Robotic manipulation of deformable objects using vision and touch sensing

迄今为止，对机器人操作的研究主要集中在处理刚性物体上。然而，许多重要的应用领域需要操纵非刚性或可变形的物体，如织物、软管和电缆。这样的对象处理起来更具挑战性，因为它们可以表现出更大的行为多样性。机器人操作已广泛应用于工业任务，如仓库操作和装配。然而，对于目前的机器人平台来说，处理柔性物体仍然是一个挑战。这个博士项目将研究使用一个由配备触觉传感器和立体摄像机的机器人操纵系统来操纵织物。深度强化学习将用于提高机器人的操作性能，通过与模拟环境中的物体交互实现在线学习。通过施加挤压、滑动、抓握和移动等过程，可以估计织物的性能和状态。仿真环境中的优化模型将在真实机器人平台上进行部署和测试。该项目将为仓库操作和柔性材料组装提供解决方案。

Research on robotic manipulation has mainly focused on handling rigid objects so far. However, many important application domains require manipulating non-rigid or deformable objects, such as fabrics, hoses and cables. Such objects are far more challenging to handle, as they can exhibit a much greater diversity of behaviours. Robotic manipulation has been widely applied in industry for tasks such as warehouse operation and assembly. However, handling flexible objects is still challenging for current robotic platforms.This PhD project will investigate the manipulation of fabrics using a system comprised of robot manipulators equipped with tactile sensors and stereo cameras. Deep reinforcement learning will be used to improve the manipulation performance of the robot, enabling online learning through interaction with objects in a simulated environment.By exerting procedures like squeezing, sliding, grasping and moving, the properties and states of the fabrics can be estimated. The optimized models in the simulation environment will then be deployed and tested on the real robot platforms. The project will create solutions to warehouse operations and assembly of flexible materials.