{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:23:23Z","timestamp":1754155403708,"version":"3.41.2"},"reference-count":25,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Human beings are able to adjust their arm stiffness in daily life tasks. This paper aims to enable a robot to learn these human-like variable stiffness motor skills autonomously.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The paper presents a reinforcement learning method to enable a robot to learn variable stiffness motor skills autonomously. Firstly, the variable stiffness motor skills are encoded by the previously proposed dynamical movement primitives and stiffness primitives (DMP-SP) framework, which is able to generate both motion and stiffness curves for robots. The admittance controller is then used to make a robot follow the motion and stiffness curves. The authors then use the policy improvement with path integrals (PI2) algorithm to optimize the robot motion and stiffness curves iteratively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The performance of the proposed method is evaluated on an UR10 robot by two different tasks: a) via-point task, b) sweeping the floor. The results show that after training, the robot is capable of accomplishing the tasks safely and compliantly.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The method can help the robots walk out of the isolated environment and accelerate their integration into human being\u2019s daily life.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper uses reinforcement learning method to improve DMP-SP framework, thus allowing the robots to learn variable stiffness motor skills autonomously with no need for extra sensors.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-04-2024-0168","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T02:40:15Z","timestamp":1728614415000},"page":"174-182","source":"Crossref","is-referenced-by-count":0,"title":["Policy improvement of the dynamical movement 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