Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.

人机协作（HRC）是一个术语，用于描述机器人和人共同努力实现目标的任务。与传统的工业机器人不同，协作机器人需要自适应。能够改变他们的方法以更好地适应人类伴侣的情况和需求。由于传统的编程技术可能难以应付所需的复杂性，因此一种新兴的方法是通过观察人类的表演并模仿动作来学习技能。通常称为从示范学习（LfD）。在这项工作中，我们提出了一种LfD方法，该方法结合了集成的机器学习算法（即随机森林（RF））和随机回归，并使用了从人类演示中捕获的触觉信息。使用两个协作任务评估了所提出方法的功能；协同操作一个物体（由人类提供指导，但机器人负责处理物体的重量），并协同组装简单的互锁部件。所提出的方法被证明可以模仿学习。解释人类的行为并在各种初始和最终条件下产生等效的机器人运动。在验证了集成机器学习可用于实际机器人问题后，我们提出了利用加权随机森林（WRF）的进一步扩展，该扩展基于每个树的性能将权重附加到每棵树上。结果表明，在HRC任务中，WRF方法优于RF。解释人类的行为并在各种初始和最终条件下产生等效的机器人运动。在验证了集成机器学习可用于实际机器人问题之后，我们提出了利用加权随机森林（WRF）的进一步扩展，该扩展基于每个树的性能将权重附加到每棵树上。结果表明，在HRC任务中，WRF方法优于RF。解释人类的行为并在各种初始和最终条件下产生等效的机器人运动。在验证了集成机器学习可用于实际机器人问题后，我们提出了利用加权随机森林（WRF）的进一步扩展，该扩展基于每个树的性能将权重附加到每棵树上。结果表明，在HRC任务中，WRF方法优于RF。