Header logo is


2008


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Polymeric Micro/Nanofiber Manufacturing and Mechanical Characterization

Nain, A. S., Sitti, M., Amon, C.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 295-303, 2008 (inproceedings)

pi

[BibTex]

2008


[BibTex]


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An untethered magnetically actuated micro-robot capable of motion on arbitrary surfaces

Floyd, S., Pawashe, C., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 419-424, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Fabrication of bio-inspired elastomer nanofiber arrays with spatulate tips using notching effect

Kim, S., Sitti, M., Jang, J., Thomas, E. L.

In Nanotechnology, 2008. NANO’08. 8th IEEE Conference on, pages: 780-782, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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A motorized anchoring mechanism for a tethered capsule robot using fibrillar adhesives for interventions in the esophagus

Glass, P., Cheung, E., Wang, H., Appasamy, R., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, pages: 758-764, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

al

DOI [BibTex]

DOI [BibTex]


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In-lane Localization in Road Networks using Curbs Detected in Omnidirectional Height Images

Stueckler, J., Schulz, H., Behnke, S.

In Proceedings of Robotik 2008, 2008 (inproceedings)

ev

link (url) [BibTex]

link (url) [BibTex]


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Decoding of reach and grasp from MI population spiking activity using a low-dimensional model of hand and arm posture

Yadollahpour, P., Shakhnarovich, G., Vargas-Irwin, C., Donoghue, J. P., Black, M. J.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

ps

[BibTex]

[BibTex]


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A Dynamical System for Online Learning of Periodic Movements of Unknown Waveform and Frequency

Gams, A., Righetti, L., Ijspeert, A., Lenarčič, J.

In 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 85-90, IEEE, Scottsdale, USA, October 2008 (inproceedings)

Abstract
The paper presents a two-layered system for learning and encoding a periodic signal onto a limit cycle without any knowledge on the waveform and the frequency of the signal, and without any signal processing. The first dynamical system is responsible for extracting the main frequency of the input signal. It is based on adaptive frequency phase oscillators in a feedback structure, enabling us to extract separate frequency components without any signal processing, as all of the processing is embedded in the dynamics of the system itself. The second dynamical system is responsible for learning of the waveform. It has a built-in learning algorithm based on locally weighted regression, which adjusts the weights according to the amplitude of the input signal. By combining the output of the first system with the input of the second system we can rapidly teach new trajectories to robots. The systems works online for any periodic signal and can be applied in parallel to multiple dimensions. Furthermore, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, and is computationally inexpensive. Results using simulated and hand-generated input signals, along with applying the algorithm to a HOAP-2 humanoid robot are presented.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Passive compliant quadruped robot using central pattern generators for locomotion control

Rutishauser, S., Sproewitz, A., Righetti, L., Ijspeert, A.

In 2008 IEEE International Conference on Biomedical Robotics and Biomechatronics, pages: 710-715, IEEE, Scottsdale, USA, October 2008 (inproceedings)

Abstract
We present a new quadruped robot, ldquoCheetahrdquo, featuring three-segment pantographic legs with passive compliant knee joints. Each leg has two degrees of freedom - knee and hip joint can be actuated using proximal mounted RC servo motors, force transmission to the knee is achieved by means of a bowden cable mechanism. Simple electronics to command the actuators from a desktop computer have been designed in order to test the robot. A Central Pattern Generator (CPG) network has been implemented to generate different gaits. A parameter space search was performed and tested on the robot to optimize forward velocity.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Behavioral experiments on reinforcement learning in human motor control

Hoffmann, H., Theodorou, E., Schaal, S.

