Autonomous Observation

We address the problem of observing an agent We advocate a modeling approach for the visual system and its observer where a discrete event dynamic system DEDS framework is developed and events are de ned as ranges on parameter subsets The dynamic recursive context for nite state machines DRFSM is described with some applications in the inspection and reverse engineering domain We propose a system for observing a manipulation process where a robot hand manipulates an object We recognize the hand object interaction over time and a stabilizing observer is constructed Low level modules are developed for recognizing the events that causes state transitions within the dynamic manipulation system The work examines closely the possibilities for errors mistakes and uncertainties in the manipulation system observer construction process and event identi cation mechanisms The DRFSM DEDS systems utilizes di erent tracking techniques in order to observe and recognize tasks and agents in an active adaptive and goal directed manner This work was supported in part by DARPA grant N J and NSF grant CDA Air Force AFOSR Grants Army DAAL Grant C PRI NSF Grants CISE CDA IRI DARPA Grant N and DuPont Corporation All opinions ndings conclusions or recommendations expressed in this document are those of the author and do not necessarily re ect the views of the sponsoring agencies