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2016


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On designing an active tail for legged robots: simplifying control via decoupling of control objectives

Heim, S. W., Ajallooeian, M., Eckert, P., Vespignani, M., Ijspeert, A. J.

Industrial Robot: An International Journal, 43, pages: 338-346, Emerald Group Publishing Limited, 2016 (article)

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Preprint [BibTex]

2016


Preprint [BibTex]


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Stochastic search with Poisson and deterministic resetting

Bhat, U., De Bacco, C., Redner, S.

Journal of Statistical Mechanics: Theory and Experiment, 2016(8):083401, IOP Publishing, 2016 (article)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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ATRIAS: Design and validation of a tether-free 3D-capable spring-mass bipedal robot

Hubicki, C., Grimes, J., Jones, M., Renjewski, D., Spröwitz, A., Abate, A., Hurst, J.

{The International Journal of Robotics Research}, 35(12):1497-1521, Sage Publications, Inc., Cambridge, MA, 2016 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Dynamics of beneficial epidemics

Berdahl, A., Brelsford, C., De Bacco, C., Dumas, M., Ferdinand, V., Grochow, J. A., Hébert-Dufresne, L., Kallus, Y., Kempes, C. P., Kolchinsky, A., others,

arXiv preprint arXiv:1604.02096, 2016 (article)

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Preprint [BibTex]

Preprint [BibTex]


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Rare events statistics of random walks on networks: localisation and other dynamical phase transitions

De Bacco, C., Guggiola, A., Kühn, R., Paga, P.

Journal of Physics A: Mathematical and Theoretical, 49(18):184003, IOP Publishing, 2016 (article)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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One for all?! Simultaneous examination of load-inducing factors for advancing media-related instructional research

Wirzberger, M., Beege, M., Schneider, S., Nebel, S., Rey, G. D.

Computers {\&} Education, 100, pages: 18-31, Elsevier BV, 2016 (article)

Abstract
In multimedia learning settings, limitations in learners' mental resource capacities need to be considered to avoid impairing effects on learning performance. Based on the prominent and often quoted Cognitive Load Theory, this study investigates the potential of a single experimental approach to provide simultaneous and separate measures for the postulated load-inducing factors. Applying a basal letter-learning task related to the process of working memory updating, intrinsic cognitive load (by varying task complexity), extraneous cognitive load (via inducing split-attention demands) and germane cognitive load (by varying the presence of schemata) were manipulated within a 3 × 2 × 2-factorial full repeated-measures design. The performance of a student sample (N = 96) was inspected regarding reaction times and errors in updating and recall steps. Approaching the results with linear mixed models, the effect of complexity gained substantial strength, whereas the other factors received at least partial significant support. Additionally, interactions between two or all load-inducing factors occurred. Despite various open questions, the study comprises a promising step for the empirical investigation of existing construction yards in cognitive load research.

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DOI [BibTex]

DOI [BibTex]

2014


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Roombots: A hardware perspective on 3D self-reconfiguration and locomotion with a homogeneous modular robot

Spröwitz, A., Moeckel, R., Vespignani, M., Bonardi, S., Ijspeert, A. J.

{Robotics and Autonomous Systems}, 62(7):1016-1033, Elsevier, Amsterdam, 2014 (article)

Abstract
In this work we provide hands-on experience on designing and testing a self-reconfiguring modular robotic system, Roombots (RB), to be used among others for adaptive furniture. In the long term, we envision that RB can be used to create sets of furniture, such as stools, chairs and tables that can move in their environment and that change shape and functionality during the day. In this article, we present the first, incremental results towards that long term vision. We demonstrate locomotion and reconfiguration of single and metamodule RB over 3D surfaces, in a structured environment equipped with embedded connection ports. RB assemblies can move around in non-structured environments, by using rotational or wheel-like locomotion. We show a proof of concept for transferring a Roombots metamodule (two in-series coupled RB modules) from the non-structured environment back into the structured grid, by aligning the RB metamodule in an entrapment mechanism. Finally, we analyze the remaining challenges to master the full Roombots scenario, and discuss the impact on future Roombots hardware.

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DOI [BibTex]

2014


DOI [BibTex]


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Nonequilibrium statistical mechanics of the heat bath for two Brownian particles

De Bacco, C., Baldovin, F., Orlandini, E., Sekimoto, K.

Physical review letters, 112(18):180605, APS, 2014 (article)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

Spröwitz, A. T., Ajallooeian, M., Tuleu, A., Ijspeert, A. J.

Frontiers in Computational Neuroscience, 8(27):1-13, 2014 (article)

Abstract
In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95\% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2–3 primitives) than kinematic patterns from on-ground locomotion (νm4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Shortest node-disjoint paths on random graphs

De Bacco, C., Franz, S., Saad, D., Yeung, C. H.

Journal of Statistical Mechanics: Theory and Experiment, 2014(7):P07009, IOP Publishing, 2014 (article)

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Preprint link (url) Project Page [BibTex]

Preprint link (url) Project Page [BibTex]


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Modeling of cognitive aspects of mobile interaction

Russwinkel, N., Prezenski, S., Lindner, S., Halbrügge, M., Schulz, M., Wirzberger, M.

Cognitive Processing, 15(Suppl.1), pages: S22-S24, Springer Nature, 2014 (article)

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DOI [BibTex]

DOI [BibTex]

2008


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Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Spröwitz, A., Moeckel, R., Maye, J., Ijspeert, A. J.

The International Journal of Robotics Research, 27(3-4):423-443, 2008 (article)

Abstract
This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures. We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm referred to as Powell's method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning involves running the Powell optimization algorithm in parallel with the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization include the fact that it is carried out online, the robots do not require stopping or resetting and it is fast. We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is computationally cheap, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes.

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link (url) DOI [BibTex]

2008


link (url) DOI [BibTex]