Adaptive Coordination Strategies for Human-Robot Handovers

Abstract

Handovers of objects are critical interactions that frequently arise in physical collaborations. In such interactions, humans naturally monitor the pace and workload of their partners and adapt their handovers accordingly. In this paper, we investigate how robots designed to engage in physical collaborations may achieve similar adaptivity in performing handovers. To that end, we collected and analyzed data from human dyads performing a common household task-unloading a dish rack-where receivers had different levels of task demands. We identified two coordination strategies that enabled givers to adapt to receivers' task demands. We then formulated and implemented these strategies on a robotic manipulator. The implemented autonomous system was evaluated in a human-robot interaction study against two baselines that use "proactive" and "reactive" coordination methods. The results show a tradeoff between team performance and user experience when human receivers had greater task demands. In particular, the proactive method provided the greatest levels of team performance but offered the poorest user experience compared to the reactive and adaptive methods. The reactive method, while improving user experience over the proactive method, resulted in the poorest team performance. Our adaptive method maintained this improved user experience while offering an improved team performance compared to the reactive method. Our findings offer insights into the tradeoffs involved in the use of these methods and inform the future design of handover interactions for robots.

Human social motor solutions for human–machine interaction in dynamical task contexts

“…As demonstrated by human "dynamic clamp" methodologies, the use of artificial agents as members of dyadic or group contexts can also allow researchers to deduce unidirectional effects of the (artificial) member on the rest of the group to better understand the processes underlying social interactions and diagnosis of social disorders (23,43,48,70). Outside of using assistive artificial systems for skill acquisition or training to prepare for future social encounters, embodying robotic systems with human-like dynamics may facilitate action prediction and safety in domains, such as advanced manufacturing (e.g., handing objects from robot to human), where the movement capabilities of such systems are not readily apparent (71,72).…”

Embodiment in Socially Interactive Robots

“…To allow for less trained users to effectively interact with and leverage industrial robot systems, HRI researchers have been working on various approaches to human-robot collaboration, such as using cross-training to improve task sharing between human and robot workers Nikolaidis and Shah (2013), planning shared work plans taking human ergonomics into consideration Pearce et al (2018), and adapting robot actions to human motion, availability, adaptability, and intent Huang and Mutlu (2016); Lasota and Shah (2015); Huang et al (2015b); Nikolaidis et al (2017).…”

Symbiotic human-robot collaborative assembly

“…According to the state-of-the-art, robot actions can be optimised on the basis of human behaviour [75,180,194]. A step forward in this direction is represented by a set of studies [81,181], showing that the success of a human-robot team also depends on the human level of adaptability in different situations. The robot should be able to interpret the setting and in particular, the level of human adaptability, and act properly.…”