  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.
