Jak Kirman

A decision-theoretic approach to planning, perception, and control

The application of Bayesian decision theory as a framework for designing high-level robotic control systems is discussed. The approach to building planning and control systems integrates sensor fusion, prediction, and sequential decision making. The system explicitly uses the value of sensor information as well as the value of actions that facilitate further sensing. A stochastic decision model and a model for mobile-target localization used in the control system are described. A control system implemented to drive a small mobile robot equipped with eight sonar transducers with a maximum range of six meters and a visual processing system capable of identifying moving targets in its visual field and reporting their motion relative to the robot is also discussed.<<ETX>>

Deliberation scheduling for time-critical sequential decision making

We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates on the meta-level control problem of deliberalion scheduling, allocating computational resources to these routines. We provide different models corresponding to optimization problems that capture the different circumstances and computational strategies for decision making under time constraints. We consider precursor models in which all decision making is performed prior to execution and recurrent models in which decision making is performed in parallel with execution, accounting for the states observed during execution and anticipating future states. We describe algorithms for precursor and recurrent models and provide the results of our empirical investigations to date.

Sensor abstractions for control of navigation

An approach to building high-level control systems for robotics that is based on Bayesian decision theory is presented. The authors show how this approach provides a natural and modular way of integrating sensing and planning. They develop a simple solution for a particular problem as an illustration. They also examine the cost of using such a model and consider the areas in which abstraction can reduce this cost. The authors focus on the area of spatial abstraction. They discuss an abstraction that has been used to solve problems involving robot navigation and give a detailed account of the mapping from raw sensor data to the abstraction.<<ETX>>