According to recent theories of sensorimotor development in biological systems, the dynamics of physical interaction with the world encodes control knowledge. Control is derived by reinforcing and learning to predict constructive patterns of interaction, and behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. For grasping and manipulation, we propose a closed-loop control process that is parametric in the number and identity of contact resources. In this paper, we show how control decisions can be made by estimating patterns of membership in a family of prototypical dynamic models. A grasp controller can thus be tuned continuously online to optimize performance over a variety of object geometries. This same process can be used to estimate the haptic category in which the object resides. We illustrate how a grasping policy that is incrementally optimal for several objects can be acquired using our Salisbury hand with tactile sensor feedback.
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