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With robots leaving factories and entering less controlled domains, possiblysharing the space with humans, safety is paramount and multimodal awareness ofthe body surface and the surrounding environment is fundamental. Takinginspiration from peripersonal space representations in humans, we present aframework on a humanoid robot that dynamically maintains such a protectivesafety zone, composed of the following main components: (i) a human 2Dkeypoints estimation pipeline employing a deep learning based algorithm,extended here into 3D using disparity; (ii) a distributed peripersonal spacerepresentation around the robot's body parts; (iii) a reaching controller thatincorporates all obstacles entering the robot's safety zone on the fly into thetask. Pilot experiments demonstrate that an effective safety margin between therobot's and the human's body parts is kept. The proposed solution is flexibleand versatile since the safety zone around individual robot and human bodyparts can be selectively modulated---here we demonstrate stronger avoidance ofthe human head compared to rest of the body. Our system works in real time andis self-contained, with no external sensory equipment and use of onboardcameras only.

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