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Todd Layton

dblp:135/8563 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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
Multi-agent systems · 46% Motion planning and robot control · 30% Robot manipulation · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
assembly
0.212013
IkeaBot: An autonomous multi-robot coordinated furniture assembly system · ICRA 2013
Knowledge, reasoning and agents › Multi-agent systems › multi-robot coordination
heterogeneous robot team coordination
0.212013
IkeaBot: An autonomous multi-robot coordinated furniture assembly system · ICRA 2013
Robotics › Motion planning and robot control › assembly planning
multi-robot assembly planning
0.212013
IkeaBot: An autonomous multi-robot coordinated furniture assembly system · ICRA 2013
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.212013
IkeaBot: An autonomous multi-robot coordinated furniture assembly system · ICRA 2013
Robotics › Motion planning and robot control
assembly planning
0.012013
IkeaBot: An autonomous multi-robot coordinated furniture assembly system · ICRA 2013

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

symbolic planning · 0.2geometric reasoning · 0.2
YearPublicationVenuePosition
2013 IkeaBot: An autonomous multi-robot coordinated furniture assembly system
abstract
We present an automated assembly system that directs the actions of a team of heterogeneous robots in the completion of an assembly task. From an initial user-supplied geometric specification, the system applies reasoning about the geometry of individual parts in order to deduce how they fit together. The task is then automatically transformed to a symbolic description of the assembly-a sort of blueprint. A symbolic planner generates an assembly sequence that can be executed by a team of collaborating robots. Each robot fulfills one of two roles: parts delivery or parts assembly. The latter are equipped with specialized tools to aid in the assembly process. Additionally, the robots engage in coordinated co-manipulation of large, heavy assemblies. We provide details of an example furniture kit assembled by the system.
Ross A. Knepper, Todd Layton, John Romanishin, Daniela Rus
ICRA2