Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization

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Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization

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Roveda, Loris / Magni, Mauro / Cantoni, Martina / Piga, Dario / Bucca, Giuseppe

Robots are increasingly exploited in production plants. Within the Industry 4.0 paradigm, the robotcomplements the human’s capabilities, learning new tasks and adapting itself to compensate foruncertainties. With this aim, the presented paper focuses on the investigation of machine learningtechniques to make a sensorless robot able to learn and optimize an industrial assembly task.Relying on sensorless Cartesian impedance control, two main contributions are defined: (1) a task-trajectory learning algorithm based on a few human’s demonstrations (exploiting Hidden MarkovModel approach), and (2) an autonomous optimization procedure of the task execution (exploitingBayesian Optimization). To validate the proposed methodology, an assembly task has been selected asa reference application. The task consists of mounting a gear into its square-section shaft on a fixedbase to simulate the assembly of a gearbox. A Franka EMIKA Panda manipulator has been used as atest platform, implementing the proposed methodology. The experiments, carried out on a populationof 15 subjects, show the effectiveness of the proposed strategy, making the robot able to learn andoptimize its behavior to accomplish the assembly task, even in the presence of task uncertainties.