Compact Real-time Avoidance on a Humanoid Robot for Human-robot Interaction

With robots leaving factories and entering less controlled domains, possibly sharing the space with humans, safety is paramount and multimodal awareness of the body surface and the surrounding environment is fundamental. Taking inspiration from peripersonal space representations in humans, we presen...

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Vydáno v:	2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI) s. 416 - 424
Hlavní autoři:	Nguyen, Dong Hai Phuong, Hoffmann, Matej, Roncone, Alessandro, Pattacini, Ugo, Metta, Giorgio
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	New York, NY, USA ACM 26.02.2018
Edice:	ACM Conferences
Témata:	Aerospace electronics Computer systems organization > Embedded and cyber-physical systems > Robotics Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic control Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems > Object detection Computing methodologies > Artificial intelligence > Computer vision > Computer vision tasks > Vision for robotics Computing methodologies > Artificial intelligence > Control methods Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Cognitive robotics deep learning for robotics human keypoints estimation Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Collaborative interaction Human-robot interaction Humanoid robots margin of safety peripersonal space physical human-robot interaction Pipelines Real-time systems Robot vision systems Theory of computation > Design and analysis of algorithms > Mathematical optimization > Continuous optimization > Nonconvex optimization Three-dimensional displays whole-body awareness margin of safety peripersonal space whole-body awareness deep learning for robotics humanoid robots human keypoints estimation physical human-robot interaction
ISBN:	9781450349536, 1450349536
ISSN:	2167-2148
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Popis
Shrnutí:	With robots leaving factories and entering less controlled domains, possibly sharing the space with humans, safety is paramount and multimodal awareness of the body surface and the surrounding environment is fundamental. Taking inspiration from peripersonal space representations in humans, we present a framework on a humanoid robot that dynamically maintains such a protective safety zone, composed of the following main components: (i) a human 2D keypoints estimation pipeline employing a deep learning based algorithm, extended here into 3D using disparity; (ii) a distributed peripersonal space representation around the robot»s body parts; (iii) a reaching controller that incorporates all obstacles entering the robot»s safety zone on the fly into the task. Pilot experiments demonstrate that an effective safety margin between the robot»s and the human»s body parts is kept. The proposed solution is flexible and versatile since the safety zone around individual robot and human body parts can be selectively modulated---here we demonstrate stronger avoidance of the human head compared to rest of the body. Our system works in real time and is self-contained, with no external sensory equipment and use of onboard cameras only.
ISBN:	9781450349536 1450349536
ISSN:	2167-2148
DOI:	10.1145/3171221.3171245