This study presents an error recovery architecture for future variable-mix variable-volume production based on cyber-physical-human systems (CPHS). It focuses on bin picking, which is a crucial manufacturing process for handling bulk industrial parts during kitting. One of the main challenges in bin picking is efficiently introducing perturbations to arbitrarily placed parts and make all parts graspable and resolve deadlock situations. For example, a suction-type gripper is advantageous for handling objects stably without geometric models as it can easily adhere to flat surfaces. However, the success rate of bin picking using a suction gripper depends on the orientation of the target part. If its flat graspable surface does not face upward, the suction gripper cannot attach to and pick up the target object, resulting in a deadlock. In this case, an external force must be applied to change the orientation of the target object to resume the bin-picking process. A conventional, albeit inefficient, solution is a human worker or an additional mechanism that perturbs the container. Because applying such a perturbation by a versatile robot is challenging due to the limited physical information, a promising approach for efficient error recovery is a combination of human remote instruction and automated trajectory planning. This study developed a CPHS-based architecture to facilitate error recovery through smooth human-robot collaboration. We perform three experiments to demonstrate the feasibility of this approach for efficient error recovery.

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