Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention

Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. We have designed and implemented a system for dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task. During the observation of a partners reaching movement, the robot is able to contextually estimate the goal position of the partner hand and the location in space of the candidate targets, while moving its gaze around with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control provides a relevant advantage with respect to typical passive observation, both in term of estimation precision and of time required for action recognition. © 2012 Springer-Verlag

Learning to Look in Different Environments: An Active-Vision Model which Learns and Readapts Visual Routines

One of the main claims of the active vision framework is that nding data on the basis of task requirements is more ecient than reconstructing the whole scene by performing a complete visual scan. To be successful, this approach requires that agents learn visual routines to direct overt attention to locations with the information needed to accomplish the task. In ecological conditions, learning such visual routines is dicult due to the partial observability of the world, the changes in the environment, and the fact that learning signals might be indirect. This paper uses a reinforcement-learning actor-critic model to study how visual routines can be formed, and then adapted when the environment changes, in a system endowed with a controllable gaze and reaching capabilities. The tests of the model show that: (a) the autonomouslydeveloped visual routines are strongly dependent on the task and the statistical properties of the environment; (b) when the statistics of the environment change, the performance of the system remains rather stable thanks to the re-use of previously discovered visual routines while the visual exploration policy remains for long time sub-optimal. We conclude that the model has a robust behaviour but the acquisition of an optimal visual exploration policy is particularly hard given its complex dependence on statistical properties of the environment, showing another of the diculties that adaptive active vision agents must face