In Abstracts of the Eighteenth Annual Meeting of Neural Control of Movement (NCM), Naples, Florida, April 29-May 4, 2008, clmc (inproceedings)

Abstract
Reinforcement learning (RL) - learning solely based on reward or cost feedback - is widespread in robotics control and has been also suggested as computational model for human motor control. In human motor control, however, hardly any experiment studied reinforcement learning. Here, we study learning based on visual cost feedback in a reaching task and did three experiments: (1) to establish a simple enough experiment for RL, (2) to study spatial localization of RL, and (3) to study the dependence of RL on the cost function. In experiment (1), subjects sit in front of a drawing tablet and look at a screen onto which the drawing pen's position is projected. Beginning from a start point, their task is to move with the pen through a target point presented on screen. Visual feedback about the pen's position is given only before movement onset. At the end of a movement, subjects get visual feedback only about the cost of this trial. We choose as cost the squared distance between target and virtual pen position at the target line. Above a threshold value, the cost was fixed at this value. In the mapping of the pen's position onto the screen, we added a bias (unknown to subject) and Gaussian noise. As result, subjects could learn the bias, and thus, showed reinforcement learning. In experiment (2), we randomly altered the target position between three different locations (three different directions from start point: -45, 0, 45). For each direction, we chose a different bias. As result, subjects learned all three bias values simultaneously. Thus, RL can be spatially localized. In experiment (3), we varied the sensitivity of the cost function by multiplying the squared distance with a constant value C, while keeping the same cut-off threshold. As in experiment (2), we had three target locations. We assigned to each location a different C value (this assignment was randomized between subjects). Since subjects learned the three locations simultaneously, we could directly compare the effect of the different cost functions. As result, we found an optimal C value; if C was too small (insensitive cost), learning was slow; if C was too large (narrow cost valley), the exploration time was longer and learning delayed. Thus, reinforcement learning in human motor control appears to be sen

am

[BibTex]

[BibTex]


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Movement generation by learning from demonstration and generalization to new targets

Pastor, P., Hoffmann, H., Schaal, S.

In Adaptive Motion of Animals and Machines (AMAM), 2008, clmc (inproceedings)

am

PDF [BibTex]

PDF [BibTex]


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Combining dynamic movement primitives and potential fields for online obstacle avoidance

Park, D., Hoffmann, H., Schaal, S.

In Adaptive Motion of Animals and Machines (AMAM), Cleveland, Ohio, 2008, 2008, clmc (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


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Fabrication of Single and Multi-Layer Fibrous Biomaterial Scaffolds for Tissue Engineering

Nain, A. S., Miller, E., Sitti, M., Campbell, P., Amon, C.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 231-238, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Performance of different foot designs for a water running robot

Floyd, S., Adilak, S., Ramirez, S., Rogman, R., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 244-250, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Dynamic modeling of a basilisk lizard inspired quadruped robot running on water

Park, H. S., Floyd, S., Sitti, M.

In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages: 3101-3107, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Bacterial propulsion of chemically patterned micro-cylinders

Behkam, B., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, pages: 753-757, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Structure from Behavior in Autonomous Agents

Martius, G., Fiedler, K., Herrmann, J.

In Proc. IEEE Intl. Conf. Intelligent Robots and Systems (IROS 2008), pages: 858 - 862, 2008 (inproceedings)

al

DOI [BibTex]

DOI [BibTex]


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Orthogonal wall correction for visual motion estimation

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 1-6, May 2008 (inproceedings)

ev

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Neural activity in the motor cortex of humans with tetraplegia

Donoghue, J., Simeral, J., Black, M., Kim, S., Truccolo, W., Hochberg, L.

AREADNE Research in Encoding And Decoding of Neural Ensembles, June, Santorini, Greece, 2008 (conference)

ps

[BibTex]

[BibTex]


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Computational model for movement learning under uncertain cost

Theodorou, E., Hoffmann, H., Mistry, M., Schaal, S.

