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Mark D. J. Brown

dblp:73/3692 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—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 · 62% Motion planning and robot control · 38%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
robot programming
0.012001
Automated Programming of an Industrial Robot through Teach-By Showing · ICRA 2001
Robotics › Motion planning and robot control
robot control
0.012001
Automated Programming of an Industrial Robot through Teach-By Showing · ICRA 2001
Robotics › Motion planning and robot control › robot control
sensor-based control
0.012001
Automated Programming of an Industrial Robot through Teach-By Showing · ICRA 2001

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

procedural program generation · 0.0demonstration learning · 0.0
YearPublicationVenuePosition
2001 Automated Programming of an Industrial Robot through Teach-By Showing
abstract
Intelligent Automation, Inc. (IAI) presents a system that learns from demonstrations of a human operator and then produces a sensor-driven program that can control a robot without operator intervention. The system generates a procedural program, identical in function to a procedural program developed in a conventional robot programming language, that can be easily re-used in similar but different application tasks. In essence, based on the skills demonstrated by a human teacher, a robot can be automatically programmed to perform complex assembly tasks. Robot users with no programming skills will be able to "bootstrap" themselves to a semi-autonomous operation after a small number of taught examples. In addition, as the task requirements change, the robot can be re-programmed as easily as it was initially programmed. Unlike the conventional program development process, the operator need not postulate about the value of sensor feedback variables required at a particular level of the controller. In fact, the operator need not be familiar with the syntax and semantics of the programming language at all.
Donald R. Myers, Michael J. Pritchard, Mark D. J. Brown
ICRA3