We present a comprehensive approach to handle perception uncertainty to reduce failure rates in robotic bin-picking. Our focus is on mixed-bins. We identify the main failure modes at various stages of the bin-picking task and present methods to recover from them. If uncertainty in part detection leads to perception failure, then human intervention is invoked. Our approach estimates the confidence in the part match provided by an automated perception system, which is used to detect perception failures. Human intervention is also invoked if uncertainty in estimated part location and orientation leads to a singulation planning failure. We have developed a user interface that enables remote human interventions when necessary. Finally, if uncertainty in part posture in the gripper leads to failure in placing the part with the desired accuracy, sensor-less fine-positioning moves are used to correct the final placement errors. We have developed a fine-positioning planner with a suite of fine-motion strategies that offer different tradeoffs between completion time and postural accuracy at the destination. We report our observations from system characterization experiments with a dual-armed Baxter robot, equipped with a Ensenso three-dimensional camera, to perform bin-picking on mixed-bins.

We present an approach to resolve automated perception failures during bin-picking operations in hybrid assembly cells. Our model exploits complementary strengths of humans and robots. Whereas the robot performs binpicking and proceeds to the subsequent operation like kitting or assembly, a remotely located human assists the robot in critical situations by resolving any automated perception problems encountered during bin-picking. We present the design details of our overall system comprising an automated part recognition system and a remote user interface that allows effective information exchange between the human and the robot that is geared toward solutions that minimize human operator time in resolving the detected perception failures. We use illustrative real robot experiments to show that human-robot information exchange leads to improved bin-picking performance.

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