Performance guaranteed human-robot collaboration with POMDP supervisory control

Abstract

Abstract Human-Robot Collaboration (HRC) studies how to achieve effective collaborations between human and robots to take advantage of the flexibility from human and the autonomy from robots. Many applications involving HRC, such as joint assembly manufacturing systems and advanced driver assistance systems, need to achieve high-level tasks in a provably correct manner. These applications motivate the requirements of HRC to have the performance guarantee to assure the task completion and safety of both human and robots. In this paper, a correct-by-design HRC framework is built to enable a performance guaranteed HRC. To model the uncertainties from human, robots and the environment, partially observable Markov decision process (POMDP) is used as the model. Based on the POMDP modeling, a supervisory control framework is applied and designed to be adaptive to modeling uncertainties. To reduce the model checking complexity involved in the supervisor synthesis process, an abstraction method for POMDP is integrated to find a quotient system with a smaller size of state space. Based on the abstraction method, a verification adaptation technique is developed with simulation relation checking algorithms to deal with possible online model changing. If the verification adaptation indicates the necessity to update the supervisor, supervisor adjustment methods are given. Altogether, it leads to a semi-online adaptation approach for system model changing. Examples are given to illustrate this framework.