Action and Language Learning in Robots Sample Clauses

Action and Language Learning in Robots. Recent models from cognitive robotics research have addressed some of the issues described above, and contributed to the identification of the open research challenges in language and action research. Before we discuss in detail the key challenges, we review a few of the most interesting contributions. Xxx Xxx (2005; Xxx et al. 2004) propose the use of conversational robots able to translate complex spoken commands such us “hand me the blue one on your right” into situated actions. These robots are provided with a control architecture that includes a three-dimensional model of the environment (which is updated by the robot on the basis of linguistic, visual, or haptic input) and sensory-motor control programs. This model is consistent with the notion of schemas proposed by Xxxxxx (1954), in which the meaning of words is associated with both perceptual features and motor program. For example, the word ‘red’ is grounded in the motor program for directing active gaze towards red objects. Similarly, the word ‘heavy’ is grounded in haptic expectations associated with lifting actions. Objects are represented as bundles of properties tied to a particular location along with encodings of motor affordances for affecting the future location of the bundle.
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