Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning

• URL: http://arxiv.org/abs/2510.09254v1
• Date: Fri, 10 Oct 2025 10:51:42 GMT
• Title: Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning
• Authors: Dominik Urbaniak, Alejandro Agostini, Pol Ramon, Jan Rosell, Raúl Suárez, Michael Suppa,
• Abstract summary: This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a single artificial demonstration.<n>The demonstration is encoded as a Dynamic Movement Primitive (DMP) and iteratively reshaped using policy-based reinforcement learning.<n>The approach is validated in simulation and real-robot experiments, outperforming a RRT-Connect baseline in terms of computation and execution time.
• Score: 36.09105994195904
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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