In Abstracts of the Society of Neuroscience Meeting (SFN 2008), Washington, DC 2008, 2008, clmc (inproceedings)

Abstract
Stochastic optimal control is a framework for computing control commands that lead to an optimal behavior under a given cost. Despite the long history of optimal control in engineering, it has been only recently applied to describe human motion. So far, stochastic optimal control has been mainly used in tasks that are already learned, such as reaching to a target. For learning, however, there are only few cases where optimal control has been applied. The main assumptions of stochastic optimal control that restrict its application to tasks after learning are the a priori knowledge of (1) a quadratic cost function (2) a state space model that captures the kinematics and/or dynamics of musculoskeletal system and (3) a measurement equation that models the proprioceptive and/or exteroceptive feedback. Under these assumptions, a sequence of control gains is computed that is optimal with respect to the prespecified cost function. In our work, we relax the assumption of the a priori known cost function and provide a computational framework for modeling tasks that involve learning. Typically, a cost function consists of two parts: one part that models the task constraints, like squared distance to goal at movement endpoint, and one part that integrates over the squared control commands. In learning a task, the first part of this cost function will be adapted. We use an expectation-maximization scheme for learning: the expectation step optimizes the task constraints through gradient descent of a reward function and the maximizing step optimizes the control commands. Our computational model is tested and compared with data given from a behavioral experiment. In this experiment, subjects sit in front of a drawing tablet and look at a screen onto which the drawing-pen's position is projected. Beginning from a start point, their task is to move with the pen through a target point presented on screen. Visual feedback about the pen's position is given only before movement onset. At the end of a movement, subjects get visual feedback only about the cost of this trial. In the mapping of the pen's position onto the screen, we added a bias (unknown to subject) and Gaussian noise. Therefore the cost is a function of this bias. The subjects were asked to reach to the target and minimize this cost over trials. In this behavioral experiment, subjects could learn the bias and thus showed reinforcement learning. With our computational model, we could model the learning process over trials. Particularly, the dependence on parameters of the reward function (Gaussian width) and the modulation of movement variance over time were similar in experiment and model.

am

[BibTex]

[BibTex]


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A Bayesian approach to empirical local linearizations for robotics

Ting, J., D’Souza, A., Vijayakumar, S., Schaal, S.

In International Conference on Robotics and Automation (ICRA2008), Pasadena, CA, USA, May 19-23, 2008, 2008, clmc (inproceedings)

Abstract
Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics systems where the the dynamics and kinematics are often not faithfully obtainable, empirical linearization may be preferable. In this case, it is important to only use data for the local linearization that lies within a ``reasonable'' linear regime of the system, which can be defined from the Hessian at the point of the linearization -- a quantity that is not available without an analytical model. We introduce a Bayesian approach to solve statistically what constitutes a ``reasonable'' local regime. We approach this problem in the context local linear regression. In contrast to previous locally linear methods, we avoid cross-validation or complex statistical hypothesis testing techniques to find the appropriate local regime. Instead, we treat the parameters of the local regime probabilistically and use approximate Bayesian inference for their estimation. This approach results in an analytical set of iterative update equations that are easily implemented on real robotics systems for real-time applications. As in other locally weighted regressions, our algorithm also lends itself to complete nonlinear function approximation for learning empirical internal models. We sketch the derivation of our Bayesian method and provide evaluations on synthetic data and actual robot data where the analytical linearization was known.

am

link (url) [BibTex]

link (url) [BibTex]


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Do humans plan continuous trajectories in kinematic coordinates?

Hoffmann, H., Schaal, S.

In Abstracts of the Society of Neuroscience Meeting (SFN 2008), Washington, DC 2008, 2008, clmc (inproceedings)

