Abstract 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.

中文翻译：

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通过贝叶斯优化的不确定性适应增强了无传感器装配任务学习中的人机协作

摘要 机器人越来越多地用于生产工厂。在工业 4.0 范式中，机器人补充了人类的能力，学习新任务并调整自身以补偿不确定性。为此，本文重点研究机器学习技术，使无传感器机器人能够学习和优化工业装配任务。依靠无传感器笛卡尔阻抗控制，定义了两个主要贡献：（1）基于少数人的演示（利用隐马尔可夫模型方法）的任务轨迹学习算法，以及（2）任务执行的自主优化过程（利用贝叶斯优化）。为了验证所提出的方法，已选择装配任务作为参考应用程序。该任务包括将齿轮安装到固定底座上的方形轴中，以模拟齿轮箱的组装。Franka EMIKA Panda 机械手已被用作测试平台，实现了所提出的方法。对 15 名受试者进行的实验表明了所提出策略的有效性，即使在存在任务不确定性的情况下，机器人也能够学习和优化其行为以完成组装任务。