7 accepted papers
This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform tasks, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and
In this study, we propose a shared control method for teleoperated mobile robots using brain-machine interfaces (BMI). The control commands generated through BMI for robot operation face issues of low input frequency, discreteness, and uncertainty due to noise. To address these challenges, our metho…
This paper discusses the tele-operation system for multiple mobile manipulators. If a single person could freely tele-operate multiple mobile manipulators simultaneously, it would be a great step toward the goal of “avatar-symbiotic society” allowing people to live beyond the constraints of their bo…
We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model withou…
In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by service robots in real environments. For robots to act autonomously in a real environment, it is necessary to define the appropriate actions for the environment. However, it is difficult t…
In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by service robots in real environments. For robots to act autonomously in a real environment, it is necessary to define the appropriate actions for the environment. However, it is difficult t
One of the biggest challenges in intelligent robotics is to build robots that can learn to use language. To this end, we think that the practical long-term on-line concept/word learning algorithm for robots is a key issue to be addressed. In this paper, we develop an unsupervised on-line learning al…