Name
Title | ||
|---|---|---|
Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution. |
Abstract | ||
|---|---|---|
In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we present a novel interactive framework, where a collaborative robot learns skills for trajectory based tasks from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated skill using Hidden Markov Model (HMM). Our experimental results show that the learned model can be used to produce a generalized trajectory based skill. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Robotics | Robot learning,Control engineering,Artificial intelligence,Engineering,Robot,Hidden Markov model,Robotics,Trajectory,State distribution |
DocType | Volume | Citations |
Journal | abs/1809.10797 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sulabh Kumra | 1 | 0 | 2.37 |
| Ferat Sahin | 2 | 706 | 45.49 |