We present an approach to automatically learn a bimanual robotic cleaning task on compliant objects. One robot grasps the object, while the other robot cleans it. Given a part with unknown deformation characteristics, the system visually detects the regions to be cleaned, and generates plans for both the grasping and cleaning arms. As the system performs cleaning attempts and gains experience with multiple new parts, it learns models of the part deformation depending on the cleaning force and grasping parameters. A planner iteratively generates tool paths for both robots using the available knowledge to optimize the cleaning time, including (1) delays from regrasping a part to minimize deflection and (2) time taken for repeated cleaning attempts over regions that remained dirty. A nonparametric deflection model is learned separately for each part, with minimal assumptions of the material behavior. We demonstrate the approach on a system of two KUKA LWR iiwa robots and a set of thin planar parts. Results indicate that the system is effective at rapidly learning part deformation models to enable effective iterative cleaning performance.
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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