Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedDT<strong>10</strong> Lince <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 14:00–15:00<br />
Tactile Exploration<br />
Chair Shinichi Hirai, Ritsumeikan Univ.<br />
Co-Chair<br />
14:00–14:15 WedDT<strong>10</strong>.1<br />
Online Spatio-Temporal Gaussian Process<br />
Experts with Application to Tactile Classification<br />
Harold Soh, Yanyu Su and Yiannis Demiris<br />
Personal Robotics Laboratory,<br />
Imperial College London, United Kingdom<br />
• Problem: Learning and Predicting<br />
Multivariate Time-series (e.g. sensor data).<br />
• Proposed Solution: STORK-GP, Sparse<br />
Online GP with novel Recursive Kernel<br />
based on relevance detection.<br />
• Application: Tactile classification.<br />
• Benefits: Method creates new models<br />
“on-the-fly” and refines existing models.<br />
• Experiments: High Accuracy comparable<br />
to extensively-optimised offline classifiers.<br />
• Download STORKG-GP:<br />
www.haroldsoh.com<br />
Online Tactile Classifier using<br />
STORK-GP Online Experts<br />
14:30–14:45 WedDT<strong>10</strong>.3<br />
3D Surface Reconstruction for Robotic Body<br />
Parts with Artificial Skins<br />
Philipp Mittendorfer and Gordon Cheng<br />
Institute for Cognitive Systems, Technische Universität München, Germany<br />
www.ics.ei.tum.de<br />
• We only utilize a-priori knowledge given by the elemental skin unit cell<br />
• We calculate relative positions and orientations of all unit cells in a patch<br />
• We utilize network relationships and gravity measurements in ≥ 2 poses<br />
• We only depend on sensor skin features ⇒ transferable between robots<br />
14:15–14:30 WedDT<strong>10</strong>.2<br />
Experimental Investigation of Surface<br />
Identification Ability of a Low-Profile Fabric<br />
Tactile Sensor<br />
Van Anh Ho, Masaaki Makikawa and Shinichi Hirai<br />
Department of Robotics, Ritsumeikan University, Japan<br />
Takahiro Araki<br />
Research and Development Department, Okamoto Corp., Japan<br />
• Design a fabric sensor with loops on the<br />
surface to enhance the slip detection, as<br />
well as to capture stck-slip events during<br />
sliding motion.<br />
• Three methods have been employed to<br />
evaluate recognition ability of the sensor<br />
over several typical texture.<br />
• Results show that ANN-based<br />
classification using Discrete Wavelet<br />
Transformation (DWT) of sensor’s<br />
signal outperformed the others.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–159–<br />
Sensor’s construction and DWT<br />
signals over textures<br />
14:45–15:00 WedDT<strong>10</strong>.4<br />
A Novel Dynamic Slip Prediction and Compensation<br />
Approach Based on Haptic Surface Exploration<br />
Xiaojing Song, Hongbin Liu, Joao Bimbo, Kaspar Althoefer<br />
and Lakmal D Seneviratne<br />
Department of Informatics, King’s College London, UK<br />
• Efficient haptic surface<br />
exploration to identify friction<br />
properties of object surfaces.<br />
• Slip threshold is predicted<br />
online based on identified<br />
friction property.<br />
• Slip compensator is<br />
implemented to prevent<br />
slippage during a dynamic<br />
grasping.