The application of robots to variable tasks in unstructured environments presents a series of problems that must be solved in order to achieve viable results Common teaching-type robots cannot be applied in these cases as the programming time and labor investment far exceed the time and cost of direct manual production. Numerically controlled (NC) robots programmed off-line by modified NC methods have been applied with economic success to program robots directly from computer-aided design (CAD) data where tasks are sufficiently repetitive and the operating environment is sufficiently structured Similarly, off-line programming systems have been developed by various robot manufacturers to generate instructions from CAD data for their robots. Likewise, developers of 3D simulation software have devised methods to merge CAD data with physical models of robots and system hardware to produce robot path programs that approximate the tasks to be performed. Each of these systems is unable to provide a totally automated means to program robot tasks directly from CAD data due to inaccuracies in the real-world elements and/or the models, and due to a lack of knowledge about the processes. A new approach to automatic robot programming is needed that is capable of dealing with:inherent differences between the CAD models and the real-world parts;uncertainties regarding the precise location and accessibility of the parts relative to the robot:process knowledge required to adapt these differences and uncertainties; andprocess knowledge essential to optimizing robot activities. Such an automatic robot programming system is being developed to meet the dual-use defense and commercial ship construction needs of American shipyards under the Technology Reinvestment Project (TRP) for Shipbuilding Robotics. This system automates the programmer's task of identifying location of welds, assigning weld process parameters and adaptive welding strategies to each joint. A procedural diagram for this system is shown in Figure 1. The results and benefits of this approach are described herein. Fig. 1Procedure for automatic off-line robot task planning