Adaptive Coordination Strategies for Human-Robot Handovers

By Chien-Ming Huang, Maya Cakmak, Bilge Mutlu. 2015

154 citations recorded by the source corpus.

—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.

Subfields: Artificial Intelligence, Social Psychology, Reinforcement Learning, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Social Psychology

In Reinforcement Learning

In Robotics & Control

Citations in the corpus

Learning Dynamic Robot-to-Human Object Handover from Human Feedback (2016, background): “Earlier work on object transfer generally assumes that the object remains stationary once joint contact is established and relies on handcrafted controllers [1, 5, 14, 21].”
Initiative in robot assistance during collaborative task execution (2016, background): “Others focus on the coordination of micro-interactions that occur during collaboration, such as object hand-overs, using gaze [14] or adapting timing of motions to the human’s state [15].”
Symbiotic human-robot collaborative assembly (2019, background): “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.”
Evaluating Fluency in Human–Robot Collaboration (2019, background): “Huang et al. assessed human–robot handovers in unloading a dish rack [20].”
Real-time natural language corrections for assistive robotic manipulators (2017, background): “While recent work has shown robotic manipulators capable of human-scale household tasks such as unloading a dishwasher (Huang et al., 2015) and folding laundry (Maitin-Shepard et al.”
A Survey of Methods for Safe Human-Robot Interaction (2017, methodology): “Joosse et al. (2013), for example, conducted human-subject experiments in which the agent type (human or robot) and approach speed (slow or fast) were varied during an invasion of a participant’s personal space by a robot.”