From object-action to property-action: Learning causally dominant properties through cumulative explorative interactions

Emerging studies from neuroscience in relation to organization of sematic memory in the brain provide converging evidence suggesting that conceptual knowledge is organized in a distributed fashion in "property specific" cortical networks that directly support perception and action (and were active during learning). Though learning 'object-action' affordances and using such knowledge for prediction and planning is an active topic in cognitive robotics, this article urges to go beyond and look at "property-action" networks instead. To this effect, a brain guided framework for distributed property specific organization of sensorimotor knowledge for humanoid iCub is presented. Two simple learning rules namely 'elimination' and 'growth' are proposed to compare top down anticipation and bottom up real experience to abstract underlying causal relations. An engaging scenario how the robot cumulatively learns and abstracts causally dominant properties that influence motion of various objects when forces are exerted on them is used to validate the neural architecture. The implicit advantage is that such learnt "property-action" relations can be effortlessly generalized to a domain of objects for which the robot need not have any past experience/learning but nevertheless share the "property". Further, the study has relevance in both better understanding how common causal relations can be cumulatively learnt, represented and exploited, to providing novel embodied frameworks for analogical reasoning.