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2013


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Magnetically Actuated Soft Capsule With the Multimodal Drug Release Function

Yim, S., Goyal, K., Sitti, M.

IEEE/ASME Trans. on Mechatronics, 18(4):1413-1418, IEEE, 2013 (article)

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Project Page [BibTex]

2013


Project Page [BibTex]


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Optimal distribution of contact forces with inverse-dynamics control

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., Schaal, S.

The International Journal of Robotics Research, 32(3):280-298, March 2013 (article)

Abstract
The development of legged robots for complex environments requires controllers that guarantee both high tracking performance and compliance with the environment. More specifically the control of the contact interaction with the environment is of crucial importance to ensure stable, robust and safe motions. In this contribution we develop an inverse-dynamics controller for floating-base robots under contact constraints that can minimize any combination of linear and quadratic costs in the contact constraints and the commands. Our main result is the exact analytical derivation of the controller. Such a result is particularly relevant for legged robots as it allows us to use torque redundancy to directly optimize contact interactions. For example, given a desired locomotion behavior, we can guarantee the minimization of contact forces to reduce slipping on difficult terrains while ensuring high tracking performance of the desired motion. The main advantages of the controller are its simplicity, computational efficiency and robustness to model inaccuracies. We present detailed experimental results on simulated humanoid and quadruped robots as well as a real quadruped robot. The experiments demonstrate that the controller can greatly improve the robustness of locomotion of the robots.1

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Tank-like module-based climbing robot using passive compliant joints

Seo, T., Sitti, M.

IEEE/ASME Transactions on Mechatronics, 18(1):397-408, 2013 (article)

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[BibTex]

[BibTex]


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Enhanced fabrication and characterization of gecko-inspired mushroom-tipped microfiber adhesives

Song, J., Mengüç, Y., Sitti, M.

Journal of Adhesion Science and Technology, 27(17):1921-1932, Routledge, 2013 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Micro-scale mobile robotics

Diller, E., Sitti, M.

Foundations and Trends in Robotics, 2(3):143-259, Now Publishers Incorporated, 2013 (article)

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[BibTex]

[BibTex]


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Survey and Introduction to the Focused Section on Bio-Inspired Mechatronics

Sitti, M., Menciassi, A., Ijspeert, A., Low, K. H., Kim, S.

Mechatronics, IEEE/ASME Transactions on, 18(2):409-418, DOI: 10.1109/TMECH.2012. 2233492, 2013 (article)

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[BibTex]

[BibTex]

2012


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From Dynamic Movement Primitives to Associative Skill Memories

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., Schaal, S.

Robotics and Autonomous Systems, 2012 (article)

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Project Page [BibTex]

2012


Project Page [BibTex]


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Two-dimensional autonomous microparticle manipulation strategies for magnetic microrobots in fluidic environments

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

IEEE Transactions on Robotics, 28(2):467-477, IEEE, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Three-dimensional microfiber devices that mimic physiological environments to probe cell mechanics and signaling

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

Lab on a Chip, 12(10):1775-1779, Royal Society of Chemistry, 2012 (article)

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[BibTex]

[BibTex]


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Active visual search in unknown environments using uncertain semantics

Aydemir, Alper, Pronobis, Andrzej, Jensfelt, Patric, Sj, Kristoffer, Aydemir, Alper, Jensfelt, Patric, Aydemir, A, Jensfelt, P, Aydemir, A, Jensfelt, P, others

Transactions, 1, pages: 2329-2335, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Modelling of conductive atomic force microscope probes for scanning tunnelling microscope operation

Ozcan, O, Sitti, M

IET Micro \& Nano Letters, 7(4):329-333, IET, 2012 (article)

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[BibTex]

[BibTex]


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Shape memory polymer-based flexure stiffness control in a miniature flapping-wing robot

Hines, L., Arabagi, V., Sitti, M.

IEEE Transactions on Robotics, 28(4):987-990, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Micro-manipulation using rotational fluid flows induced by remote magnetic micro-manipulators

Ye, Z., Diller, E., Sitti, M.

Journal of Applied Physics, 112(6):064912, AIP, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Remotely addressable magnetic composite micropumps

Diller, E., Miyashita, S., Sitti, M.

Rsc Advances, 2(9):3850-3856, Royal Society of Chemistry, 2012 (article)

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[BibTex]

[BibTex]


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Model-free reinforcement learning of impedance control in stochastic environments

Stulp, Freek, Buchli, Jonas, Ellmer, Alice, Mistry, Michael, Theodorou, Evangelos A., Schaal, S.

Autonomous Mental Development, IEEE Transactions on, 4(4):330-341, 2012 (article)

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[BibTex]

[BibTex]


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Shape-Programmable Soft Capsule Robots for Semi-Implantable Drug Delivery

Yim, S., Sitti, M.

Mechatronics, IEEE/ASME Transactions on, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Control of multiple heterogeneous magnetic microrobots in two dimensions on nonspecialized surfaces

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

IEEE Transactions on Robotics, 28(1):172-182, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Gecko-Inspired Controllable Adhesive Structures Applied to Micromanipulation

Mengüç, Y., Yang, S. Y., Kim, S., Rogers, J. A., Sitti, M.

Advanced Functional Materials, 22(6):1245-1245, WILEY-VCH Verlag, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Elastomer surfaces with directionally dependent adhesion strength and their use in transfer printing with continuous roll-to-roll applications

Yang, S. Y., Carlson, A., Cheng, H., Yu, Q., Ahmed, N., Wu, J., Kim, S., Sitti, M., Ferreira, P. M., Huang, Y., others,

Advanced Materials, 24(16):2117-2122, WILEY-VCH Verlag, 2012 (article)

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[BibTex]

[BibTex]


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Effect of retraction speed on adhesion of elastomer fibrillar structures

Abusomwan, U., Sitti, M.

Applied Physics Letters, 101(21):211907, AIP, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation

Stulp, F., Theodorou, E., Schaal, S.

IEEE Transactions on Robotics, 2012 (article)

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[BibTex]

[BibTex]


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Impact and Surface Tension in Water: a Study of Landing Bodies

Shih, B., Laham, L., Lee, K. J., Krasnoff, N., Diller, E., Sitti, M.

Bio-inspired Robotics Final Project, Carnegie Mellon University, 2012 (article)

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[BibTex]

[BibTex]


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Design and rolling locomotion of a magnetically actuated soft capsule endoscope

Yim, S., Sitti, M.

IEEE Transactions on Robotics, 28(1):183-194, IEEE, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Design and manufacturing of a controllable miniature flapping wing robotic platform

Arabagi, V., Hines, L., Sitti, M.

The International Journal of Robotics Research, 31(6):785-800, SAGE Publications Sage UK: London, England, 2012 (article)

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[BibTex]

[BibTex]


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Chemotactic steering of bacteria propelled microbeads

Kim, D., Liu, A., Diller, E., Sitti, M.

Biomedical microdevices, 14(6):1009-1017, Springer US, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]

2011


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Learning, planning, and control for quadruped locomotion over challenging terrain

Kalakrishnan, Mrinal, Buchli, Jonas, Pastor, Peter, Mistry, Michael, Schaal, S.

International Journal of Robotics Research, 30(2):236-258, February 2011 (article)

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[BibTex]

2011


[BibTex]


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Design and application of a wire-driven bidirectional telescopic mechanism for workspace expansion with a focus on shipbuilding tasks

Lee, D., Chang, D., Shin, Y., Son, D., Kim, T., Lee, K., Kim, J.

Advanced Robotics, 25, 2011 (article)

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[BibTex]

[BibTex]


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Bayesian robot system identification with input and output noise

Ting, J., D’Souza, A., Schaal, S.

Neural Networks, 24(1):99-108, 2011, clmc (article)

Abstract
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods

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link (url) [BibTex]

link (url) [BibTex]


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Learning variable impedance control

Buchli, J., Stulp, F., Theodorou, E., Schaal, S.

International Journal of Robotics Research, 2011, clmc (article)

Abstract
One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high degree-of-freedom (DOF) robotic tasks. In this contribution, we accomplish such variable impedance control with the reinforcement learning (RL) algorithm PISq ({f P}olicy {f I}mprovement with {f P}ath {f I}ntegrals). PISq is a model-free, sampling based learning method derived from first principles of stochastic optimal control. The PISq algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PISq is that it can scale to problems of many DOFs, so that reinforcement learning on real robotic systems becomes feasible. We sketch the PISq algorithm and its theoretical properties, and how it is applied to gain scheduling for variable impedance control. We evaluate our approach by presenting results on several simulated and real robots. We consider tasks involving accurate tracking through via-points, and manipulation tasks requiring physical contact with the environment. In these tasks, the optimal strategy requires both tuning of a reference trajectory emph{and} the impedance of the end-effector. The results show that we can use path integral based reinforcement learning not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

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link (url) [BibTex]

link (url) [BibTex]


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Iterative path integral stochastic optimal control: Theory and applications to motor control

Theodorou, E. A.

University of Southern California, University of Southern California, Los Angeles, CA, 2011 (phdthesis)

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PDF [BibTex]

PDF [BibTex]


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Learning of grasp selection based on shape-templates

Herzog, A.

Karlsruhe Institute of Technology, 2011 (mastersthesis)

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[BibTex]

[BibTex]


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Waalbot II: Adhesion recovery and improved performance of a climbing robot using fibrillar adhesives

Murphy, M. P., Kute, C., Mengüç, Y., Sitti, M.

The International Journal of Robotics Research, 30(1):118-133, SAGE Publications Sage UK: London, England, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Automated 2-D nanoparticle manipulation using atomic force microscopy

Onal, C. D., Ozcan, O., Sitti, M.

IEEE Transactions on Nanotechnology, 10(3):472-481, IEEE, 2011 (article)

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[BibTex]

[BibTex]


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Biaxial mechanical modeling of the small intestine

Bellini, C., Glass, P., Sitti, M., Di Martino, E. S.

Journal of the mechanical behavior of biomedical materials, 4(8):1727-1740, Elsevier, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Understanding haptics by evolving mechatronic systems

Loeb, G. E., Tsianos, G.A., Fishel, J.A., Wettels, N., Schaal, S.

Progress in Brain Research, 192, pages: 129, 2011 (article)

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[BibTex]

[BibTex]


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Assembly and disassembly of magnetic mobile micro-robots towards deterministic 2-D reconfigurable micro-systems

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

The International Journal of Robotics Research, 30(14):1667-1680, SAGE Publications Sage UK: London, England, 2011 (article)

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[BibTex]

[BibTex]


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Modeling of stochastic motion of bacteria propelled spherical microbeads

Arabagi, V., Behkam, B., Cheung, E., Sitti, M.

Journal of Applied Physics, 109(11):114702, AIP, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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The effect of aspect ratio on adhesion and stiffness for soft elastic fibres

Aksak, B., Hui, C., Sitti, M.

Journal of The Royal Society Interface, 8(61):1166-1175, The Royal Society, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Enhancing adhesion of biologically inspired polymer microfibers with a viscous oil coating

Cheung, E., Sitti, M.

The Journal of Adhesion, 87(6):547-557, Taylor & Francis Group, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Piezoelectric polymer fiber arrays for tactile sensing applications

Sümer, B., Aksak, B., Şsahin, K., Chuengsatiansup, K., Sitti, M.

Sensor Letters, 9(2):457-463, American Scientific Publishers, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Control methodologies for a heterogeneous group of untethered magnetic micro-robots

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

The International Journal of Robotics Research, 30(13):1553-1565, SAGE Publications, 2011 (article)

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[BibTex]

[BibTex]

2010


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Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)

Peters, J., Kober, J., Schaal, S.

Automatisierungstechnik, 58(12):688-694, 2010, clmc (article)

Abstract
Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.

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link (url) [BibTex]


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Gait planning based on kinematics for a quadruped gecko model with redundancy

Son, D., Jeon, D., Nam, W. C., Chang, D., Seo, T., Kim, J.

Robotics and Autonomous Systems, 58, 2010 (article)

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[BibTex]

[BibTex]


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Flat dry elastomer adhesives as attachment materials for climbing robots

Unver, O., Sitti, M.

IEEE transactions on robotics, 26(1):131-141, IEEE, 2010 (article)

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[BibTex]

[BibTex]


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A Bayesian approach to nonlinear parameter identification for rigid-body dynamics

Ting, J., DSouza, A., Schaal, S.

Neural Networks, 2010, clmc (article)

Abstract
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.

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link (url) [BibTex]


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A first optimal control solution for a complex, nonlinear, tendon driven neuromuscular finger model

Theodorou, E. A., Todorov, E., Valero-Cuevas, F.

Proceedings of the ASME 2010 Summer Bioengineering Conference August 30-September 2, 2010, Naples, Florida, USA, 2010, clmc (article)

Abstract
In this work we present the first constrained stochastic op- timal feedback controller applied to a fully nonlinear, tendon driven index finger model. Our model also takes into account an extensor mechanism, and muscle force-length and force-velocity properties. We show this feedback controller is robust to noise and perturbations to the dynamics, while successfully handling the nonlinearities and high dimensionality of the system. By ex- tending prior methods, we are able to approximate physiological realism by ensuring positivity of neural commands and tendon tensions at all timesthus can, for the first time, use the optimal control framework to predict biologically plausible tendon tensions for a nonlinear neuromuscular finger model. METHODS 1 Muscle Model The rigid-body triple pendulum finger model with slightly viscous joints is actuated by Hill-type muscle models. Joint torques are generated by the seven muscles of the index fin-

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PDF [BibTex]

PDF [BibTex]


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An experimental analysis of elliptical adhesive contact

Sümer, B., Onal, C. D., Aksak, B., Sitti, M.

Journal of Applied Physics, 107(11):113512, AIP, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Efficient learning and feature detection in high dimensional regression

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

Neural Computation, 22, pages: 831-886, 2010, clmc (article)

Abstract
We present a novel algorithm for efficient learning and feature selection in high- dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the Expectation- Maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This Variational Bayesian Least Squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust â??black- boxâ? approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector Machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques.

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link (url) [BibTex]

link (url) [BibTex]


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Stochastic Differential Dynamic Programming

Theodorou, E., Tassa, Y., Todorov, E.

In the proceedings of American Control Conference (ACC 2010) , 2010, clmc (article)

Abstract
We present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state- and control-dependent process noise, and proceed to derive the second-order expansion of the cost-to-go. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

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PDF [BibTex]

PDF [BibTex]


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Enhanced wet adhesion and shear of elastomeric micro-fiber arrays with mushroom tip geometry and a photopolymerized p (DMA-co-MEA) tip coating

Glass, P., Chung, H., Washburn, N. R., Sitti, M.

Langmuir, 26(22):17357-17362, American Chemical Society, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]