynamics of symbol emergence in a multi-agent system through semiotic communication. Semiotic communication i…
Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping
Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi +1
We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM…
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi +1
In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Ba…
Bayesian Body Schema Estimation using Tactile Information obtained through Coordinated Random Movements
Tomohiro Mimura, Yoshinobu Hagiwara, Tadahiro Taniguchi +1
This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data…