Abstract
The planning and execution of human arm movements is still unresolved. An ongoing controversy is whether we plan a movement in kinematic coordinates and convert these coordinates with an inverse internal model into motor commands (like muscle activation) or whether we combine a few muscle synergies or equilibrium points to move a hand, e.g., between two targets. The first hypothesis implies that a planner produces a desired end-effector position for all time points; the second relies on the dynamics of the muscular-skeletal system for a given control command to produce a continuous end-effector trajectory. To distinguish between these two possibilities, we use a visuomotor adaptation experiment. Subjects moved a pen on a graphics tablet and observed the pen's mapped position onto a screen (subjects quickly adapted to this mapping). The task was to move a cursor between two points in a given time window. In the adaptation test, we manipulated the velocity profile of the cursor feedback such that the shape of the trajectories remained unchanged (for straight paths). If humans would use a kinematic plan and map at each time the desired end-effector position onto control commands, subjects should adapt to the above manipulation. In a similar experiment, Wolpert et al (1995) showed adaptation to changes in the curvature of trajectories. This result, however, cannot rule out a shift of an equilibrium point or an additional synergy activation between start and end point of a movement. In our experiment, subjects did two sessions, one control without and one with velocity-profile manipulation. To skew the velocity profile of the cursor trajectory, we added to the current velocity, v, the function 0.8*v*cos(pi + pi*x), where x is the projection of the cursor position onto the start-goal line divided by the distance start to goal (x=0 at the start point). As result, subjects did not adapt to this manipulation: for all subjects, the true hand motion was not significantly modified in a direction consistent with adaptation, despite that the visually presented motion differed significantly from the control motion. One may still argue that this difference in motion was insufficient to be processed visually. Thus, as a control experiment, we replayed control and modified motions to the subjects and asked which of the two motions appeared 'more natural'. Subjects chose the unperturbed motion as more natural significantly better than chance. In summary, for a visuomotor transformation task, the hypothesis of a planned continuous end-effector trajectory predicts adaptation to a modified velocity profile. The current experiment found no adaptation under such transformation.

am

[BibTex]

[BibTex]


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Design and Numerical Modeling of an On-Board Chemical Release Module for Motion Control of Bacteria-Propelled Swimming Micro-Robots

Behkam, B., Nain, A. S., Amon, C. H., Sitti, M.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 239-244, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Investigation of Calcium Mechanotransduction by Quasi 3-D Microfiber Mechanical Stimulation of Cells

Ruder, W. C., Pratt, E. D., Sitti, M., LeDuc, P. R., Antaki, J. F.

In ASME 2008 Summer Bioengineering Conference, pages: 1049-1050, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Beanbag robotics: Robotic swarms with 1-dof units

Kriesel, D. M., Cheung, E., Sitti, M., Lipson, H.

In International Conference on Ant Colony Optimization and Swarm Intelligence, pages: 267-274, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Particle image velocimetry and thrust of flagellar micro propulsion systems

Danis, U., Sitti, M., Pekkan, K.

In APS Division of Fluid Dynamics Meeting Abstracts, 1, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


Thumb xl trajectory nips
Nonrigid Structure from Motion in Trajectory Space

Akhter, I., Sheikh, Y., Khan, S., Kanade, T.

In Neural Information Processing Systems, 1(2):41-48, 2008 (inproceedings)

Abstract
Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes, which have to be estimated anew for each video sequence. In contrast, we propose that the evolving 3D structure be described by a linear combination of basis trajectories. The principal advantage of this approach is that we do not need to estimate any basis vectors during computation. We show that generic bases over trajectories, such as the Discrete Cosine Transform (DCT) basis, can be used to compactly describe most real motions. This results in a significant reduction in unknowns, and corresponding stability in estimation. We report empirical performance, quantitatively using motion capture data, and qualitatively on several video sequences exhibiting nonrigid motions including piece-wise rigid motion, partially nonrigid motion (such as a facial expression), and highly nonrigid motion (such as a person dancing).

ps

pdf project page [BibTex]

pdf project page [BibTex]


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A modular bio-inspired architecture for movement generation for the infant-like robot iCub

Degallier, S., Righetti, L., Natale, L., Nori, F., Metta, G., Ijspeert, A.

