Continuous tactile sensing for enhanced human-robot collaboration

Collaborative manipulation of objects is usually a trivial activity for humans but is still very challenging for robots. Such tasks involve many complex aspects, such as human and object safety, social and handling context, grasping stability, slip detection, and ergonomics. Although huge research efforts have been devoted over decades to endow robots with the skills required for grasping, manipulation, sharing of objects, and collaboration with humans, there is still a need for reliable systems capable of reacting to unexpected events. As for humans, the sense of touch is essential for robots to perform many tasks as it provides information that can not be obtained through contactless sensing modalities. Thus, recent trends in robotics research explore the use of tactile sensing in human-robot object manipulation. An important aspect that is often overlooked in the existing literature is that tactile sensing is inherently sequential and therefore should be approached as a continuous process. The aim of this thesis is to explore continuous tactile sensing to enhance robot collaboration capabilities for object manipulation. The contribution of this work is threefold: firstly, an innovative multimodal technique that identifies the surface materials of objects using continuous tactile sensing is developed. Secondly, continuous tactile sensing is used to provide contact information to a control system that grasps objects of unknown geometry. Finally, an approach to hand over objects between xiii a robot and a human, relying on continuous tactile sensing, is developed to ensure the safety of the robot and the object during the transfer. In this thesis, the proposed approaches are evaluated on real physical robotic platforms. A comparison with the state-of-the art techniques in material recognition shows that the proposed multimodal approach enhances identification speed and accuracy. The experimental results also show excellent performance of the proposed approach for grasping objects even when information about their geometry is not available. Finally, the proposed object handover algorithm is proven to adapt to unexpected force perturbations on the object and release it in a timely manner without dropping. This work entails significant progress towards the development of autonomous robots that collaborate with humans in everyday tasks.