Dynamic Mode Decomposition for perturbation estimation in human robot interaction

Berger, Erik; Sastuba, Mark; Vogt, David; Jung, Bernhard; Amor, Heni Ben

doi:10.1109/roman.2014.6926317

Cited by 21 publications

(14 citation statements)

References 20 publications

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“…Recently, many sparse optimization algorithms have been proposed in the fields of signal and image processing [16], compressive sensing [17], machine learning [25], and data mining [26] for consideration that the optimization variables have some sparse structures. Sparse optimization makes it possible to reconstruct high-dimensional signals and extract potential information from a small amount of data.…”

Section: Sparse Optimized Dmd Via Non-convex Regularizationmentioning

confidence: 99%

“…DMD can also be used for biometrics to detect fraudulent samples [15]. In the field of robotics and neuroscience, DMD has been used to estimate the perturbation of human-robot interactions [16], and the coherent modes in large-scale neural recording have been extracted [17]. Nowadays, theoretical research with DMD mainly focuses on two aspects.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.

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Section: Sparse Optimized Dmd Via Non-convex Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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“…These modes essentially capture different large-scale to smallscale structures (sparse components) including a background structure (low-rank model) [7]. DMD has gained significant applications in various fields [2,3,16], including for detecting spoof samples from facial authentication video data sets [33] and for detecting spoofed finger-vein images [31]. The advantage of this method is its ability to identify regions of dominant motion in an image sequence in a completely data-driven manner without relying on any prior assumptions about the patterns of behaviour within the data.…”

Section: Motivation: Dynamic Mode Decomposition (Dmd)mentioning

confidence: 99%

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Tirunagari

Poh

Wells

et al. 2017

Machine Vision and Applications

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“…In addition, nonlinearities underlying the current robot or task can often lead to instabilities in the system [21]. To circumvent such challenges, several approaches have been proposed for learning perturbation filters using a datadriven machine learning method [3,4,5]. An alternative, bio-inspired approach was proposed in [19].…”

Section: Related Workmentioning

confidence: 99%

Robots that anticipate pain: Anticipating physical perturbations from visual cues through deep predictive models

Sur

Amor

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple subnetworks for dealing with robot dynamics and perceptual input from the environment.We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.

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Dynamic Mode Decomposition for perturbation estimation in human robot interaction

Cited by 21 publications

References 20 publications

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Robots that anticipate pain: Anticipating physical perturbations from visual cues through deep predictive models

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