Learning and adaptivity will play a large role in robotics in the future. Two questions are open: (1) in principle, how much it is possible to learn; and (2) in practice, how much should an agent be able to learn. The bootstrapping scenario describes the extreme case in which agents need to learn "everything" from scratch, including a torque-to-pixels model for its robotic body. This paper considers the bootstrapping problem for a subset of the set of all robots. The Simple Vehicles are an idealization of mobile robots equipped with a set of "canonical" exteroceptive sensors: the camera, the range finder and the field sampler. The sensorimotor dynamics of these sensors are derived and shown to be surprising similar. These sensorimotor dynamics are well approximated by a class of nonlinear systems that assume an instantaneous bilinear relation among observations, commands, and changes in the observations. The bilinear approximation is sufficient to guarantee success in the task of generalized "servoing": driving the observations to a given goal snapshot. Simulations and experiments substantiate the theoretical results. This is the first instance of a bootstrapping agent that can learn the model of the dynamics of a relatively large universe of systems and use the models to solve well-defined tasks, with no parameter tuning or hand-designed features.
from robot theory http://ift.tt/1EgNyjj
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