Research Center "Enrico Piaggio", University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy.
Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, via Morego, 30, Genova, 16163, Italy.
J Neuroeng Rehabil. 2020 May 13;17(1):63. doi: 10.1186/s12984-020-00680-8.
Human-likeliness of robot movements is a key component to enable a safe and effective human-robot interaction, since it contributes to increase acceptance and motion predictability of robots that have to closely interact with people, e.g. for assistance and rehabilitation purposes. Several parameters have been used to quantify how much a robot behaves like a human, which encompass aspects related to both the robot appearance and motion. The latter point is fundamental to allow the operator to interpret robotic actions, and plan a meaningful reactions. While different approaches have been presented in literature, which aim at devising bio-aware control guidelines, a direct implementation of human actions for robot planning is not straightforward, still representing an open issue in robotics.
We propose to embed a synergistic representation of human movements for robot motion generation. To do this, we recorded human upper-limb motions during daily living activities. We used functional Principal Component Analysis (fPCA) to extract principal motion patterns. We then formulated the planning problem by optimizing the weights of a reduced set of these components. For free-motions, our planning method results into a closed form solution which uses only one principal component. In case of obstacles, a numerical routine is proposed, incrementally enrolling principal components until the problem is solved with a suitable precision.
Results of fPCA show that more than 80% of the observed variance can be explained by only three functional components. The application of our method to different meaningful movements, with and without obstacles, show that our approach is able to generate complex motions with a very reduced number of functional components. We show that the first synergy alone accounts for the 96% of cost reduction and that three components are able to achieve a satisfactory motion reconstruction in all the considered cases.
In this work we moved from the analysis of human movements via fPCA characterization to the design of a novel human-like motion generation algorithm able to generate, efficiently and with a reduced set of basis elements, several complex movements in free space, both in free motion and in case of obstacle avoidance tasks.
机器人运动的拟人化是实现安全有效的人机交互的关键组成部分，因为它有助于提高必须与人密切交互的机器人的接受度和运动可预测性，例如用于辅助和康复目的。已经使用了几个参数来量化机器人的拟人化程度，这些参数涵盖了与机器人外观和运动相关的各个方面。后者对于允许操作员解释机器人的动作并规划有意义的反应至关重要。虽然文献中提出了不同的方法来设计生物感知控制准则，但直接为机器人规划实施人类动作并不简单，在机器人学中仍然是一个未解决的问题。
我们提出了一种用于机器人运动生成的人类运动协同表示方法。为此，我们记录了人类在日常生活活动中的上肢运动。我们使用功能主成分分析 (fPCA) 提取主要运动模式。然后，我们通过优化这些组件的一个简化集合的权重来制定规划问题。对于自由运动，我们的规划方法得到一个封闭形式的解，该解仅使用一个主成分。在存在障碍物的情况下，提出了一种数值例程，逐步注册主成分，直到以适当的精度解决问题。
fPCA 的结果表明，只有三个功能组件就可以解释超过 80%的观察到的方差。我们的方法应用于具有和不具有障碍物的不同有意义的运动，表明我们的方法能够使用非常少的功能组件生成复杂的运动。我们表明，仅第一个协同作用就可以将成本降低 96%，并且三个组件能够在所有考虑的情况下实现令人满意的运动重建。
在这项工作中，我们从通过 fPCA 特征分析人类运动转变为设计一种新颖的类人运动生成算法，该算法能够高效地生成具有减少的基础元素集的几个复杂运动，无论是在自由空间中的自由运动还是在避免障碍物任务中。
