Periodic Reporting for period 2 - I.AM. (Impact Aware Manipulation by Dexterous Robot Control and Learning in Dynamic Semi-Structured Logistic Environments)
Periodo di rendicontazione: 2021-07-01 al 2022-12-31
In particular, under the lead ot the TU/e, the consortium has created procedures to estimate object-enviroment and robot-environment impact dynamics and impact model parameters from motion capture and robot encoder data. Furthermore, a procedure to identify stiffness and damping properties of a suction cup gripper under partial vacuum from motion capture data has been devised. Procedures to learn a dynamical system to perform swift motions such as tossing, hitting, and grabbing of relative heavy objects have been developed by EPFL. Furthermore, a method to obtain more accurate robot-enviroment dynamic intereaction forces and faster impact detection (from hundreds of second to a few milliseconds) has been proposed and validated by TUM, based on data from the joint encoders, joint torque, and an additional external IMU positioned on the end-effector. An impact-aware QP robot control framework has been developed, including learning procedures to estimate dynamics properties of soft objects to perform impacts without violating hardware limitations, by CNRS. Algoryx has been key in developing a composite software which allows to represents the dynamic and geometric properties of simulation scene while also allowing for integration with QP robot control as well as in supporting TU/e in creating a data collection and storage procedure for impact motions. Some of the collected data has already been openly released, encouraging research on impact-aware robotics. Supported by all I.AM. project partners, Smart Robotics and Vanderlande have been instrumental for the setting up of a laboratory on TU/e campus for the TOSS And BOX scenarios, provided with a large conveyor belt, two robot arms (UR10 and Panda), and a motion capture system;