Learning of object manipulation operations from continuous multimodal input

Großekathöfer, Ulf; Barchunova, Alexandra; Haschke, Robert; Hermann, Thomas; Franzius, Mathias; Ritter, Helge

doi:10.1109/humanoids.2011.6100880

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…We are striving for a better understanding of human grasping and manipulation skills and are convinced that by observing humans we can gain a deeper insight into the control processes involved in manual intelligence [5,21,22]. Having only postural information about the hand does not reveal the associated forces and we therefore sought an explicit sensing channel for contact forces during manipulation.…”

Section: Tactile Dataglovementioning

confidence: 98%

Flexible and stretchable fabric-based tactile sensor

Büscher

Kõiva

Schürmann

et al. 2015

Robotics and Autonomous Systems

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192

115

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h i g h l i g h t s• A flexible and stretchable durable fabric-based tactile sensor capable of capturing typical human interaction forces was developed.• We present elaborate measurement results of the sensor. • A process of creating multiple sensor areas in a single fabric patch was developed.• The measures against performance degradation due to moisture are presented. • Using the developed technology, a tactile dataglove with 54 pressure sensitive regions was built.a r t i c l e i n f o Article history: Available online xxxx Keywords: Tactile sensor Flexible tactile sensor Stretchable tactile sensor Tactile dataglove a b s t r a c tWe introduce a novel, fabric-based, flexible, and stretchable tactile sensor, which is capable of seamlessly covering natural shapes. As humans and robots have curved body parts that move with respect to each other, the practical usage of traditional rigid tactile sensor arrays is limited. Rather, a flexible tactile skin is required. Our design allows for several tactile cells to be embedded in a single sensor patch. It can have an arbitrary perimeter and can cover free-form surfaces. In this article we discuss the construction of the sensor and evaluate its performance. Our flexible tactile sensor remains operational on top of soft padding such as a gel cushion, enabling the construction of a human-like soft tactile skin. The sensor allows pressure measurements to be read from a subtle less than 1 kPa up to high pressures of more than 500 kPa, which easily covers the common range for everyday human manual interactions. Due to a layered construction, the sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage.As an exciting application for the sensor, we describe the construction of a wearable tactile dataglove with 54 tactile cells and embedded data acquisition electronics. We also discuss the necessary implementation details to maintain long term sensor performance in the presence of moisture.

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Section: Tactile Dataglovementioning

confidence: 98%

Flexible and stretchable fabric-based tactile sensor

Büscher

Kõiva

Schürmann

et al. 2015

Robotics and Autonomous Systems

Self Cite

192

115

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“…Furthermore, our recent work has showed that audio and tactile data can generate a symbolic sequence of action primitives of sufficient granularity and semantic content . The automatically extracted action primitives have been successfully used in a classification application with HMM-based models (Grossekathöfer et al, 2011). During a considerable number of simple object manipulations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input

Barchunova¹,

Moringen²,

Haschke³

et al. 2012

PsycEXTRA Dataset

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We propose a hierarchical approach for Bayesian modeling and segmentation of continuous sequences of bimanual object manipulations. Based on bimodal (audio and tactile) low-level time series, the presented approach identifies semantically differing subsequences. It consists of two hierarchically executed stages, each of which employs a Bayesian method for unsupervised change point detection (Fearnhead, 2005). In the first step we propose to use a mixture of model pairs for bimanual tactile data. To this end, we select "object interaction" and "no object interaction" regions for the left and the right hand synchronously. In the second step we apply a set of Autoregressive (AR) models to the audio data. This allows us to select regions within "object interaction" segments according to qualitative changes in the audio signal. Two simple model types that allow the calculation of modality-specific segment likelihoods serve as a foundation for this modeling approach. Based on the acquired ground truth, empirical evaluation has showed that the generated segments correctly capture the semantic structure of the test time series. The developed procedure can serve as a building block for higher-level action and activity modeling frameworks.

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On the Role of Embodiment for Self-Organizing Robots: Behavior As Broken Symmetry

Der

2014

Guided Self-Organization: Inception

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Learning of object manipulation operations from continuous multimodal input

Cited by 4 publications

References 14 publications

Flexible and stretchable fabric-based tactile sensor

Flexible and stretchable fabric-based tactile sensor

Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input

On the Role of Embodiment for Self-Organizing Robots: Behavior As Broken Symmetry

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