Kinesthetic Skill Refinement for Error Recovery in Skill-Based Robotic Systems

Skill-based robotic systems can perform tasks more flexibly than typical industrial manipulators. These systems are equipped with a repertoire of reusable skills and take advantage of a knowledge base about their workspace. That being so, the robot can execute tasks composed of a combination of different skills, tools, and objects without having to be reprogrammed explicitly for each task. Despite its advantages, these systems are affected by modeling errors and an inaccurate knowledge base. Such issues lead to failures in production. Since automated error detection is still an open problem, they often have to be solved by a robot operator. That is generally done by accessing the implementation of the faulty task and determining what to change to achieve the desired outcome, which is time-consuming and requires expertise. The proposed work aims to provide the robot operator with a faster and more intuitive error recovery method for a skill-based system via GUI-assisted kinesthetic refinement of robot skills. Furthermore, partially automated error recovery strategies are included. First, the targeted skills can be composed of an arbitrary number of steps with corresponding reversion behaviors. Second, consecutive human corrections on different parts of a given object are analyzed to infer a possible object pose error. Experiments show that our method takes one-fourth of the time required for conventional manual correction.