We present an approach geared toward estimating task execution confidence for robotic bin-picking applications. This requires estimating execution confidence for all constituent subtasks including part recognition and pose estimation, singulation, transport, and fine positioning. This paper is focussed on computing associated confidence parameters for the part recognition and pose estimation subtask. In particular, our approach allows a robot to evaluate how good the part recognition and pose estimation is, based on a confidence-measure, and thereby determine whether to proceed with the task execution (part singulation) or to request help from a human in order to resolve the associated failure. The value of a mean-square distance metric at a local minimum where the part matching solution is found is used as a surrogate for the confidence parameter. Experiments with a Baxter robot are used illustrate our approach.

We present an approach geared toward estimating task execution confidence for robotic bin-picking applications. This requires estimating execution confidence for all constituent subtasks including part recognition and pose estimation, singulation, transport, and fine positioning. This paper is focussed on computing associated confidence parameters for the part recognition and pose estimation subtask. In particular, our approach allows a robot to evaluate how good the part recognition and pose estimation is, based on a confidence-measure, and thereby determine whether to proceed with the task execution (part singulation) or to request help from a human in order to resolve the associated failure. The value of a mean-square distance metric at a local minimum where the part matching solution is found is used as a surrogate for the confidence parameter. Experiments with a Baxter robot are used illustrate our approach.