Journal article Open Access
João Silvério
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise
Name | Size | |
---|---|---|
Silverio_IROS19_2019.pdf
md5:7762df9766d333922fc3b9d9f37040d1 |
5.8 MB | Download |
All versions | This version | |
---|---|---|
Views | 66 | 40 |
Downloads | 101 | 46 |
Data volume | 588.0 MB | 266.1 MB |
Unique views | 43 | 34 |
Unique downloads | 85 | 42 |