Towards Unifying Human Likeness: Evaluating Metrics for Human-Like Motion Retargeting on Bimanual Manipulation Tasks

Generating human-like robot motions is pivotal for achieving smooth human-robot interactions. Such motions contribute to better predictions of robot motions by humans, thus leading to more intuitive interaction and increased ac- ceptability. Human likeness in robot motions has been conven- tionally measured and realized via the optimization of human- likeness metrics. However, the abundance of such metrics and the absence of standardized criteria impede their usage in novel contexts. In this work, we introduce a unified human- likeness metric built from a hierarchically weighted sum of individual metrics. The proposed metric is derived from a thorough analysis of eleven existing human-likeness criteria and is applicable across various tasks and robot models. We evaluate its performance in the context of motion retargeting of bimanual tasks with three different humanoid robots.