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 automation 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 predictions 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 tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25% of the waypoints with a median prediction error less than 0:5% of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robot programming tasks over 30% faster when using the PRP system to compute predictions of future positions.