Learning Stable Task Sequences from Demonstration with Linear Parameter Varying Systems and Hidden Markov Models
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Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear ...
Augmented paper has been proposed as a way to integrate more easily ICTs in settings like formal education, where paper has a strong presence. However, despite the multiplicity of educational applications using paper-based computing, their deployment in au ...
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This paper presents a publicly available toolkit and a benchmark suite for rigorous verification of Integer Numerical Transition Systems (INTS), which can be viewed as control-flow graphs whose edges are annotated by Presburger arithmetic formulas. We pres ...
This paper presents two weak partially synchronous system models Manti(n-k) and Msink(n-k), which are just strong enough for solving k-set agreement: We introduce the generalized (n-k)-loneliness failure detector L(k), which we first prove to be sufficient ...
We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the ...
This paper presents a method for learning discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e. time-invariant) Dynamical System (DS), and define sufficient conditions to ensure global asymptotic stability a ...
We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrat ...
Institute of Electrical and Electronics Engineers2010
We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrat ...