Laboratory for Perceptual Robotics
Research Overview

A Basis for Robot Control, Learning, and Skill Acquisition

This research explores the potential for sensory and motor behavior to be expressed as sequences of control drawn from a control basis . Our approach is related to a body of work aimed toward exploiting `local' and multiple-model approaches to coping with strongly nonlinear and time-varying systems. The control basis structures behavior and provides a formal basis for scheduling resources in adaptive systems. Control actions consist of combinations of closed-loop control primitives with specific resource designations. By enumerating controllers that engage a variety of resources, we support sensorimotor policies that are robust over a wide range of contexts. Moreover, this framework brings traditions in real-time programming and operations research to bear on adaptive electromechanical systems. Solutions to sensorimotor problems are sequences of concurrent control processes designed to traverse a finite set of system equilibria. End-to-end behavior can thus be modeled as a discrete event system, permitting the application of powerful formal techniques to further structure the behavior of the system. Reversible constraints on control actions are used to define legal composite behavior from which a programmer may write behavioral programs. Techniques for automatically programming behavior may also exploit these forms of structure.

To accomplish our goals, we have adopted tools for exploiting redundancy in systems with excess degrees of freedom. Foremost among these is a generalization of null space control techniques developed to address subordinate control objectives in the context of holonomic manipulator systems with excess DOF. We have applied them to multi-robot, nonholonomic systems with sets of contiguous goal states. Individual robots do not present excess degrees of freedom, but teams of robots with kinematic constraints are generally redundant and can accomodate tasks with multiple objectives. We also advocate a interaction-based state estimator in which a time history of closed-loop responses serve to form critical control categories. Furthermore, we are experimenting with a developmental programming hypothesis that holds that critical control knowledge is compiled incrementally and re-used in a sequence of increasingly complex tasks.