Robot learning from demonstration using predictive sequence learning
2011 (English)In: Robotic systems: applications, control and programming / [ed] Ashish Dutta, Kanpur, India: IN-TECH, 2011, 235-250 p.Chapter in book (Refereed)
In this chapter, the prediction algorithm Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL generates hypotheses from a sequence of sensory-motor events. Generated hypotheses can be used as a semi-reactive controller for robots. PSL has previously been used as a method for LFD, but suffered from combinatorial explosion when applied to data with many dimensions, such as high dimensional sensor and motor data. A new version of PSL, referred to as Fuzzy Predictive Sequence Learning (FPSL), is presented and evaluated in this chapter. FPSL is implemented as a Fuzzy Logic rule base and works on a continuous state space, in contrast to the discrete state space used in the original design of PSL. The evaluation of FPSL shows a significant performance improvement in comparison to the discrete version of the algorithm. Applied to an LFD task in a simulated apartment environment, the robot is able to learn to navigate to a specific location, starting from an unknown position in the apartment.
Place, publisher, year, edition, pages
Kanpur, India: IN-TECH, 2011. 235-250 p.
Computer Vision and Robotics (Autonomous Systems)
Research subject Computer and Information Science
IdentifiersURN: urn:nbn:se:umu:diva-50973ISBN: 978-953-307-941-7OAI: oai:DiVA.org:umu-50973DiVA: diva2:471682