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Meihua Tai

dblp:40/5731 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 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
1 paper
Motion planning and robot control · 87% Legged, aerial and field robots · 13%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
nonholonomic systems
0.012004
Model Reduction Method for Nonholonomic Mechanical Systems with Semidirect Product Symmetry · ICRA 2004
Robotics › Motion planning and robot control
robot dynamics
0.012004
Model Reduction Method for Nonholonomic Mechanical Systems with Semidirect Product Symmetry · ICRA 2004
Robotics › Legged, aerial and field robots › field robotics
spherical robot
0.012004
Model Reduction Method for Nonholonomic Mechanical Systems with Semidirect Product Symmetry · ICRA 2004

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

nonholonomic constraints · 0.0hamilton's variational principle · 0.0
YearPublicationVenuePosition
2004 Model Reduction Method for Nonholonomic Mechanical Systems with Semidirect Product Symmetry
abstract
In this paper, a method of deriving reduced dynamic models for a class of nonholonomically constrained systems that are described on semidirect products is developed. The theory is built upon the Lagrangian reduction method for unconstrained systems on semidirect products and the Lagrangian reduction theory for nonholonomic systems defined on symmetry groups. The reduction is achieved by first applying Hamilton's variational principle and then imposing the nonholonomic constraints. The theory is then applied to Chaplygin's gyro type of spherical robots.
Meihua Tai
ICRA1
2004 Different Predictions by the Minimum Variance and Minimum Torque-Change Models on the Skewness of Movement Velocity Profiles
abstract
We investigated the differences between two well-known optimization principles for understanding movement planning: the minimum variance (MV) model of Harris and Wolpert (1998) and the minimum torque change (MTC) model of Uno, Kawato, and Suzuki (1989). Both models accurately describe the properties of human reaching movements in ordinary situations (e.g., nearly straight paths and bell-shaped velocity profiles). However, we found that the two models can make very different predictions when external forces are applied or when the movement duration is increased. We considered a second-order linear system for the motor plant that has been used previously to simulate eye movements and single-joint arm movements and were able to derive analytical solutions based on the MV and MTC assumptions. With the linear plant, the MTC model predicts that the movement velocity profile should always be symmetrical, independent of the external forces and movement duration. In contrast, the MV model strongly depends on the movement duration and the system's degree of stability; the latter in turn depends on the total forces. The MV model thus predicts a skewed velocity profile under many circumstances. For example, it predicts that the peak location should be skewed toward the end of the movement when the movement duration is increased in the absence of any elastic force. It also predicts that with appropriate viscous and elastic forces applied to increase system stability, the velocity profile should be skewed toward the beginning of the movement. The velocity profiles predicted by the MV model can even show oscillations when the plant becomes highly oscillatory. Our analytical and simulation results suggest specific experiments for testing the validity of the two models.
Hirokazu Tanaka, Meihua Tai, Ning Qian
Neural Comput.2