In 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 795-800, IEEE, Scottsdale, USA, October 2008 (inproceedings)

Abstract
Movement generation in humans appears to be processed through a three-layered architecture, where each layer corresponds to a different level of abstraction in the representation of the movement. In this article, we will present an architecture reflecting this organization and based on a modular approach to human movement generation. We will show that our architecture is well suited for the online generation and modulation of motor behaviors, but also for switching between motor behaviors. This will be illustrated respectively through an interactive drumming task and through switching between reaching and crawling.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Study of the intermixing of Fe\textendashPt multilayers by analytical and high-resolution transmission electron microscopy

Sigle, W., Kaiser, T., Goll, D., Goo, N. H., Srot, V., van Aken, P. A., Detemple, E., Jäger, W.

In EMC2008, 14th European Microscopy Congress, Vol. 2: Materials Science, pages: 109-110, Springer, Aachen, Germany, 2008 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


Thumb xl sigalnips
Combined discriminative and generative articulated pose and non-rigid shape estimation

Sigal, L., Balan, A., Black, M. J.

In Advances in Neural Information Processing Systems 20, NIPS-2007, pages: 1337–1344, MIT Press, 2008 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]


no image
Reconstructing reach and grasp actions using neural population activity from Primary Motor Cortex

Vargas-Irwin, C. E., Yadollahpour, P., Shakhnarovich, G., Black, M. J., Donoghue, J. P.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

ps

[BibTex]

[BibTex]

2007


no image
Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers

Nakanishi, J., Mistry, M., Peters, J., Schaal, S.

In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)

Abstract
Compliant control will be a prerequisite for humanoid robotics if these robots are supposed to work safely and robustly in human and/or dynamic environments. One view of compliant control is that a robot should control a minimal number of degrees-of-freedom (DOFs) directly, i.e., those relevant DOFs for the task, and keep the remaining DOFs maximally compliant, usually in the null space of the task. This view naturally leads to task space control. However, surprisingly few implementations of task space control can be found in actual humanoid robots. This paper makes a first step towards assessing the usefulness of task space controllers for humanoids by investigating which choices of controllers are available and what inherent control characteristics they have—this treatment will concern position and orientation control, where the latter is based on a quaternion formulation. Empirical evaluations on an anthropomorphic Sarcos master arm illustrate the robustness of the different controllers as well as the eas e of implementing and tuning them. Our extensive empirical results demonstrate that simpler task space controllers, e.g., classical resolved motion rate control or resolved acceleration control can be quite advantageous in face of inevitable modeling errors in model-based control, and that well chosen formulations are easy to implement and quite robust, such that they are useful for humanoids.

ei

PDF Web DOI [BibTex]

2007


PDF Web DOI [BibTex]


no image
Sistema avanzato per la classificazione delle aree agricole in immagini ad elevata risoluzione geometrica: applicazione al territorio del Trentino

Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C.

In pages: 1-6, 11. Conferenza Nazionale ASITA, November 2007 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with Ensemble Method and Graph Sharpening

Choi, I., Shin, H.

In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Discriminative Subsequence Mining for Action Classification

Nowozin, S., BakIr, G., Tsuda, K.

In ICCV 2007, pages: 1919-1923, IEEE Computer Society, Los Alamitos, CA, USA, 11th IEEE International Conference on Computer Vision, October 2007 (inproceedings)

Abstract
Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But this ordering information can contain important information about the action itself, e.g. consider the sport disciplines of hurdle race and long jump, where the global temporal order of motions (running, jumping) is important to discriminate between the two. In this work we propose to use a sequential representation which retains this temporal order. Further, we introduce Discriminative Subsequence Mining to find optimal discriminative subsequence patterns. In combination with the LPBoost classifier, this amounts to simultaneously learning a classification function and performing feature selection in the space of all possible feature sequences. The resulting classifier linearly combines a small number of interpretable decision functions, each checking for the presence of a single discriminative pattern. The classifier is benchmarked on the KTH action classification data set and outperforms the best known results in the literature.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Unsupervised Classification for non-invasive Brain-Computer-Interfaces

Eren, S., Grosse-Wentrup, M., Buss, M.

