Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space)

Ono, Tomohiro; Kanaoka, Daiju; Shiba, Tomoya; Tokuno, Shoshi; Yano, Yuga; Mizutani, Akinobu; Matsumoto, Ikuya; Amano, Hayato; Tamukoh, Hakaru

doi:10.1080/01691864.2022.2115315

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…In our previous research, we created one that utilizes YolactEdge [11] and point cloud information. This recognition technology can estimate the position and posture of an object [12].…”

Section: Previous Research 21 Object Recognition and Estimate Positionmentioning

confidence: 99%

Object Search and Empty Space Detection System for Home Service Robot

Shiba

Ono

Tamukoh

2023

Proceedings of International Conference on Artificial Life and Robotics

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Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-andplace tasks, robots need to process to search for objects in the case of missing a target object. We propose approaches for the robot to search for a target object from a shelf and a method for selecting empty spaces to move off-target objects. The proposed method is based on two recognition models to select a space and plan motions. The object is to be moved and the space is selected based on the size and position of empty spaces. In the experiments, the robot planned actions to search for a target object from a shelf and to place an object near a group of similar objects. We used these in the RoboCup@Home competition to evaluate the effectiveness of the proposed method.

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“…In our previous research, we created one that utilizes YolactEdge [11] and point cloud information. This recognition technology can estimate the position and posture of an object [12].…”

Section: Previous Research 21 Object Recognition and Estimate Positionmentioning

confidence: 99%

Object Search and Empty Space Detection System for Home Service Robot

Shiba

Ono

Tamukoh

2023

Proceedings of International Conference on Artificial Life and Robotics

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“…Recently, the demand for home service robots has been increasing due to a low birth rate and an aging population, and research on such robots has been active [1], [2], [3], [4], [5], [6]. Highly accurate task planning is necessary to realize a general-purpose service robot that performs appropriate actions in response to human requests.…”

Section: Introductionmentioning

confidence: 99%

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns

Yamao,

Kanaoka,

Isomoto

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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The task planning system is required to accomplish various requests from a human in real-world environments. SayCan, one of the task planning systems, has high accuracy. However, its accuracy decreases for requests that include abstract nouns of the ambiguous word/phrase. We propose a novel task planning system based on SayCan that introduces a function for checking concrete names of abstract nouns and a rule-based skill extraction, enhancing accuracy. The proposed system facilitates the interpretation of requests and enables appropriate task planning. The effectiveness of the proposed system was demonstrated at RoboCup@Home, where it achieved high performance.

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“…These robots operate in dynamic environments, frequently encountering new and unfamiliar objects, making the learning process of recognition technologies and operational efficiency crucial. Ono et al's research [4] automates dataset generation and annotation, reducing the time and cost involved in the learning process while maintaining real-time performance using You Only Look Once v4 (YOLOv4) [5]. However, their experiments [6] showed that YOLO learning took two hours to prepare 500,000 training images and 18 hours.…”

Section: Introductionmentioning

confidence: 99%

A Rapidly Adjustable Object Recognition System through Language Based Prompt Engineering

Yamaguchi,

Shiba,

Isomoto

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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We propose the use of language-based prompt engineering to achieve rapidly adjustable object recognition in RoboCup@Home. The proposed prompt engineering involves humans adding features, such as the color and material of an object, into the text prompts inputted into Language Segment Anything. In this research, we evaluated the effectiveness of our proposed method in three benchmark tests for object recognition at RoboCup@Home 2023. The results show that the highest scores were obtained in specific tasks, indicating that the proposed method applies to various recognition tasks.

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Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space)

Cited by 8 publications

References 27 publications

Object Search and Empty Space Detection System for Home Service Robot

Object Search and Empty Space Detection System for Home Service Robot

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns

A Rapidly Adjustable Object Recognition System through Language Based Prompt Engineering

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