Name
Abstract | ||
|---|---|---|
One of the main barriers to automating a particular task with a robot is the amount of time needed to program the robot. Decreasing the programming time would facilitate automation in domains previously off limits. In this paper, we present a novel method for leveraging the previous work of a user to decrease future programming time: predictive robot programming. The decrease in programming time is accomplished by predicting waypoints in future robot programs and automatically moving the manipulator end-effector to the predicted position. To this end, we develop algorithms that construct simple continuous-density hidden Markov models by a state-merging algorithm based on waypoints from prior robot programs. We then use these models to predict the waypoints in future robot programs. While the focus of this paper is the application of predictive robot programming, we also give an overview of the underlying algorithms used and present experimental results. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/IRDS.2002.1041501 | Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference |
Keywords | Field | DocType |
directed graphs,hidden Markov models,learning (artificial intelligence),manipulator dynamics,position control,robot programming,directed acyclic graph,hidden Markov models,learning algorithm,manipulators,position control,predictive programming,robot programming,state merging algorithm | Robot learning,Functional reactive programming,Computer science,Inductive programming,Directed graph,Automation,Reactive programming,Artificial intelligence,Robot,Hidden Markov model,Machine learning | Conference |
Volume | Citations | PageRank |
1 | 9 | 0.94 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin R. Dixon | 1 | 65 | 9.43 |
| Martin Strand | 2 | 9 | 0.94 |
| Khosla, P.K. | 3 | 931 | 123.84 |