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We propose a framework where the human-robot interaction is modeled as a multimodal dialogue which is regulated by an attentional system that guides the robot towards the execution of structured tasks. Specifically, we propose an approach where the dialogue between the human and the robot is represented as a Partially Obervable Markov Decision Process (POMDP), while the associated dialogue policy...
We present an attentional control architecture for a robotic agent capable of adapting its deliberative and executive behavior to internal and external processes. In this framework, the agent is endowed with simple attentional mechanisms regulating the sensors sampling rates and action activations. The process of changing the frequency of sensors readings is interpreted as an increase or decrease...
The main goal of our current research is the design of a behavior-based robotic architecture that has the capability of adapting its behaviors to the rate of change of both the environment and its internal states reducing the computational costs of input processing. Inspired by research on biological clocks, we introduced a simple schema theory model where releasing mechanisms are combined with adaptive...
Influenced by the results obtained in neuroscience and biology, we have introduced a model (AIRM) that, inspired by biological rhythms, adaptively controls a behavior based robotic system (BBRS). The proposed model is implemented by means of an NSP (neuro symbolic processor). Since the NSP can be implemented on FPGA, we can take advantage of a parallel execution of the AIRM model and then an improvement...
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