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2020


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Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Rolinek, M., Swoboda, P., Zietlow, D., Paulus, A., Musil, V., Martius, G.

In Computer Vision – ECCV 2020, Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups.

Code Arxiv [BibTex]

2020

Code Arxiv [BibTex]


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A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), 2020 (conference) Accepted

Project Page PDF [BibTex]

Project Page PDF [BibTex]


Optimizing Rank-based Metrics with Blackbox Differentiation
Optimizing Rank-based Metrics with Blackbox Differentiation

Rolinek, M., Musil, V., Paulus, A., Vlastelica, M., Michaelis, C., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 7620-7630, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020, Best paper nomination (inproceedings)

Abstract
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors.

Paper @ CVPR Long Oral Short Oral Arxiv Code Pdf Project Page [BibTex]

Paper @ CVPR Long Oral Short Oral Arxiv Code Pdf Project Page [BibTex]

2016


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Dynamical self-consistency leads to behavioral development and emergent social interactions in robots.

Der, R., Martius, G.

In Proc. IEEE Int. Conf. on Development and Learning and Epigenetic Robotics, pages: 49-56, IEEE, September 2016, in press (inproceedings)

DOI [BibTex]

2016

DOI [BibTex]


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Compliant control for soft robots: emergent behavior of a tendon driven anthropomorphic arm.

Martius, G., Hostettler, R., Knoll, A., Der, R.

In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 767-773, 2016 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Extrapolation and learning equations

Martius, G., Lampert, C. H.

2016, arXiv preprint \url{https://arxiv.org/abs/1610.02995} (misc)

Project Page [BibTex]

Project Page [BibTex]

2012


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The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots

Der, R., Martius, G.

Springer, Berlin Heidelberg, 2012 (book)

Abstract
Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self-determined, individual development in a playful and obviously embodiment-related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world.

link (url) [BibTex]

2007


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Guided Self-organisation for Autonomous Robot Development

Martius, G., Herrmann, J. M., Der, R.

In Advances in Artificial Life 9th European Conference, ECAL 2007, 4648, pages: 766-775, LNCS, Springer, 2007 (inproceedings)

[BibTex]

2007

[BibTex]

2005


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Learning to Feel the Physics of a Body

Der, R., Hesse, F., Martius, G.

In Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 , 2, pages: 252-257, Washington, DC, USA, 2005 (inproceedings)

Abstract
Despite the tremendous progress in robotic hardware and in both sensorial and computing efficiencies the performance of contemporary autonomous robots is still far below that of simple animals. This has triggered an intensive search for alternative approaches to the control of robots. The present paper exemplifies a general approach to the self-organization of behavior which has been developed and tested in various examples in recent years. We apply this approach to an underactuated snake like artifact with a complex physical behavior which is not known to the controller. Due to the weak forces available, the controller so to say has to develop a kind of feeling for the body which is seen to emerge from our approach in a natural way with meandering and rotational collective modes being observed in computer simulation experiments.

[BibTex]

2005

[BibTex]