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Found 14 papers, 4 papers with code
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.
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.
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.
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.
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models
Instead, it is a game based on joint attention without explicit feedback.
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.
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.
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.
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.
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.
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.
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.
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.
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA).
