Qualitative spatial reasoning foractual robots in real-world environments mustnecessarily involve perceptive knowledge, sincecomplete a-priori information of the outerworld can never be assumed, not even at aqualitative level. In this paper a contributionis made towards the integration of quantitativedata – namely, sensor signals – into a higherlevel qualitative plan. This integrationincludes the use of neural networks to learnhow to map complex perceptual signals into aqualitative description. Sensors are used toacquire actual knowledge of the environment andproperly identify, with the help of aconnectionist system, the real state of thesystem. The approach is presented in theframework of robotic tasks involving contactfor which the most informative perception comesfrom force/torque sensors. Empirical simulationresults are provided for the chamferlesstwo-dimensional peg-in-hole insertion modelwith friction. The advantages of learningapproaches over geometric model-basedtechniques are discussed: our approach issimple but robust against unpredictable changesof task parameters, and it exhibits agracefully degrading behavior and on-lineadaptation to new task conditions. Anenhancement to incorporate a measure ofconfidence of the network is also presented.
Spatial Cognition and Computation – Springer Journals
Published: Oct 16, 2004
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