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

Robots are increasingly exploited in production plants. Within the Industry 4.0 paradigm, the robot complements the human's capabilities, learning new tasks and adapting itself to compensate for uncertainties. With this aim, the presented paper focuses on the investigation of machine learning techniques 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 Markov Model approach), and (2) an autonomous optimization procedure of the task execution (exploiting Bayesian Optimization). To validate the proposed methodology, an assembly task has been selected as a reference application. The task consists of mounting a gear into its square-section shaft on a fixed base to simulate the assembly of a gearbox. A Franka EMIKA Panda manipulator has been used as a test platform, implementing the proposed methodology. The experiments, carried out on a population of 15 subjects, show the effectiveness of the proposed strategy, making the robot able to learn and optimize its behavior to accomplish the assembly task, even in the presence of task uncertainties. (C) 2020 The Author(s). Published by Elsevier B.V.

Automated summary

Robots are playing an increasingly important role in production, learning and optimizing industrial assembly tasks to adapt to uncertainties. Research shows that using machine learning techniques, robots can learn and optimize task execution through sensorless control, improving efficiency and accuracy.