A fundamental source of information that we exploit in social interactions comes from the way we move. Our movements are characterized by certain regularities that make the motion of living beings – or biological motion – unique, both in terms of how it is performed and how it is perceived. Humans are particularly good at perceiving biological movements and at sensing their properties, and even newborns exhibit the ability to recognize biological motion in the scene. With the aim of deepening the understanding of the complex mechanisms at the basis of human motion, we started to implement a form of visual processing that allowed iCub to detect biological motion. The developed model is based on low-level visual motion features like average speed and curvature computed from the optic flow of the scene. These geometrical features are inspired by the two-third power law, a motor control law proper of biological motion. The biological motion detection algorithm proved to be successful in face of the heterogeneity of different actions to be judged.
The mechanism supporting this ability scales well also to more complex tasks, as coordination in simple rhythmic exercise or simple action understanding – suggesting that it could represent a reasonable building block for more complex social skills.
Here you can find our dataset about actions understanding across views:
- Vignolo, A., Rea, F., Noceti, N., Sciutti, A., Odone, F., & Sandini, G. (2016, November). Biological movement detector enhances the attentive skills of humanoid robot iCub. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) (pp. 338-344). IEEE.
- Vignolo, A., Noceti, N., Rea, F., Sciutti, A., Odone, F., & Sandini, G. (2017). Detecting biological motion for human–robot interaction: A link between perception and action. Frontiers in Robotics and AI, 4, 14.