Published In Predictive Robot Programming: Theoretical and Experimental Analysis

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automa-tion due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes pre-dictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from task