Header logo is


1999


no image
Segmentation of endpoint trajectories does not imply segmented control

Sternad, D., Schaal, D.

Experimental Brain Research, 124(1):118-136, 1999, clmc (article)

Abstract
While it is generally assumed that complex movements consist of a sequence of simpler units, the quest to define these units of action, or movement primitives, still remains an open question. In this context, two hypotheses of movement segmentation of endpoint trajectories in 3D human drawing movements are re-examined: (1) the stroke-based segmentation hypothesis based on the results that the proportionality coefficient of the 2/3 power law changes discontinuously with each new â??strokeâ?, and (2) the segmentation hypothesis inferred from the observation of piecewise planar endpoint trajectories of 3D drawing movements. In two experiments human subjects performed a set of elliptical and figure-8 patterns of different sizes and orientations using their whole arm in 3D. The kinematic characteristics of the endpoint trajectories and the seven joint angles of the arm were analyzed. While the endpoint trajectories produced similar segmentation features as reported in the literature, analyses of the joint angles show no obvious segmentation but rather continuous oscillatory patterns. By approximating the joint angle data of human subjects with sinusoidal trajectories, and by implementing this model on a 7-degree-of-freedom anthropomorphic robot arm, it is shown that such a continuous movement strategy can produce exactly the same features as observed by the above segmentation hypotheses. The origin of this apparent segmentation of endpoint trajectories is traced back to the nonlinear transformations of the forward kinematics of human arms. The presented results demonstrate that principles of discrete movement generation may not be reconciled with those of rhythmic movement as easily as has been previously suggested, while the generalization of nonlinear pattern generators to arm movements can offer an interesting alternative to approach the question of units of action.

am

link (url) [BibTex]

1999


link (url) [BibTex]


no image
Two-dimensional fine particle positioning using a piezoresistive cantilever as a micro/nano-manipulator

Sitti, M., Hashimoto, H.

In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on, 4, pages: 2729-2735, 1999 (inproceedings)

pi

[BibTex]

[BibTex]


no image
Teleoperated nano scale object manipulation

Sitti, M., Hashimoto, H.

Recent Advances on Mechatronics, pages: 322-335, Singapore: Springer-Verlag, 1999 (article)

pi

[BibTex]

[BibTex]

1996


no image
A kendama learning robot based on a dynamic optimiation principle

Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Rieka, O., Nakano, E., Wada, Y., Kawato, M.

In Preceedings of the International Conference on Neural Information Processing, pages: 938-942, Hong Kong, September 1996, clmc (inproceedings)

am

[BibTex]

1996


[BibTex]


no image
Incorporating invariances in support vector learning machines

Schölkopf, B., Burges, C., Vapnik, V.

In Artificial Neural Networks: ICANN 96, LNCS vol. 1112, pages: 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science (inproceedings)

Abstract
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
A Kendama learning robot based on bi-directional theory

Miyamoto, H., Schaal, S., Gandolfo, F., Koike, Y., Osu, R., Nakano, E., Wada, Y., Kawato, M.

Neural Networks, 9(8):1281-1302, 1996, clmc (article)

Abstract
A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions.

am

link (url) [BibTex]

link (url) [BibTex]


no image
From isolation to cooperation: An alternative of a system of experts

Schaal, S., Atkeson, C. G.

In Advances in Neural Information Processing Systems 8, pages: 605-611, (Editors: Touretzky, D. S.;Mozer, M. C.;Hasselmo, M. E.), MIT Press, Cambridge, MA, 1996, clmc (inbook)

Abstract
We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensionality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task oriented incremental learning. 

am

link (url) [BibTex]

link (url) [BibTex]


no image
One-handed juggling: A dynamical approach to a rhythmic movement task

Schaal, S., Sternad, D., Atkeson, C. G.

