Predictive Robot Programming: Theoretical and Experimental Analysis
By Kevin R. Dixon, John M. Dolan, Pradeep K. Khosla. 2004
18 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Dana Kulic
- Wataru Takano
- Yoshihiko Nakamura
- Cornelius Weber
- Vincenzo Lomonaco
In Control and Systems Engineering
- German Ignacio Parisi
- Luiza Mici
- Stefan Wermter
- Dana Kulic
- Yoshihiko Nakamura
- Wataru Takano
- Aude Gemma Billard
- Seyed Mohammad Khansari-Zadeh
Citations in the corpus
- An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “A number of other works have also adapted HMMs to the problem of incremental learning of human motion [31]–[34].”
- Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains (2008, background): “Dixon et al. (2004) describe a system for partially automating the programming of an industrial robot manipulator by facilitating the definition of trajectory waypoints.”