{"ID":2857507,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09254","arxiv_id":"2510.09254","title":"Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning","abstract":"Learning-based motion planning can quickly generate near-optimal trajectories. However, it often requires either large training datasets or costly collection of human demonstrations. This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a single artificial demonstration. The demonstration is encoded as a Dynamic Movement Primitive (DMP) and iteratively reshaped using policy-based reinforcement learning to create a diverse trajectory dataset for varying obstacle configurations. This dataset is used to train a neural network that takes as inputs the task parameters describing the obstacle dimensions and location, derived automatically from a point cloud, and outputs the DMP parameters that generate the trajectory. The approach is validated in simulation and real-robot experiments, outperforming a RRT-Connect baseline in terms of computation and execution time, as well as trajectory length, while supporting multi-modal trajectory generation for different obstacle geometries and end-effector dimensions. Videos and the implementation code are available at https://github.com/DominikUrbaniak/obst-avoid-dmp-pi2.","short_abstract":"Learning-based motion planning can quickly generate near-optimal trajectories. However, it often requires either large training datasets or costly collection of human demonstrations. This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a si...","url_abs":"https://arxiv.org/abs/2510.09254","url_pdf":"https://arxiv.org/pdf/2510.09254v1","authors":"[\"Dominik Urbaniak\",\"Alejandro Agostini\",\"Pol Ramon\",\"Jan Rosell\",\"Raúl Suárez\",\"Michael Suppa\"]","published":"2025-10-10T10:51:42Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":608457,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2857507,"paper_url":"https://arxiv.org/abs/2510.09254","paper_title":"Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning","repo_url":"https://github.com/DominikUrbaniak/obst-avoid-dmp-pi2","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}