A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning.

Zeyuan Cai, Zhiquan Feng, Liran Zhou, Changsheng Ai, Haiyan Shao, Xiaohui Yang

Abstract read

In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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1 citing paper in PubMed.

Closed loop iterative learning control for consistency tracking in lower limb rehabilitation robotic system with initial state deviations.Scientific reports · 2025
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5 · Who and what money

Authors and funding

6 authors.

Zeyuan CaiSchool of Information Science and Engineering, University of Jinan, Jinan 250022, China.

Zhiquan FengSchool of Information Science and Engineering, University of Jinan, Jinan 250022, China.ORCID https://orcid.org/0000-0002-7704-4197

Liran ZhouSchool of Information Science and Engineering, University of Jinan, Jinan 250022, China.

Changsheng AiSchool of Mechanical Engineering, University of Jinan, Jinan 250022, China.

Haiyan ShaoSchool of Mechanical Engineering, University of Jinan, Jinan 250022, China.

Xiaohui YangSchool of Information Science and Engineering, University of Jinan, Jinan 250022, China.ORCID https://orcid.org/0000-0001-9677-979X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.

Indexed as

RoboticsAlgorithmsHumansLearningReinforcement, Psychology

Identifiers

PMID36210974

DOI10.1155/2022/2341898

PMCPMC9534615

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.