Ricerc@Sapienza

Robots assisting humans with some specific tasks have been demonstrated on several occasions. A further challenging idea is to anticipate human needs by mining the future demand from the next action prediction. To trigger this anticipation mechanism a robot has to recognize what the human is doing now, foresee what the human will do next, and from their connection guesstimating what to do to help. We propose here a deep network combining the essential components of this challenging process leading to foreseeing the help that can be provided in human-robot collaboration.

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate.