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2020


Sweat softens the outermost layer of the human finger pad: evidence from simulations and experiments
Sweat softens the outermost layer of the human finger pad: evidence from simulations and experiments

Nam, S., Kuchenbecker, K. J.

Work-in-progress poster presented at Eurohaptics Conference, Leiden, The Netherlands, September 2020, Award for best poster in 2020 (poster)

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Project Page [BibTex]

2020


Project Page [BibTex]


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Haptify: a Comprehensive Benchmarking System for Grounded Force-Feedback Haptic Devices

Fazlollahi, F., Kuchenbecker, K. J.

Work-in-progress poster presented at the IEEE Eurohaptics Conference, Leiden, Netherlands, September 2020 (poster)

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[BibTex]

[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]

2016


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Distinct adaptation to abrupt and gradual torque perturbations with a multi-joint exoskeleton robot

Oh, Y., Sutanto, G., Mistry, M., Schweighofer, N., Schaal, S.

Abstracts of Neural Control of Movement Conference (NCM 2016), Montego Bay, Jamaica, April 2016 (poster)

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[BibTex]

2016


[BibTex]

2009


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Bayesian Quadratic Reinforcement Learning

Hennig, P., Stern, D., Graepel, T.

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

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PDF Web [BibTex]

2009


PDF Web [BibTex]