Skill learning and inference framework

Authors:
Sang Hyoung Lee;Il Hong Suh
Affiliations:
Education Center for Network-Based Intelligent Robotics, Hanyang University, Seoul, Korea;Department of Computer Science and Engineering, Hanyang University, Seoul, Korea
Venue:
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
Year:
2013

Citing 4
Cited 0

To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Combining dynamical systems control and programmingby demonstration for teaching discrete bimanual coordination tasks to a humanoid robot

Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

International Journal of Robotics Research
Online segmentation and clustering from continuous observation of whole body motions

IEEE Transactions on Robotics

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Abstract

We propose a skill learning and inference framework, which includes five processing modules as follows: 1) human demonstration process, 2) autonomous segmentation process, 3) process of learning dynamic movement primitives, 4) process of learning Bayesian networks, 5) process of constructing motivation graph and inferring skills. Based on the framework, the robot learns and infers situation-adequate and goal-oriented skills to cope with uncertainties and human perturbations. To validate the framework, we present the experimental results when using a robot arm that performs a daily-life task.