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2023

LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks

Liu, Haokun, Zhu, Yaonan, Kato, Kenji et al.

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This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions.

The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands.
Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction.
This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.

LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks · Around

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R. Wang, Y. Wu, W. L. Chan, and K. P. Tee, “Dynamic movement primitives plus: For enhanced reproduction quality and efficient trajectory modification using truncated kernels and local biases,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, pp. 3765–3771
2016
Earlier work this paper cites.
J. Nakanishi, S. Itadera, T. Aoyama, and Y. Hasegawa, “Towards the development of an intuitive teleoperation system for human support robot using a vr device,” Advanced Robotics , vol. 34, no. 19, pp. 1239–1253, 2020
2020
Earlier work this paper cites.

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Original
2023
Cited alongside, same era.

Then

Y. Zhu, B. Jiang, Q. Chen, T. Aoyama, and Y. Hasegawa, “A shared control framework for enhanced grasping performance in teleoperation,” IEEE Access , vol. 11, pp. 69 204–69 215, 2023
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