This paper proposes a new control framework for manipulating soft objects. A Deep Reinforcement Learning (DRL) approach is used to make the shape of a deformable object reach a set of desired points by controlling a robotic arm which manipulates it. Our framework is more easily generalizable than existing ones: it can work directly with different initial and desired final shapes without need for relearning. We achieve this by using learning parallelization, i.e., executing multiple agents in parallel on various environment instances. We focus our study on deformable linear objects. These objects are interesting in industrial and agricultural domains, yet their manipulation with robots, especially in 3D workspaces, remains challenging. We simulate the entire environment, i.e., the soft object and the robot, for the training and the testing using PyBullet and OpenAI Gym. We use a combination of state-of-the-art DRL techniques, the main ingredient being a training approach for the learning agent (i.e., the robot) based on Deep Deterministic Policy Gradient (DDPG). Our simulation results support the usefulness and enhanced generality of the proposed approach.

翻译：本文提出了一种用于操控软体物体的新型控制框架。采用深度强化学习方法，通过控制操作软体物体的机器人臂，使可变形物体的形状达到一组目标点。与现有方法相比，我们的框架具有更强的泛化能力：可直接应用于不同初始形状和目标形状而无需重新学习。我们通过并行化学习实现这一优势，即在多个环境实例上并行执行多个智能体。本研究聚焦于可变形线状物体，这类物体在工业和农业领域具有重要应用价值，但其机器人操控（尤其是在三维工作空间中）仍具挑战性。我们使用PyBullet和OpenAI Gym仿真了整个环境（包括软体物体与机器人）以进行训练与测试。采用多种前沿深度强化学习技术组合，核心是基于深度确定性策略梯度（DDPG）的智能体训练方法。仿真结果验证了所提方法的有效性与增强的泛化能力。