An intelligent machine is autonomous. An autonomous machine can operate successfully in a diversity of situations without resort to intervention by “higher level” processes, for example, humans. Physical machines are ultimately force or torque controlled dynamical systems: the specification of input torques, whether via syntactic prescriptions or feedback controllers, results in certain classes of vector fields. Control procedures whose resulting vector fields have globally attracting goal states may properly be said to evince autonomous behavior. In this light it makes sense to “program” robots using the language of dynamical systems via feedback. This talk will review various procedures developed within the Yale Robotics Lab that result in provably autonomous behavior according to the criterion developed above. These procedures are expressed in a rudimentary “geometric programming language” appropriate to the domain of tasks being undertaken and result in closed loop dynamical systems with global convergence properties (often, the strongest that the topology of the program can allow). A variety of simulation results and physical experimental studies will attest to the practicability of these methods.
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