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2020


Gripping apparatus and method of producing a gripping apparatus
Gripping apparatus and method of producing a gripping apparatus

Song, S., Sitti, M., Drotlef, D., Majidi, C.

Google Patents, Febuary 2020, US Patent App. 16/610,209 (patent)

Abstract
The present invention relates to a gripping apparatus comprising a membrane; a flexible housing; with said membrane being fixedly connected to a periphery of the housing. The invention further relates to a method of producing a gripping apparatus.

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[BibTex]

2020


[BibTex]


Method of actuating a shape changeable member, shape changeable member and actuating system
Method of actuating a shape changeable member, shape changeable member and actuating system

Hu, W., Lum, G. Z., Mastrangeli, M., Sitti, M.

Google Patents, January 2020, US Patent App. 16/477,593 (patent)

Abstract
The present invention relates to a method of actuating a shape changeable member of actuatable material. The invention further relates to a shape changeable member and to a system comprising such a shape changeable member and a magnetic field apparatus.

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[BibTex]


Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

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arXiv code (python) PDF [BibTex]

2007


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Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference

Schölkopf, B., Platt, J., Hofmann, T.

Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), pages: 1690, MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (proceedings)

Abstract
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.

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Web [BibTex]

2007


Web [BibTex]