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Nimbus Goehausen

dblp:39/8370 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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
Motion planning and robot control · 54% Legged, aerial and field robots · 46%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots		 0.112010
Parameterized maneuver learning for autonomous helicopter flight · ICRA 2010
Robotics › Legged, aerial and field robots › aerial robot control
autonomous helicopter flight		 0.112010
Parameterized maneuver learning for autonomous helicopter flight · ICRA 2010
Robotics › Motion planning and robot control › robot control › learning control
maneuver learning		 0.112010
Parameterized maneuver learning for autonomous helicopter flight · ICRA 2010
Robotics › Motion planning and robot control
robot learning		 0.112010
Parameterized maneuver learning for autonomous helicopter flight · ICRA 2010
Robotics › Motion planning and robot control
trajectory planning		 0.012010
Parameterized maneuver learning for autonomous helicopter flight · ICRA 2010

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

parameterized maneuver learning · 0.1learning from demonstration · 0.1
YearPublicationVenuePosition
2010 Parameterized maneuver learning for autonomous helicopter flight
abstract
Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system's dynamics can be very time-consuming and often exceedingly difficult. We present an algorithm for automatically generating large classes of trajectories for difficult control tasks by learning parameterized versions of desired maneuvers from multiple expert demonstrations. Our algorithm has enabled the successful execution of several parameterized aerobatic maneuvers by our autonomous helicopter.
Nimbus Goehausen, Pieter Abbeel
 ICRA3