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Maurice R. Masliah

dblp:55/1419 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2000
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, 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.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 44% Human-robot interaction · 44% Immersive interaction · 13%

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

TopicWeightPapersLastEvidence papers
Human-robot interaction › shared control
control allocation
0.012000
Measuring the allocation of control in a 6 degree-of-freedom docking experiment · CHI 2000
Interaction techniques and input
input device
0.012000
Measuring the allocation of control in a 6 degree-of-freedom docking experiment · CHI 2000
Immersive interaction
virtual reality
0.012000
Measuring the allocation of control in a 6 degree-of-freedom docking experiment · CHI 2000

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

m-metric · 0.0docking task experiment · 0.0
YearPublicationVenuePosition
2000 Measuring the allocation of control in a 6 degree-of-freedom docking experiment
abstract
Coordination definitions and metrics are reviewed from the motor control, biomedical, and human factors literature. This paper presents an alternative measurement called the M-metric, the product of the simultaneity and efficiency of a trajectory, as a means of quantifying allocation of control within a docking task. A 6 degree-of-freedom (DOF) longitudinal virtual docking task experiment was conducted to address how control is allocated across six DOFs, how allocation of control changes with extended practice, and if differences in the allocation of control are input device dependent. The results show that operators, rather than controlling all 6 DOFs equally, allocate their control to the rotational and translational DOFs separately, and switch control between the two groups. With practice, allocation of control within the translational and rotational subsets increases at a faster rate than across all 6 DOFs together.
Maurice R. Masliah, Paul Milgram
CHI1
1998 The mobile robot surrogate method for developing autonomy
abstract
The mobile robot surrogate (MRS) method, as a means for developing autonomy in complex systems, is presented. Used to analyze and interpret sonar data for landmark identification and navigation, this method involves: 1) putting a human operator within a telerobotic loop; 2) studying the capabilities and limitations of the operator for different tasks; 3) using protocol analysis to extract the knowledge from the operator into an expert system; 4) substituting the expert system for the operator within the robotic loop. A central aspect of the MRS methodology is that the operator, through the telerobotic interface, is limited to the same sensor information as the robot. The strength of this methodology is shown through the development of door identification and door traversal performances by an autonomous mobile robot using ultrasonic data.
Maurice R. Masliah, Robert W. Albrecht
IEEE Trans. Robotics Autom.1