用强化学习优化轨迹，单次示范即可生成避障路径。
Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning
- 用动态运动基元编码示范，再通过强化学习迭代优化
- 仅需一次示范就能生成多样避障轨迹，比RRT-Connect更快更短
- 适合机器人在复杂障碍环境中快速规划多模态路径
基于学习的运动规划能快速生成近似最优轨迹，但通常需要大量训练数据或昂贵的人类示范。本文提出一种新方法：仅需一次人工示范，即可快速生成平滑、近似最优且无碰撞的三维笛卡尔轨迹。该示范被编码为动态运动基元（DMP），并利用基于策略的强化学习进行迭代重塑，生成适用于不同障碍配置的多样化轨迹数据集。随后训练神经网络，输入为从点云自动提取的障碍尺寸与位置参数，输出为生成轨迹的DMP参数。该方法在仿真和真实机器人实验中均验证有效，相较RRT-Connect基线，在计算时间、执行时间和轨迹长度上均有提升，同时支持多种障碍几何形状和末端执行器尺寸下的多模态路径生成。视频与代码已开源。
原文摘要 · Abstract (English)
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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