Robotic manipulation of deformable objects using vision and touch sensing

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.