{"id":1445,"date":"2023-10-06T08:38:05","date_gmt":"2023-10-06T07:38:05","guid":{"rendered":"https:\/\/babel.isa.uma.es\/kipr\/?p=1445"},"modified":"2023-10-06T08:38:05","modified_gmt":"2023-10-06T07:38:05","slug":"adaptation-of-model-free-rl-to-variations-in-the-task-under-continuous-state-and-action-spaces-applied-to-robot-grasping","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=1445","title":{"rendered":"Adaptation of model-free RL to variations in the task under continuous state and action spaces applied to robot grasping"},"content":{"rendered":"

Shahid, A.A., Piga, D., Braghin, F. et al. Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning, <\/strong> Auton Robot 46, 483\\u2013498 (2022) DOI: 10.1007\/s10514-022-10034-z<\/a>.<\/h4>\n

This paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In order to accelerate the learning process, the fine-tuning procedure is proposed that demonstrates the continuous adaptation of on-policy RL to new environments, allowing the learned policy to adapt and execute the (partially) modified task. A dense reward function is designed for the task to enable an efficient learning of the agent. A grasping task involving a Franka Emika Panda manipulator is considered as the reference task to be learned. The learned control policy is demonstrated to be generalizable across multiple object geometries and initial robot\/parts configurations. The approach is finally tested on a real Franka Emika Panda robot, showing the possibility to transfer the learned behavior from simulation. Experimental results show 100% of successful grasping tasks, making the proposed approach applicable to real applications.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"

Shahid, A.A., Piga, D., Braghin, F. et al. Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning, …<\/span> Read More →<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,174],"tags":[173,234],"class_list":["post-1445","post","type-post","status-publish","format-standard","hentry","category-applications-of-reinforcement-learning-to-robots","category-industrial-robots","tag-continuous-mdps","tag-modelless-reinforcement-learning"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1445"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1445"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1445\/revisions"}],"predecessor-version":[{"id":1446,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1445\/revisions\/1446"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1445"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1445"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1445"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}