Journal of Motor Behavior, 28(2):165-183, 1996, clmc (article)

Abstract
The skill of rhythmic juggling a ball on a racket is investigated from the viewpoint of nonlinear dynamics. The difference equations that model the dynamical system are analyzed by means of local and non-local stability analyses. These analyses yield that the task dynamics offer an economical juggling pattern which is stable even for open-loop actuator motion. For this pattern, two types of pre dictions are extracted: (i) Stable periodic bouncing is sufficiently characterized by a negative acceleration of the racket at the moment of impact with the ball; (ii) A nonlinear scaling relation maps different juggling trajectories onto one topologically equivalent dynamical system. The relevance of these results for the human control of action was evaluated in an experiment where subjects performed a comparable task of juggling a ball on a paddle. Task manipulations involved different juggling heights and gravity conditions of the ball. The predictions were confirmed: (i) For stable rhythmic performance the paddle's acceleration at impact is negative and fluctuations of the impact acceleration follow predictions from global stability analysis; (ii) For each subject, the realizations of juggling for the different experimental conditions are related by the scaling relation. These results allow the conclusion that for the given task, humans reliably exploit the stable solutions inherent to the dynamics of the task and do not overrule these dynamics by other control mechanisms. The dynamical scaling serves as an efficient principle to generate different movement realizations from only a few parameter changes and is discussed as a dynamical formalization of the principle of motor equivalence.

am

link (url) [BibTex]

link (url) [BibTex]


no image
In vivo diabetic wound healing with nanofibrous scaffolds modified with gentamicin and recombinant human epidermal growth factor

Dwivedi, C., Pandey, I., Pandey, H., Patil, S., Mishra, S. B., Pandey, A. C., Zamboni, P., Ramteke, P. W., Singh, A. V.

Journal of Biomedical Materials Research Part A, 106(3):641-651, March (article)

Abstract
Abstract Diabetic wounds are susceptible to microbial infection. The treatment of these wounds requires a higher payload of growth factors. With this in mind, the strategy for this study was to utilize a novel payload comprising of Eudragit RL/RS 100 nanofibers carrying the bacterial inhibitor gentamicin sulfate (GS) in concert with recombinant human epidermal growth factor (rhEGF); an accelerator of wound healing. GS containing Eudragit was electrospun to yield nanofiber scaffolds, which were further modified by covalent immobilization of rhEGF to their surface. This novel fabricated nanoscaffold was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, and X‐ray diffraction. The thermal behavior of the nanoscaffold was determined using thermogravimetric analysis and differential scanning calorimetry. In the in vitro antibacterial assays, the nanoscaffolds exhibited comparable antibacterial activity to pure gentemicin powder. In vivo work using female C57/BL6 mice, the nanoscaffolds induced faster wound healing activity in dorsal wounds compared to the control. The paradigm in this study presents a robust in vivo model to enhance the applicability of drug delivery systems in wound healing applications. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 641–651, 2018.

pi

link (url) DOI [BibTex]


link (url) DOI [BibTex]


Thumb xl gpcr cons
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties

Marco, A., Baumann, D., Hennig, P., Trimpe, S.

Submitted to Journal (under review) (article)

Abstract
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll-outs result in failure behaviors, causing premature experiment detention. In such cases, the designer is forced to decide on heuristic cost penalties because the acquired data is often scarce, or not comparable with that of the stable policies. To overcome this, we propose a Bayesian model that captures exactly what we know about the cost of unstable controllers prior to data collection: Nothing, except that it should be a somewhat large number. The resulting Bayesian model, approximated with a Gaussian process, predicts high cost values in regions where failures are likely to occur. In this way, the model guides the BO exploration toward regions of stability. We demonstrate the benefits of the proposed model in several illustrative and statistical synthetic benchmarks, and also in experiments on a real robotic platform. In addition, we propose and experimentally validate a new BO method to account for unknown constraints. Such method is an extension of Max-Value Entropy Search, a recent information-theoretic method, to solve unconstrained global optimization problems.

PDF link (url) [BibTex]


no image
test jon
(book)

[BibTex]


no image
test
(article)

[BibTex]

[BibTex]


no image
Geometric Image Synthesis

Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C.

(conference)

avg

Project Page [BibTex]

Project Page [BibTex]


no image
Robotics Research

Tong, Chi Hay, Furgale, Paul, Barfoot, Timothy D, Guizilini, Vitor, Ramos, Fabio, Chen, Yushan, T\uumová, Jana, Ulusoy, Alphan, Belta, Calin, Tenorth, Moritz, others

(article)

pi

[BibTex]

[BibTex]