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Christina L. Campbell

dblp:40/6704 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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
2 papers
Motion planning and robot control · 73% Robot manipulation · 27%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot learning
0.112006
Superpositioning of behaviors learned through teleoperation · IEEE Trans. Robotics 2006
Robotics › Motion planning and robot control
robot control
0.012003
Robonaut task learning through teleoperation · ICRA 2003
Human-robot interaction
learning from demonstration
0.012003
Robonaut task learning through teleoperation · ICRA 2003
Human-robot interaction
robot learning
0.012003
Robonaut task learning through teleoperation · ICRA 2003
Robotics › Robot manipulation
grasping
0.012006
Superpositioning of behaviors learned through teleoperation · IEEE Trans. Robotics 2006
Robotics › Robot manipulation › grasping
reach-to-grasp
0.012006
Superpositioning of behaviors learned through teleoperation · IEEE Trans. Robotics 2006
Human-robot interaction › teleoperation
virtual reality teleoperation
0.012003
Robonaut task learning through teleoperation · ICRA 2003

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

time-series segmentation · 0.1teleoperation · 0.1episode averaging · 0.1verbs and adverbs algorithm · 0.1
YearPublicationVenuePosition
2006 Superpositioning of behaviors learned through teleoperation
abstract
This paper reports that the superposition of a small set of behaviors, learned via teleoperation, can lead to robust completion of an articulated reach-and-grasp task. The results support the hypothesis that a robot can learn to interact purposefully with its environment through a developmental acquisition of sensory-motor coordination. Teleoperation can bootstrap the process by enabling the robot to observe its own sensory responses to actions that lead to specific outcomes within an environment. It is shown that a reach-and-grasp task, learned by an articulated robot through a small number of teleoperated trials, can be performed autonomously with success in the face of significant variations in the environment and perturbations of the goal. In particular, teleoperation of the robot to reach and grasp an object at nine different locations in its workspace enabled robust autonomous performance of the task anywhere within the workspace. Superpositioning was performed using the Verbs and Adverbs algorithm that was developed originally for the graphical animation of articulated characters. The work was performed on Robonaut, the NASA space-capable humanoid at Johnson Space Center, Houston, TX.
Christina L. Campbell, Richard Alan Peters II, Bobby Bodenheimer, William Bluethmann, Eric Huber, Robert O. Ambrose
IEEE Trans. Robotics1
2003 Robonaut task learning through teleoperation
abstract
This paper addresses the problem of automatic skill acquisition by a robot. It reports that six trials of a reach-grasp-release-retract skill are sufficient for learning a canonical description of the task under the following circumstances: The robot is Robonaut, NASA's space-capable, dexterous humanoid. Robonaut was teleoperated by a person using full immersion Virtual Reality technology that transforms the operator's arm and hand motions into those of the robot. The operator's sole source of real-time feedback was visual. During the six trials all of the Robot's sensory inputs and motor control parameters were recorded as time-series. Later the time-series from each trial was partitioned into the same number of episodes as a function of changes in the motor parameter sequence. The episodes were time normalized and averaged across trials The resultant motor parameter sequence and sensor signals were used to control the robot without the teleoperator. The robot was able to perform the task autonomously with robot starting positions and object locations both similar to, and different from the original trials.
Richard Alan Peters II, Christina L. Campbell, William Bluethmann, Eric Huber
ICRA2