In Automed 2007, pages: 65-66, VDI Verlag, Düsseldorf, Germany, Automed Workshop, October 2007 (inproceedings)

Abstract
Non-invasive Brain-Computer-Interfaces (BCIs) are devices that infer the intention of human subjects from signals generated by the central nervous system and recorded outside the skull, e.g., by electroencephalography (EEG). They can be used to enable basic communication for patients who are not able to communicate by normal means, e.g., due to neuro-degenerative diseases such as amyotrophic lateral sclerosis (ALS) (see [Vaughan2003] for a review). One challenge in research on BCIs is minimizing the training time prior to usage of the BCI. Since EEG patterns vary across subjects, it is usually necessary to record a number of trials in which the intention of the user is known to train a classifier. This classifier is subsequently used to infer the intention of the BCI-user. In this paper, we present the application of an unsupervised classification method to a binary noninvasive BCI based on motor imagery. The result is a BCI that does not require any training, since the mapping from EEG pattern changes to the intention of the user is learned online by the BCI without any feedback. We present experimental results from six healthy subjects, three of which display classification errors below 15%. We conclude that unsupervised BCIs are a viable option, but not yet as reliable as supervised BCIs. The rest of this paper is organized as follows. In the Methods section, we first introduce the experimental paradigm. This is followed by a description of the methods used for spatial filtering, feature extraction, and unsupervised classification. We then present the experimental results, and conclude the paper with a brief discussion.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., Schölkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 , pages: 13-31, (Editors: M Hutter and RA Servedio and E Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory (ALT), October 2007 (inproceedings)

Abstract
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


no image
Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., von Luxburg, U.

In ALT 2007, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)

Abstract
Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are ``identified‘‘: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds on the probability that cluster identification is successful, and use them to predict ``optimal‘‘ values of k for the mutual and symmetric k-nearest-neighbor graphs. We point out different properties of the mutual and symmetric nearest-neighbor graphs related to the cluster identification problem.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


Thumb xl floweval
A Database and Evaluation Methodology for Optical Flow

Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.

In Int. Conf. on Computer Vision, ICCV, pages: 1-8, Rio de Janeiro, Brazil, October 2007 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]


Thumb xl iccv07b
Shining a light on human pose: On shadows, shading and the estimation of pose and shape,

Balan, A., Black, M. J., Haussecker, H., Sigal, L.

In Int. Conf. on Computer Vision, ICCV, pages: 1-8, Rio de Janeiro, Brazil, October 2007 (inproceedings)

ps

pdf YouTube [BibTex]

pdf YouTube [BibTex]


no image
Inducing Metric Violations in Human Similarity Judgements

Laub, J., Macke, J., Müller, K., Wichmann, F.

In Advances in Neural Information Processing Systems 19, pages: 777-784, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Attempting to model human categorization and similarity judgements is both a very interesting but also an exceedingly difficult challenge. Some of the difficulty arises because of conflicting evidence whether human categorization and similarity judgements should or should not be modelled as to operate on a mental representation that is essentially metric. Intuitively, this has a strong appeal as it would allow (dis)similarity to be represented geometrically as distance in some internal space. Here we show how a single stimulus, carefully constructed in a psychophysical experiment, introduces l2 violations in what used to be an internal similarity space that could be adequately modelled as Euclidean. We term this one influential data point a conflictual judgement. We present an algorithm of how to analyse such data and how to identify the crucial point. Thus there may not be a strict dichotomy between either a metric or a non-metric internal space but rather degrees to which potentially large subsets of stimuli are represented metrically with a small subset causing a global violation of metricity.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Seeger, M.

In Advances in Neural Information Processing Systems 19, pages: 1233-1240, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
A Local Learning Approach for Clustering

Wu, M., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 1529-1536, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.

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PDF Web [BibTex]

PDF Web [BibTex]


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Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces

Grosse-Wentrup, M., Gramann, K., Buss, M.

