Navigating the social world relies upon the human capacity for mentalizing, or attributing intentions to social agents.Unfortunately, currently available commercial robots still lack such awareness of human intentionality. Building uponrecently-proposed Bayesian models of Theory of Mind (ToM), we propose a ToM model that can handle intention inferencein dynamic, fast-changing environments like a hospital, where staff have to attend to objectives and emergencies as theyarise. Our model infers and maintains a distribution over possible intentions, and uses the posterior predictives to forecastfuture trajectories, which is essential for robot motion planning. We show that our model performs excellently at inferringthe intentions and trajectories of human players controlling a nurse agent in a simulated environment. This work lays thefoundation for robots that can co-work with humans in dynamic, social environments with high-stake goals.

Primate visual development is characterized by low visual acuity and colour sensitivity besides high plasticity and synapticgrowth in the first year of infancy, prior to the development of specific visual-cognitive functions. In this work, weinvestigate the possible synergy between the gradual variation in visual input distribution and the concurrent growth of astatistical model of vision on the task of large-scale object classification. We adopt deep convolutional neural networks(CNNs) as a statistical model of vision and study its performance in 4 training setups each varying in either the modelbeing static or growing in parameters or the visual input being fully-formed or refining in saturation, contrast and spatialresolution. Our experiments indicate that a setup reflective of infant visual development, wherein a gradually growingmodel is trained on a refining visual input distribution, converges to a better generalization at a faster rate in comparisonto other setups.