Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Bert Larson

dblp:85/2875 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 67% Robot navigation and mapping · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
industrial robot		 0.012003
An auto-teach/re-teach implementation of industrial robots for bio-product manufacturing automation · ICRA 2003
Robotics › Robot manipulation › robot programming
robot teaching		 0.012003
An auto-teach/re-teach implementation of industrial robots for bio-product manufacturing automation · ICRA 2003
Robotics › Robot navigation and mapping › visual navigation
visual teach and repeat		 0.012003
An auto-teach/re-teach implementation of industrial robots for bio-product manufacturing automation · ICRA 2003

Methods — techniques the papers use, named apart from their topics

air pressure sensing · 0.0
YearPublicationVenuePosition
2003 An auto-teach/re-teach implementation of industrial robots for bio-product manufacturing automation
abstract
This paper describes an air pressure sensor based auto-teach/re-teach methodology for industrial multi-link robots. This kind of auto-teach/re-teach function can be easily applied to bio-product manufacturing automation to reduce human-robot interactions. Section 1 introduces the background and objective of auto-teach/re-teach in industrial robotics applications. Section 2 describes the hardware and software of this approach. Section 3 describes the algorithms used in this application. Section 4 shows the user interface and testing result. Section 5 summarizes this approach and indicates the areas for further improvements.
WeiMin Tao, Bert Larson, Clay Kim
 ICRA2