284 research outputs found
Reinforcement Learning of Scalable, Flexible, and Robust Cooperative Transport Behavior Using the Transformer Encoder
No full textSueoka Y., Yoshida T., Osuka K.. Reinforcement Learning of Scalable, Flexible, and Robust Cooperative Transport Behavior Using the Transformer Encoder. Springer Proceedings in Advanced Robotics 34, 288 (2026); https://doi.org/10.1007/978-3-032-04584-3_20.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-032-04584-3_20A swarm robotic system, consisting of numerous distributed autonomous robots, has been increasingly envisioned for applications in coordinated object removal tasks, such as clearing debris and fallen trees in unknown and complex environments typical of disaster sites. In these environments, the system should be robust, flexible, and scalable, and able to function even when communication between robots is interrupted. Although some previous studies have demonstrated flexible cooperative behaviors using the centralized approach, there is still no autonomous distributed swarm robotic system that takes these considerations into account. This paper aims to develop a coordinated system of distributed autonomous swarm robots with robustness, flexibility, and scalability by applying the Transformer encoder to swarm systems. The focus of this study is on coordinated object removal tasks. Using the TurtleBot3 Burger robot model, we train a coordinated object removal behavior that operates without any communication between robots, leveraging reinforcement learning. We demonstrate that this behavior can be applied to scenarios involving varying numbers and sizes of objects, as well as different quantities of robots, without the need for retraining. We further explore the effect of noise on sensor data and motor performance. Additionally, we demonstrate the application of our system in a rescue scenario within an unknown environment, where it coordinates with another autonomous mobile robot to enhance effectiveness
Verification of a Two-Step Inference Model for Cooperative Evaluation of Robot Actions Using Foundation Models
No full textYoshida T., Sueoka Y., Osuka K.. Verification of a Two-Step Inference Model for Cooperative Evaluation of Robot Actions Using Foundation Models. Springer Proceedings in Advanced Robotics 34, 395 (2026); https://doi.org/10.1007/978-3-032-04584-3_27.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-032-04584-3_27By exchanging information, multiple robots can compensate for the limitations in their individual perceptions of their surroundings. Our previous study proposed a two-step cooperative inference model using foundation models to assess diverse robot actions based on the information exchanged in natural language with other robots and humans. The inference model classifies executed actions into three categories—accomplished, not accomplished, and unclear—using the fuzzy inference system and Dempster-Shafer theory. However, the inference model’s reliance on a single rule in fuzzy inference raises concerns about the accuracy of its action classification. Additionally, evaluating the model’s reliability, validity, and efficiency remains unsatisfactory. In this paper, we first enhance the inference model to generate multiple fuzzy rules and perform a comparative evaluation of reliability, validity, and efficiency using a dataset from a real-world task. Then, we discuss the evaluation results of the first and second steps, the effect of introducing the unclear category in preventing incorrect judgments, and the effect of threshold adjustments in the second step on the inference results
Mathematical Model of Pattern Formation Developed Just for Curiosity (Pattern formation in collective dynamics)
No full textWe proposed a simple mathematical model that exhibits various non-trivial patterns, inspired by friendship formation in human society. We developed this model just for curiosity and do not have any background of the study. However, we believe that this model could help understand the essential mechanism for the emergence of dynamical order in various systems, as well as be used in the design of artificial systems such as swarm robotic systems
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