Human Behavior Understanding for Robotics

Salah, Albert Ali; Ruiz‐del‐Solar, Javier; Meriçli, Çetin; Oudeyer, Pierre-Yves

doi:10.1007/978-3-642-34014-7_1

Cited by 19 publications

(14 citation statements)

References 66 publications

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“…Although simple, the perception of the field worker regarding the service units is an important factor in the success of the flexible automation process since, as in every working environment, social rules and harmony should apply in the benefit of the agricultural production [24].…”

Section: B Field Workers' Perceptionmentioning

confidence: 99%

Human-robot interaction in precision agriculture: Sharing the workspace with service units

Cheein

Herrera

Giménez

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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Currently, Chile and Argentina experience serious challenges that affect their agricultural productivity. For example, in Chile, the loss of farmable field due to recent earthquakes, and volcanic eruptions as well as the loss of water reserves due to climate changes are affecting the agriculture. Additionally, both countries are facing a same problem: the loss of human labor force. Field workers are migrating from the farm to other fields in the industry which offer them more stable and more profitable jobs (like the mining industry in Chile, or car assembling lines in Argentina). In this adverse scenario, it becomes necessary to introduce and develop agricultural automation and sensing technologies for both primary (harvesting, seeding, fertilizing, spraying) and secondary tasks (grove supervision, weed detection, hauling, mowing). However, fully robotized farms are not yet a possibility since the transition from human labor force dependent farming to autonomous farming needs to be smooth and requires legal regulation not yet in discussion. In this paper, we summarize the state of the art in human-robot interaction in farmable fields, with emphasis in the current constrains associated with flexible automatization of farms in Argentina and Chile. In particular, we introduce the guidelines for designing a humanrobot interaction strategy for harvesting tasks, that could be used for other agricultural tasks.

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Section: B Field Workers' Perceptionmentioning

confidence: 99%

Human-robot interaction in precision agriculture: Sharing the workspace with service units

Cheein

Herrera

Giménez

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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“…Recent work in computer analysis of human behavior focuses on complex and contextualized social interactions, as opposed to scenarios of a single person performing a single activity [5], [6]. One of the basic requirements of social interaction is the establishment of joint attention between the interacting parties.…”

Section: Contribution To the Special Issuementioning

confidence: 99%

Guest Editorial Behavior Understanding and Developmental Robotics

Salah

Oudeyer²,

Meriçli³

et al. 2014

IEEE Trans. Auton. Mental Dev.

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Abstract-The scientific, technological, and application challenges that arise from the mutual interaction of developmental robotics and computational human behavior understanding give rise to two different perspectives. Robots need to be capable to learn dynamically and incrementally how to interpret, and thus understand multimodal human behavior, which means behavior analysis can be performed for developmental robotics. On the other hand, behavior analysis can also be performed through developmental robotics, since developmental social robots can offer stimulating opportunities for improving scientific understanding of human behavior, and especially to allow a deeper analysis of the semantics and structure of human behavior. The contributions to the Special Issue explore these two perspectives.Index Terms-Activity recognition, affective computing, attention, developmental learning, human behavior understanding, learning by demonstration, nonverbal communication. I. THE SCOPE OF THIS SPECIAL ISSUEI N ORDER to act in a useful, relevant, and socially acceptable manner, robots will need to understand the behavior of humans at various levels of abstractions, at various time scales, and in the particular context of human-robot interactions. Robots need to be capable to learn dynamically and incrementally how to interpret, and thus understand multimodal human behavior. This includes for example learning the meaning of new linguistic constructs used by a human, learning to interpret the emotional state of particular users from paralinguistic or nonverbal behavior, characterizing properties of the interaction or learning to guess the intention, and potentially the structure of goals of a human based on its overt behavior Furthermore, robots need in particular to be capable of learning new tasks through interaction with humans, for example using imitation learning or learning by demonstration. This heavily involves the capacity for learning how to decode teaching behavior, including linguistic and nonlinguistic cues, feedback and guidance provided by humans, as well as inferring reusable primitives in human behavior.While some of the existing techniques of multimodal behavior analysis and modeling can be readily reused for robots, novel scientific and technological challenges arise when one aims to achieve human behavior understanding in the context of natural and life-long human-robot interaction. The first purpose of this Special Issue is to explore these challenges.Our second purpose is to understand how behavior analysis can be achieved through developmental robotics. DevelopDigital Object Identifier 10.1109/TAMD.2014.2328731 mental social robots can offer stimulating opportunities for improving scientific understanding of human behavior, and especially to allow a deeper analysis of the semantics and structure of human behavior. Humans tend to interpret the meaning and the structure of other's behaviors in terms of their own action repertoire, which acts as a strong helping prior for this complex inference problem. S...

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“…Synchrony has been widely used in analyzing non-verbal human actions [20]. Prabhakar et al [18] prompted the temporal causality model for deducting visual events.…”

Section: Introductionmentioning

confidence: 99%

Automated analysis of interactional synchrony using robust facial tracking and expression recognition

Zhang

et al. 2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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In this paper, we propose an automated, datadriven and unobtrusive framework to analyze interactional synchrony. We use this information to determine whether interpersonal synchrony can be an indicator of deceit. Our framework includes a robust facial tracking module, an effective expression recognition method, synchrony feature extraction and feature selection methods. These synchrony features are used to learn classification models for the deception recognition. To evaluate our proposed framework, we have conducted extensive experiments on a database of 242 video samples. We validate the performance of each technical module in our framework, and also show that these synchrony features are very effective at detecting deception.

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Human Behavior Understanding for Robotics

Cited by 19 publications

References 66 publications

Human-robot interaction in precision agriculture: Sharing the workspace with service units

Human-robot interaction in precision agriculture: Sharing the workspace with service units

Guest Editorial Behavior Understanding and Developmental Robotics

Automated analysis of interactional synchrony using robust facial tracking and expression recognition

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