Abstract
In this paper we present a method to structure contextual knowledge in spatial regions/manifolds that may be used in action selection for industrial robotic systems. The contextual knowledge is build on relatively few prior task executions and it may be derived from either teleoperation or previous action executions. We argue that our contextual representation is able to improve the execution speed of individual actions and demonstrate this on a specific time-consuming action of object detection and pose estimation. Our contextual knowledge representation is especially suited for industrial environments where repetitive tasks such as bin-and belt picking are plentiful. We present how we classify and detect the contextual information from prior task executions and demonstrate the performance gain on a real industrial pick-and-place problem.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
