Mmichael Licitra

dblp:06/2147 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 · 65% Reinforcement learning · 22% Motion planning and robot control · 6%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
action selection
0.112008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.112008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008
Knowledge, reasoning and agents › Multi-agent systems › multi-robot systems
multi-robot team
0.112008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008
Knowledge, reasoning and agents › Multi-agent systems › multi-robot systems
robot soccer
0.112008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008
Robotics › Robot navigation and mapping › mobile robot navigation
real-time navigation
0.012008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008
Robotics › Motion planning and robot control
robot control
0.012008
CMDragons: Dynamic passing and strategy on a champion robot soccer team · ICRA 2008

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

layered decision-making architecture · 0.1centralized perception · 0.1
YearPublicationVenuePosition
2008 CMDragons: Dynamic passing and strategy on a champion robot soccer team
abstract
After several years of developing multiple RoboCup small-size robot soccer teams, our CMDragons robot team achieved a highly successful level of performance, winning both the 2006 and 2007 competitions without losing a single game. Our small-size team consists of five executing wheeled robots with centralized, off-board perception and decision making. The decision making framework consists of a set of layered components, consisting of perception, evaluation and strategy, robot tactics and skills, and real-time navigation. In this paper, we present the strategy, action selection, and execution aspects of our architecture, with a focus on passing as an example of effective coordinated teamwork. The design enabled our robot team to score using multiple methods, from direct shooting up to 3D passes deflected in midair, resulting in a rich set of actions that were difficult for adversaries to counter. We provide several performance quantified claims supported by testing in our laboratory and in competition settings.
James Bruce, Stefan Zickler, Mmichael Licitra, Manuela M. Veloso
ICRA3