{"id":1877,"date":"2025-02-20T10:24:19","date_gmt":"2025-02-20T09:24:19","guid":{"rendered":"https:\/\/babel.isa.uma.es\/kipr\/?p=1877"},"modified":"2025-02-20T10:28:47","modified_gmt":"2025-02-20T09:28:47","slug":"rl-for-multiple-tasks-in-the-case-of-quadrotors","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=1877","title":{"rendered":"RL for multiple tasks in the case of quadrotors and a short state of the art about the general problem"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">J. Xing, I. Geles, Y. Song, E. Aljalbout and D. Scaramuzza, <strong>Multi-Task Reinforcement Learning for Quadrotors,<\/strong> IEEE Robotics and Automation Letters, vol. 10, no. 3, pp. 2112-2119, March 2025, DOI: <a href=\"https:\/\/doi.org\/10.1109\/LRA.2024.3520894\">10.1109\/LRA.2024.3520894<\/a>.<\/h4>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control, leveraging the shared physical dynamics of the platform to enhance sample efficiency and task performance. By employing a multi-critic architecture and shared task encoders, our framework facilitates knowledge transfer across tasks, enabling a single policy to execute diverse maneuvers, including high-speed stabilization, velocity tracking, and autonomous racing. Our experimental results, validated both in simulation and real-world scenarios, demonstrate that our framework outperforms baseline approaches in terms of sample efficiency and overall task performance. Video is available at https:\/\/youtu.be\/HfK9UT1OVnY.<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>J. Xing, I. Geles, Y. Song, E. Aljalbout and D. Scaramuzza, Multi-Task Reinforcement Learning for Quadrotors, IEEE Robotics and Automation <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=1877\" class=\"more-link\"><span>Read More &rarr;<\/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":[18,14],"tags":[297],"class_list":["post-1877","post","type-post","status-publish","format-standard","hentry","category-applications-of-reinforcement-learning-to-control-engineering","category-applications-of-reinforcement-learning-to-robots","tag-quadrotor"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1877"}],"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=1877"}],"version-history":[{"count":3,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1877\/revisions"}],"predecessor-version":[{"id":1880,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1877\/revisions\/1880"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1877"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1877"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1877"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}