Anticipating the Start of User Interaction for Service Robot in the Wild

Ito, Koichiro; Kong, Qing; Horiguchi, Shota; Sumiyoshi, Takashi; Nagamatsu, Kenji

doi:10.1109/icra40945.2020.9196548

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…Develop mechanisms to help robots identify when people may require additional support and when the robot should offer help or take initiative. Torrey et al (2013), Ramachandran et al (2016), Baraglia et al (2017), Brscic et al (2017), Ito et al (2020 Purpose: Reward reciprocity, prevent decompensation and empower decentralized initiative.…”

Section: Preparing For Unexpected Robot Failures That Challenge the Ecosystemmentioning

confidence: 99%

“…,Rosenthal et al (2011),Rosenthal et al (2012),Wongphati et al (2012),Foster et al (2013), Fischer et al (2014,Glas et al (2015),Bajones et al (2016),Cha and Mataric (2016),Srinivasan and Takayama (2016),White et al (2020),Ito et al (2020) Develop mechanisms that enable robots to help repair one another and/or facilitate mutual learning and problem solving (viewing individual robots as part of a distributed team) Bererton and Khosla (2001),Bererton and Khosla (2002),Burke and Murphy (2007),Kutzer et al (2008),Davis et al (2016). …”

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confidence: 99%

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Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures

Honig

Oron-Gilad

2021

Front. Robot. AI

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Unexpected robot failures are inevitable. We propose to leverage socio-technical relations within the human-robot ecosystem to support adaptable strategies for handling unexpected failures. The Theory of Graceful Extensibility is used to understand how characteristics of the ecosystem can influence its ability to respond to unexpected events. By expanding our perspective from Human-Robot Interaction to the Human-Robot Ecosystem, adaptable failure-handling strategies are identified, alongside technical, social and organizational arrangements that are needed to support them. We argue that robotics and HRI communities should pursue more holistic approaches to failure-handling, recognizing the need to embrace the unexpected and consider socio-technical relations within the human robot ecosystem when designing failure-handling strategies.

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Section: Preparing For Unexpected Robot Failures That Challenge the Ecosystemmentioning

confidence: 99%

mentioning

confidence: 99%

Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures

Honig

Oron-Gilad

2021

Front. Robot. AI

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“…Recently, Ito et al . [108] proposed an end-to-end model using RGB images to predict the start of user interaction, in which the learning between oracle faces and facial keypoints is highlighted for improving the performance.…”

Section: Introductionmentioning

confidence: 99%

Non-verbal information estimation in multi-party human-robot/virtual human interaction

Zhang¹

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based on the target person's own and the interlocutor's body behavioral and facial information. Then, this network is expanded to form a context-aware and personality-based emotion recognition framework, i.e., first estimating the personality and then recognizing the emotions based on the estimated personality. A set of experiments have been conducted showing that the proposed method has good performance in both personality and emotion experiments.

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