Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning

2025/10/10 by Urbaniak, Dominik, Agostini, Alejandro, Ramon, Pol +3
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2510.09254

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.

Citations

• Sampling-Based Motion Planning: A Comparative Review
• Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
• Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning
• Motion Policy Networks
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Motion Planning Networks: Bridging the Gap Between Learning-based and\n Classical Motion Planners
• Open3D: A Modern Library for 3D Data Processing
• Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
• Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
• Sampling-based Algorithms for Optimal Motion Planning
• Sampling-based algorithms for optimal motion planning

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