A general approach for generating artificial human-like motions from functional components of human upper limb movements

Anthropomorphism of artificial systems is a key enabling factor to ensure effective and compelling human–machine interactions in different domains, including immersive extended reality environments and cobotics applications. Among the different aspects that anthropomorphism refers to, the generation of human-like motions plays a crucial role. To this aim, optimization-based techniques, whose functional cost is devised from neuroscientific findings, or learning-based approaches have been proposed in literature. However, these methods come with limitations, e.g., limited motion variability or the need for high dimensional datasets. In previous works of our group, we proposed to exploit functional Principal Component Analysis (fPCA) of human upper limb movements, to extract principal motion modes in the joint domain and use them to directly embed the human-like behaviour in the planning algorithm. However, this approach faces with translational issues related to the computational burden and to the application to kinematic structures different from the one used to describe human movements. To overcome this problem, we propose a general framework to generate human-like motion directly in the Cartesian domain by exploiting fPCA. This solution permits to perform obstacle avoidance with low computational time and it can be applied to any kinematic chain. To prove the effectiveness of our approach, we tested it against a state-of-the-art human-like planning algorithm both in terms of the accuracy of target reaching and human-likeness features of the generated movement.