Adaptation to a humanoid robot in a collaborative joint task

Vannucci, Fabio; Sciutti, Alessandra; Jacono, Marco; Sandini, Giulio

doi:10.1109/roman.2017.8172327

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Still, Gombolay et al [65] also concluded that the operators still value human partners over robots in groups collaborations. Dragan et al [21] found that motion planning that communicates robot intent improves collaboration, and Vannucci et al [37] found that operators believe robot motions influence their actions, both of which are consistent with other research by Giuliani and Knoll. They found that when the robot partner is assigned either supportive of instructive role, the operator will adopt the opposite role [13,17].…”

Section: Test Resultssupporting

confidence: 74%

“…Freq. Intention recognition 51 [11, 15, 16, 22, 29, 31, 34, 35, 37, 38, 40-42, 46, 48, 53, 54, 56, 57, 64-66, 68, 69, 73, 74, 77, 80, 85, 88-90, 94, 97, 98, 102, 104-116, 118, 125] Visual Tracking Test 48 [10, 12, 19, 20, 22, 25, 29, 30, 32, 33, 36, 38, 40, 43-48, 56, 61, 63, 64, 67, 70, 74, 76, 77, 79, 83, 85, 91-93, 95-97, 99-101, 105, 108, 111, 112, 115, 117, 118, 123] Motion planning 32 [2,6,10,12,15,31,34,35,37,41,42,48,54,56,57,63,67,73,74,76,77,88,90,91,94,96,97,99,102,105,110,115] Collaboration planning 23 [10, 33-35, 38-41, 43, 47, 48, 58, 65, 66, 68, 69, 71, 83, 84, 104, 105, 114, 125] Collaborative heavy lifting 18 [1-4, 6, 12, 14, 18, 22, 28, 47, 55, 56, 71, 79, 84, 104, 125] Test control system [5,8,9,27,30,50,79,111,114] Performance assessment [45,51,…”

Section: Research Objectivesmentioning

confidence: 99%

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Human-Robot Trust Assessment From Physical Apprehension Signals

Hald

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Section: Test Resultssupporting

confidence: 74%

Section: Research Objectivesmentioning

confidence: 99%

Human-Robot Trust Assessment From Physical Apprehension Signals

Hald

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“…To maintain the attention of the participants to the task, the stamp was filled with water and they were asked to be careful not to pour it during the transportation of the object. Moreover, we asked the subjects to be focused on their own motion and not consider the robot moving in front of them in order to avoid motor contagion and make them maintain their natural speed (Vannucci et al, 2017 ). This choice was made to guarantee that the adaptation measured during the experiment was a result of the robot's ability to achieve synchronization and not a consequence of participants' tendency to adapt to the partner.…”

Section: Experimental Methodsmentioning

confidence: 99%

Human Motion Understanding for Selecting Action Timing in Collaborative Human-Robot Interaction

Vignolo

Sciutti

2019

Front. Robot. AI

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In the industry of the future, so as in healthcare and at home, robots will be a familiar presence. Since they will be working closely with human operators not always properly trained for human-machine interaction tasks, robots will need the ability of automatically adapting to changes in the task to be performed or to cope with variations in how the human partner completes the task. The goal of this work is to make a further step toward endowing robot with such capability. To this purpose, we focus on the identification of relevant time instants in an observed action, called dynamic instants , informative on the partner's movement timing, and marking instants where an action starts or ends, or changes to another action. The time instants are temporal locations where the motion can be ideally segmented, providing a set of primitives that can be used to build a temporal signature of the action and finally support the understanding of the dynamics and coordination in time. We validate our approach in two contexts, considering first a situation in which the human partner can perform multiple different activities, and then moving to settings where an action is already recognized and shows a certain degree of periodicity. In the two contexts we address different challenges. In the first one, working in batch on a dataset collecting videos of a variety of cooking activities, we investigate whether the action signature we compute could facilitate the understanding of which type of action is occurring in front of the observer, with tolerance to viewpoint changes. In the second context, we evaluate online on the robot iCub the capability of the action signature in providing hints to establish an actual temporal coordination during the interaction with human participants. In both cases, we show promising results that speak in favor of the potentiality of our approach.

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“…It also assists with station maintenance, such as shoveling snow on the premises. Example HRI studies similar to this robot research assistant scenario include Seo et al [159] and Vannucci et al [174].…”

Section: Taxonomy Of Social Errors In Hrimentioning

confidence: 99%

A Taxonomy of Social Errors in Human-Robot Interaction

Tian

OviattSharon

2021

J. Hum.-Robot Interact.

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Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user’s perception of a robot’s capability in achieving a task (defined as performance errors in this work) or degrade a user’s perception of a robot’s socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes.

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Adaptation to a humanoid robot in a collaborative joint task

Cited by 7 publications

References 12 publications

Human-Robot Trust Assessment From Physical Apprehension Signals

Human-Robot Trust Assessment From Physical Apprehension Signals

Human Motion Understanding for Selecting Action Timing in Collaborative Human-Robot Interaction

A Taxonomy of Social Errors in Human-Robot Interaction

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