Empirical evaluation of information for robotic manipulation tasks
Creators: name: Mulligan, Jane
Publisher: name: University of British Columbia
Year: 2009

description: Rigorous analysis and evaluation of real implemented robotic systems for intelligent tasks are rarely performed. Such systems are often extremely complicated, depending not only on 'interesting' theoretical parameters and models, but on many assumptions and constants which may be set almost arbitrarily. The information required to achieve good task performance includes all of these acknowledged and unacknowledged parameters. In order to evaluate and compare systems which employ diverse components and methods, we need a framework and criteria by which to measure them. We demonstrate techniques for evaluating a system's performance as a function of the parameters and information it uses and compare different task implementations on this basis. We view all task implementations as particular parameterizations of the task goals they represent. Some parameters belong to fixed, pre-measured models; others are data collected or measured online by the system. Comparing systems then becomes the comparison of these parameterizations. There are three key questions we need to ask when determining task parameterizations: What do we need to measure (observability)? How well must we discriminate between measured values (precision)? and How accurately must our measurements reflect the true world state (accuracy)? We present a performance based framework for empirical evaluation and comparison of task information based on these three basic notions of observability, precision (discrimination) and accuracy. Factorial experiments give us a starting point for determining which measurements, and their interactions, define the task subspace. Sensitivity analysis determines optimal parameter values and the effect of uncertainty and variations i n measurements on task performance. Finally a cost/performance metric offers a quantification of the task complexity with respect to the parameters, and performance based precision and accuracy requirements determined in the previous steps. The experiments presented to demonstrate this methodology are based on a part orienting task implemented in two very different ways. One system uses a 'sensorless' model-based push-orienting method, the other uses a real-time stereo vision system to localize objects in the workspace for sensor-driven part orienting. The parameters used to represent manipulated parts for sensing versus model-based manipulation are similar, though not identical sets, and encode the information critical to the task. Through detailed experiments we establish the statistically significant parameters and parameter interactions for each system, and apply sensitivity analysis to set optimal parameter values and explore the nature of interactions. Finally, the cost/performance metric gives us a means of counting the computation and sensing costs to achieve the observed system error rates. This type of analysis is a necessary step to understanding integrated intelligent systems. It reveals aspects of system implementations which cannot easily be predicted in advance, and gives a clear picture of the information required and the strengths and weaknesses of the system.