Search Results for author:
Found 26 papers, 6 papers with code
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA).
Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping
Navigation experiments using speech instruction with a waypoint demonstrated the performance improvement of path planning, WN-SPL by 0. 589, and reduced computation time by 7. 14 sec compared to conventional methods.
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models
Instead, it is a game based on joint attention without explicit feedback.
Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping
We propose a novel online learning algorithm, called SpCoSLAM 2. 0, for spatial concepts and lexical acquisition with high accuracy and scalability.
Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals.
Symbol Emergence as an Interpersonal Multimodal Categorization
In this paper, we describe a new computational model that represents symbol emergence in a two-agent system based on a probabilistic generative model for multimodal categorization.
Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robots
We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition.
Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model
The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as `Go to the kitchen', via probabilistic inference on a Bayesian generative model using spatial concepts.
Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information
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 environment.
Hippocampal formation-inspired probabilistic generative model
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space.
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs.
Map completion from partial observation using the global structure of multiple environmental maps
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently.
Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances
The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object.
StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition
Although this method is powerful, it can fail to preserve the linguistic content of input speech when the number of available training samples is extremely small.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference
(2) Function to improve the categorization accuracy in an agent via semiotic communication with another agent, even when some sensory modalities of each agent are missing.
Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues
The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously.
Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word
On the basis of the H2H-type Inter-MDM, we propose a naming game in the same way as the conventional Inter-MDM.
Brain-inspired probabilistic generative model for double articulation analysis of spoken language
This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys.
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation
This study provides insights into the technical aspects of the proposed method, including active perception and exploration by the robot, and how the method can enable mobile robots to learn spatial concepts through active exploration.
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models
The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and demonstrate that RMHNG is an approximate Bayesian inference method for the posterior distribution over a latent variable shared by agents, similar to MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and real image data, enabling multiple agents to develop and share a symbol system.
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study
By comparing human acceptance decisions of a partner's naming with acceptance probabilities computed in the MHNG, we tested whether human behavior is consistent with the MHNG theory.
Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality
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 naming game and cross-situational learning.
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning
Therefore, the agents change their actions according to the estimated messages to achieve cooperative tasks.
Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning
To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images.
DEQ-MCL: Discrete-Event Queue-based Monte-Carlo Localization
Spatial cognition in hippocampal formation is posited to play a crucial role in the development of self-localization techniques for robots.
