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Many methods for robot selection have been developed to date.
This research aims to evaluate new methods for robot motion control and camera orientation control through the operator's head orientation in robot teleoperation tasks.
We summarize traditional methods for robot control and then present an approach to based on biological principles, with emphasis on perception, control, and cognitive architectures.
To determine how suboptimal their plans are, we study two planning methods for robot navigation in initially unknown terrain that have successfully been used on mobile robots but not been analyzed before.
In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization.
Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model.
Similar(51)
He was recently awarded a grant from the National Science Foundation (NSF) to develop methods for robot-assisted vocational training.
Several studies have proposed whole-body tactile sensing methods for robots.
This paper introduces a novel probabilistic method for robot based object segmentation.
Human stance control is compared to a suggested method for robot stance control called zero moment point (ZMP) compensation.
This paper introduces a new robust position variable structure controller design method for robot position systems with parameter perturbations.
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