Perceived robot capability

Cha, Elizabeth; Dragan, Anca D.; Srinivasa, Siddhartha S.

doi:10.1109/roman.2015.7333656

Cited by 53 publications

(36 citation statements)

References 30 publications

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“…While our model assumes that the human may learn only the entries of the row played by the robot, there are cases where a robot action may affect entries that are associated with other actions, as well. For instance, Cha et al [2] have shown that conversational speech can affect human perception of robot capability in physical tasks. We are excited to explore the structure of probabilistic graphical models of human adaptation, and use the theoretical insights from this work to develop efficient algorithms for the robot.…”

Section: Resultsmentioning

confidence: 99%

“…We propose a model of human partial adaptation, where the human learns with probability α the entries of row ri that correspond to the robot action a R i played, and with probability (1 − α) none of the entries. We consider the following models, based on 2 We will use the terms 'reward' and 'payoff' interchangeably. 3 We will refer to this robot action as playing a row.…”

Section: Approachmentioning

confidence: 99%

“…Prior work has shown that there is often a disconnect between users' perceptions and a robot's true capability, mainly due to lack of experience with working with robots and to the influence of popular culture [1][2][3]. This gap in expectation can significantly reduce human-robot team performance [4].…”

Section: Introductionmentioning

confidence: 99%

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Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

Nikolaidis

Nath

Procaccia

et al. 2017

Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

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In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot

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Section: Resultsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

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Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

Nikolaidis

Nath

Procaccia

et al. 2017

Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

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“…Robots can forewarn users of possible failures through text [18] or con dence levels [7,15], trajectory timings [32], and actively choosing actions that showcase failure modes [23]. Se ing accurate expectations of robot capabilities is important for narrowing the gap between the perceived and true capabilities of the robot [6].…”

Section: Related Workmentioning

confidence: 99%

Expressing Robot Incapability

Kwon

Huang

Dragan

2018

Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

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Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future.

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“…Sciu i et al [2014], Zhou et al [2017] have developed expressive robotic lifting motions to help humans understand the weights of the objects that robots are manipulating. The ease with which a person could recognize a robot's goals by observing its action execution improves robot legibility [Dragan et al, 2013, Dragan and Srinivasa, 2014], predictability [Fisac et al, 2016, acceptance [Cha et al, 2015], and naturalness [Szafir et al, 2014], which are important for human recognition of robot tasks and human-robot collaboration [Powers andKiesler, 2006, Gielniak et al, 2013]. However, the prior works aim to make current executed behavior and goals more understandable and does not focus on helping people more easily predict future actions and generalize current behavior to new environments.…”

Section: Related Workmentioning

confidence: 99%

My Parents

2017

A Lady Mathematician in This Strange Universe: Memoirs

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First and foremost, I would like to express my sincere gratitude to my advisors Dr. Siddhartha Srinivasa and Dr. Stephanie Rosenthal, who have greatly helped me in motivating me to keep proactive and hardworking, shaping my interests in human-robot interaction, inspiring, supporting, and formalizing my research ideas, and directing me through social networking and career path planning. I am also extremely grateful to my commi ee member Dr. Reid Simmons for his ideas and feedbacks all along my Masters research, and to my colleagues Dr. Henny Admoni and Dr. Daqing Yi for their invaluable advices condensed from their solid technical skills, rich life experiences, and kindest hearts. Their vision, guidance and knowledge have been invaluable in my work and life. It has been a great honor to work with them. I would like to thank Rosario Scalise as my "wonder twin" collaborator throughout my thesis work. I enjoy bouncing my ideas with him who always delivers me his innovative and comprehensive explanations, in both natural language and demonstration on computer, optimized for the maximal clarity and fun. I also thank him for encouraging me to network with people in conferences as a sincere friend. Further, I would like to thank all my fellow lab-mates as well

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Perceived robot capability

Cited by 53 publications

References 30 publications

Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

Expressing Robot Incapability

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