Fluid coordination of human-robot teams

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 235-239).I envision a future where collaboration between humans and robots will be indispensable to our work in numerous domains, ranging from surgery to space exploration. The success of these systems will depend in part on the ability of robots to integrate within existing human teams. The goal of this thesis is to develop robot partners that we can work with easily and naturally, inspired by the way we work with other people. My hypothesis is that human-robot team performance improves when a robot teammate emulates the effective coordination behaviors observed in human teams. I design and evaluate Chaski, a robot plan execution system that uses insights from human-human teaming to make human-robot teaming more natural and fluid. Chaski is a task-level executive that enables a robot to robustly anticipate and adapt to other team members. Chaski also emulates a human's response to implicit communications, including verbal and gestural cues, and explicit commands. Development of such an executive is challenging because the robot must be able to make decisions very quickly in response to a human's actions. In the past, the ability of robots to demonstrate these capabilities has been limited by the time-consuming computations required to anticipate a large set of possible futures. These computations result in execution delays that endanger the robot's ability to fulfill its role on the team. I significantly improve the ability of a robot to adapt on-the-fly by generalizing the state-of-the-art in dynamic plan execution to support just-in-time task assignment and scheduling. My methods provide a novel way to represent the robot's plan compactly. This compact representation enables the plan to be incrementally updated very quickly. I empirically demonstrate that, compared to prior work in this area, my methods increase the speed of online computation by one order of magnitude on average. I also show that 89% of moderately-sized benchmark plans are updated within human reaction time using Chaski, compared to 24% for prior art. I evaluate Chaski in human subject experiments in which a person works with a mobile and dexterous robot to collaboratively assemble structures using building blocks. I measure team performances outcomes for robots controlled by Chaski compared to robots that are verbally commanded, step-by-step by the human teammate. I show that Chaski reduces the human's idle time by 85%, a statistically significant difference. This result supports the hypothesis that human-robot team performance is improved when a robot emulates the effective coordination behaviors observed in human teams.by Julie A. Shah.Ph.D

Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy

We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.ABB Inc.U.S. Commercial Regional CenterAlexander S. Onassis Public Benefit Foundatio

Comfort, Acceptance, and Preferences: The Designing of a Human-Robot Workstation that Puts the Human First

The purely manual versions of manufacturing are becoming less common, and automation is increasing. With mass production moving towards mass customization this change is inevitable. However, a future of automation does not mean that operators are going to be replaced. In fact, it means that operators’ jobs are about to become more meaningful and value adding for themselves and the company. Soon majority of the jobs where operators do the repetitive mindless task of a robot will be gone. It is time for Human-Robot Collaboration (HRC) to advance the assembly process to the next level. Human-robot teams will be formed to combine their individual strengths and compensate for their individual weaknesses. The success of human-robot collaboration heavily depends on the operator’s acceptance of the robot. Unfortunately, operators are worried about robots taking their jobs, diminishing their self-worth, and putting them in danger. To mitigate these concerns the objective of this thesis is to model the design requirements of a human-robot collaborative assembly station that appeals to operator comfort and acceptance while still supporting the needs of production. A combination of fulfilling requirements, providing the operator with a better understanding of the robot’s capabilities, and providing the operator with limited control could lead to an improved interaction between operators and robots. Operator feedback was obtained from professionals in industry through surveys and structured interviews. Then the Quality Function Deployment (QFD) tool was used to translate the vague operator requirements captured in the survey responses and interviews into product-relevant parameters that designers and engineers can apply. The nine operator requirements derived for working with robots are safety, dependability, value-adding, controllability, helpfulness, easy to communicate with, teachable, easy to fix, and enjoyable to work with

A Realistic Simulation for Swarm UAVs and Performance Metrics for Operator User Interfaces

Robots have been utilized to support disaster mitigation missions through exploration of areas that are either unreachable or hazardous for human rescuers [1]. The great potential for robotics in disaster mitigation has been recognized by the research community and during the last decade, a lot of research has been focused on developing robotic systems for this purpose. In this thesis, we present a description of the usage and classification of UAVs and performance metrics that affect controlling of UAVs. We also present new contributions to the UAV simulator developed by ECSL and RRL: the integration of flight dynamics of Hummingbird quadcopter, and distance optimization using a Genetic algorithm