Reliable Grasping with Tactile Sensing

Abstract

For robots to seamlessly integrate into everyday life and interact with various objects in unstructured environments like homes, they must exhibit extreme reliability. However, robots today often struggle to recover from errors, and understanding the root causes of these failures remains challenging. This thesis addresses robotic reliability in grasping and manipulation through the application of tactile sensing. Specifically, I have designed a highly instrumented robot hand, developed a stochastic friction model for grasp slip prediction, and employed tactile sensing to create physics-machine learning hybrid models. These models aim to predict the stability of anticipated grasping tasks and determine extrinsic contact locations. Our findings reveal that by modeling friction as a stochastic variable, we can quantify uncertainties in grasping and manipulation more accurately. For stability prediction, our hierarchical physics-machine learning hybrid approach proves effective in addressing performance, data size requirements, interpretability, and generalizability. Overall, reliability is crucial for robots to effectively assist humans in various settings, including hospitals, elderly care facilities, and disaster sites. This thesis offers a comprehensive framework for understanding the role of tactile sensing in enhancing reliability prediction in robotic grasping and manipulation.