Decision-Making Authority, Team Efficiency and Human Worker Satisfaction in Mixed Human-Robot Teams

has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with robotic counterparts. We hypothesized that giving workers partial decision-making authority over a task allocation process for the scheduling of work would achieve such a maximization, and conducted an experiment on human subjects to test this hypothesis. We found that an autonomous robot can outperform a worker in the execution of part or all of the task allocation (p < 0.001 for both). However, rather than finding an ideal balance of control authority to maximize worker satisfaction, we observed that workers preferred to give control authority to the robot (p < 0.001). Our results indicate that workers prefer to be part of an efficient team rather than have a role in the scheduling process, if maintaining such a role decreases their efficiency. These results provide guidance for the successful introduction of semi-autonomous robots into human teams. I

Learning task-oriented grasp heuristics from demonstration

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 79-82).When people plan their motions for dexterous work, they implicitly consider the next likely step in the action sequence. Almost without conscious thought, we select a grasp that meets the implicit constraints related to the task to be performed. A robot tasked with dexterous manipulation should likewise aim to grasp the intended object in a way that makes the next step straightforward. In some cases, lack of consideration of these implicit constraints can result in situations in which the object cannot be manipulated in the desired manner. While recent work has begun to address task dependent constraints, they require direct specification of task constraints or rely on grasp datasets with manually defined task labels. In this thesis, we present a framework that leverages human demonstration to learn task-oriented grasp heuristics for a set of known objects in an unsupervised manner and defined a procedure to instantiate grasps from these learned models. Equating distinct motion profiles with the execution of distinct tasks, our approach leverages the motion during human demonstration in order to partition the accompanying grasp examples into tasks in an unsupervised manner through the incorporation of unsupervised motion clustering algorithms into a grasp learning pipeline. In order to evaluate the framework, a set of human demonstrations of real world manipulation tasks were collected. The framework with unsupervised task clustering produced comparable results to the semi-supervised condition. This translated to the discovery of the correct relationship between the tasks and objects, with the distributions of the resultant grasp point models following intuitive heuristic rules (e.g. handle grasps for tools). The grasps instantiated from these grasp models followed the learned heuristics, but had some limitations due to the choice of grasp model and the instantiation method utilized. Overall, this work demonstrates that the inclusion of unsupervised motion clustering techniques into a grasp learning pipeline can assist in the production of task-oriented models without the typical overhead of direct task constraint encoding or hand labeling of datasets.by Reymundo A. Gutierrez.M. Eng

Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (p<0.001 for both), and that people preferred to cede their control authority to the robot $$(p<0.001)$$ (p<0.001). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate $$(p< 0.001)$$(p<0.001). In post hoc analysis, we found that people were more likely to assign a disproportionate amount of the work to themselves when working with a robot $$(p<0.01)$$(p<0.01) rather than human teammates only. Based upon our findings, we provide design guidance for roboticists and industry practitioners to design robotic assistants for better integration into the human workplace