Teaching a humanoid robot to draw 'Shapes'

The core cognitive ability to perceive and synthesize ‘shapes’ underlies all our basic interactions with the world, be it shaping one’s fingers to grasp a ball or shaping one’s body while imitating a dance. In this article, we describe our attempts to understand this multifaceted problem by creating a primitive shape perception/synthesis system for the baby humanoid iCub. We specifically deal with the scenario of iCub gradually learning to draw or scribble shapes of gradually increasing complexity, after observing a demonstration by a teacher, by using a series of self evaluations of its performance. Learning to imitate a demonstrated human movement (specifically, visually observed end-effector trajectories of a teacher) can be considered as a special case of the proposed computational machinery. This architecture is based on a loop of transformaElectronic supplementary material The online version of this article (doi:10.1007/s10514-011-9229-0) contains supplementary material, which is available to authorized users. V. Mohan ( ) · P. Morasso · J. Zenzeri · G. Metta · G. Sandini Robotics, Brain and Cognitive Sciences Department, Italian Institute of Technology, Genova, Italy e-mail: [email protected] P. Morasso e-mail: [email protected] J. Zenzeri e-mail: [email protected] G. Metta e-mail: [email protected] G. Sandini e-mail: [email protected] V.S. Chakravarthy Department of Biotechnology, Indian Institute of Technology, Chennai, India e-mail: [email protected] tions that express the embodiment of the mechanism but, at the same time, are characterized by scale invariance and motor equivalence. The following transformations are integrated in the loop: (a) Characterizing in a compact, abstract way the ‘shape’ of a demonstrated trajectory using a finite set of critical points, derived using catastrophe theory: Abstract Visual Program (AVP); (b) Transforming the AVP into a Concrete Motor Goal (CMG) in iCub’s egocentric space; (c) Learning to synthesize a continuous virtual trajectory similar to the demonstrated shape using the discrete set of critical points defined in CMG; (d) Using the virtual trajectory as an attractor for iCub’s internal body model, implemented by the Passive Motion Paradigm which includes a forward and an inverse motor model; (e) Forming an Abstract Motor Program (AMP) by deriving the ‘shape’ of the self generated movement (forward model output) using the same technique employed for creating the AVP; (f) Comparing the AVP and AMP in order to generate an internal performance score and hence closing the learning loop. The resulting computational framework further combines three crucial streams of learning: (1) motor babbling (self exploration), (2) imitative action learning (social interaction) and (3) mental simulation, to give rise to sensorimotor knowledge that is endowed with seamless compositionality, generalization capability and body-effectors/task independence. The robustness of the computational architecture is demonstrated by means of several experimental trials of gradually increasing complexity using a state of the art humanoid plat-