In Advances in Neural Information Processing Systems 19, pages: 537-544, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
The performance of EEG-based Brain-Computer-Interfaces (BCIs) critically depends on the extraction of features from the EEG carrying information relevant for the classification of different mental states. For BCIs employing imaginary movements of different limbs, the method of Common Spatial Patterns (CSP) has been shown to achieve excellent classification results. The CSP-algorithm however suffers from a lack of robustness, requiring training data without artifacts for good performance. To overcome this lack of robustness, we propose an adaptive spatial filter that replaces the training data in the CSP approach by a-priori information. More specifically, we design an adaptive spatial filter that maximizes the ratio of the variance of the electric field originating in a predefined region of interest (ROI) and the overall variance of the measured EEG. Since it is known that the component of the EEG used for discriminating imaginary movements originates in the motor cortex, we design two adaptive spatial filters with the ROIs centered in the hand areas of the left and right motor cortex. We then use these to classify EEG data recorded during imaginary movements of the right and left hand of three subjects, and show that the adaptive spatial filters outperform the CSP-algorithm, enabling classification rates of up to 94.7 % without artifact rejection.

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PDF Web [BibTex]

PDF Web [BibTex]


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Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, O., Sindhwani, V., Keerthi, S.

In Advances in Neural Information Processing Systems 19, pages: 217-224, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Semi-supervised SVMs (S3VMs) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.

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PDF Web [BibTex]

PDF Web [BibTex]


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A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

In Advances in Neural Information Processing Systems 19, pages: 513-520, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be computed in $O(m^2)$ time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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PDF Web [BibTex]

PDF Web [BibTex]


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An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

Keerthi, S., Sindhwani, V., Chapelle, O.

In Advances in Neural Information Processing Systems 19, pages: 673-680, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold cross-validation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done; often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations.

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PDF Web [BibTex]

PDF Web [BibTex]


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Learning Dense 3D Correspondence

Steinke, F., Schölkopf, B., Blanz, V.

In Advances in Neural Information Processing Systems 19, pages: 1313-1320, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Establishing correspondence between distinct objects is an important and nontrivial task: correctness of the correspondence hinges on properties which are difficult to capture in an a priori criterion. While previous work has used a priori criteria which in some cases led to very good results, the present paper explores whether it is possible to learn a combination of features that, for a given training set of aligned human heads, characterizes the notion of correct correspondence. By optimizing this criterion, we are then able to compute correspondence and morphs for novel heads.

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PDF Web [BibTex]

PDF Web [BibTex]


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Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space

Ulges, A., Lampert, CH., Keysers, D., Breuel, TM.

In DAGM 2007, pages: 204-215, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)

Abstract
The extraction of a parametric global motion from a motion field is a task with several applications in video processing. We present two probabilistic formulations of the problem and carry out optimization using the RAST algorithm, a geometric matching method novel to motion estimation in video. RAST uses an exhaustive and adaptive search of transformation space and thus gives -- in contrast to local sampling optimization techniques used in the past -- a globally optimal solution. Among other applications, our framework can thus be used as a source of ground truth for benchmarking motion estimation algorithms. Our main contributions are: first, the novel combination of a state-of- the-art MAP criterion for dominant motion estimation with a search procedure that guarantees global optimality. Second, experimental re- sults that illustrate the superior performance of our approach on synthetic flow fields as well as real-world video streams. Third, a significant speedup of the search achieved by extending the mod el with an additional smoothness prior.

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Solving Deep Memory POMDPs with Recurrent Policy Gradients

Wierstra, D., Förster, A., Peters, J., Schmidhuber, J.

In ICANN‘07, pages: 697-706, Springer, Berlin, Germany, International Conference on Artificial Neural Networks, September 2007 (inproceedings)

Abstract
This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov decision problems (POMDPs) that require long-term memories of past observations. The approach involves approximating a policy gradient for a Recurrent Neural Network (RNN) by backpropagating return-weighted characteristic eligibilities through time. Using a “Long Short-Term Memory” architecture, we are able to outperform other RL methods on two important benchmark tasks. Furthermore, we show promising results on a complex car driving simulation task.

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PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]