A Bayesian Robot That Distinguishes "Self" from "Other"

A Bayesian Robot That Distinguishes “Self” from “Other” Kevin Gold (kevin.gold@yale.edu) and Brian Scassellati (scaz@cs.yale.edu) Department of Computer Science, Yale University New Haven, CT 06511 USA Abstract A Bayesian kinesthetic-visual matching model allows a humanoid robot to perform mirror self-recognition with- out social understanding. The robot learns the rela- tionship between its own motor activity and perceived motion by observing the movements of its arm for four minutes. Over this time, it builds a simple dynamic Bayesian model that relates these two events, and uses this self model to build a model of “animate others” that is a copy of its self model but with the motor state hidden. Presented with a mirror, the robot then judges its mirror image to match its “self” model, while people are judged to be “animate others. ” Keywords: Self-recognition; robot; Bayesian; animacy; contingency. mirror test; Introduction Figure 1: Nico is an upper-torso humanoid robot with the arm and head kinematics of a one-year-old. There are two kinds of theories that seek to explain how 2-year-old human infants, higher primates, and other in- telligent animals discover that their mirror images are their own reflections. The first kind of theory proposes that mirror self-recognition requires social understand- ing, as the learner must have some idea of how it ap- pears to others (G. Gallup, 1982). The second kind of theory proposes that mirror self-recognition comes from matching kinesthetic experience to visual feedback, and that therefore no social knowledge is necessary for self- recognition (Mitchell, 1997). The present study is meant to add some concreteness to the latter hypothesis of kinesthetic-visual matching, and show that it is com- putationally feasible and robust to identify one’s mirror image using only a Bayesian self-model that relates mo- tor activity to motion. The “mirror test” of G. G. Gallup (1970) is typical of tests for mirror self-recognition. In the original test, a chimpanzee was given ten days to acclimate itself to a mirror in its cage. A spot of red dye was then applied to a chimpanzee’s eyebrow and ear while it was uncon- scious. On seeing its reflection in the mirror, the chim- panzee would use its mirror reflection to reach for the marks on its own head. Similar tests have found mir- ror self-recognition in humans at 2 years (Amsterdam, 1972), orangutans (Suarez & Gallup, 1981), elephants (Plotnik, Waal, & Reiss, 2006), and dolphins (Reiss & Marino, 2001), while macaques, gorillas, and lesser pri- mates appear to be unable to self-recognize (G. Gallup, The ability to recognize oneself in the mirror appears in human infants within a few months of some other criti- cal social abilities, such the development of sympathy at others’ distress (Zahn-Waxler, Radke-Yarrow, Wagner, & Chapman, 1992). The emergence of this ability at about the same time in human infants has been inter- preted by some to be more than a coincidence, suggest- ing that “theory of mind” skills are necessary for mirror self-recognition (G. Gallup, 1982; Plotnik et al., 2006). However, it is difficult to determine from phylogenetic and developmental observations alone whether this link is more than coincidental. The present work demonstrates that it is possible to perform robust self-recognition on the basis of match- ing kinesthetic experience to visual motion alone. The method consists of the robot comparing three models for each object in its visual field. The first model is that of random noise, generated with no structure over time. The second model consists of an observed inter- nal state of motor activity that generates the external feedback of motion; thus, the consistency of the match between motor activity and motion dictates the likeli- hood of this model. The third model is that of motion generated by somebody else; it is identical to its own self- motion model, only the motor state is hidden and must be reasoned about probabilistically. The likelihood of each model is updated with every new observation, such that the robot always has a best guess as to whether