Obstacle Avoidance Using Dynamic Movement Primitives and Reinforcement Learning

Dominik Urbaniak

doi:10.1109/LRA.2026.3671568

Obstacle Avoidance Using Dynamic Movement Primitives and Reinforcement Learning

2026-03-09

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DOI：10.1109/LRA.2026.3671568

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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 guided by model-specific cost designs, resulting in 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 and achieving comparable performance to the CHOMP planner, while supporting multi-modal trajectory generation for different obstacle geometries and end-effector dimensions.

Keywords:

Dynamic movement primitives

learning from demonstration

motion planning

obstacle avoidance

reinforcement learning

Obstacle Avoidance Using Dynamic Movement Primitives and Reinforcement Learning

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