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2018


Deep Reinforcement Learning for Event-Triggered Control
Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

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arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


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Direct Sparse Odometry With Rolling Shutter

Schubert, D., Usenko, V., Demmel, N., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation (conference)

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[BibTex]

[BibTex]


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Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry

Yang, N., Wang, R., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation, arXiv 1807.02570 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Nonlinear decoding of a complex movie from the mammalian retina

Botella-Soler, V., Deny, S., Martius, G., Marre, O., Tkačik, G.

PLOS Computational Biology, 14(5):1-27, Public Library of Science, May 2018 (article)

Abstract
Author summary Neurons in the retina transform patterns of incoming light into sequences of neural spikes. We recorded from ∼100 neurons in the rat retina while it was stimulated with a complex movie. Using machine learning regression methods, we fit decoders to reconstruct the movie shown from the retinal output. We demonstrated that retinal code can only be read out with a low error if decoders make use of correlations between successive spikes emitted by individual neurons. These correlations can be used to ignore spontaneous spiking that would, otherwise, cause even the best linear decoders to “hallucinate” nonexistent stimuli. This work represents the first high resolution single-trial full movie reconstruction and suggests a new paradigm for separating spontaneous from stimulus-driven neural activity.

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DOI [BibTex]

DOI [BibTex]


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The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

Schubert, D., Goll, T., Demmel, N., Usenko, V., Stueckler, J., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), 2018, arXiv:1804.06120 (inproceedings)

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[BibTex]

[BibTex]


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Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

Ma, L., Stueckler, J., Wu, T., Cremers, D.

arxiv, 2018, arXiv:1808.01834 (techreport)

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[BibTex]

[BibTex]


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Variational Network Quantization

Achterhold, J., Koehler, J. M., Schmeink, A., Genewein, T.

In International Conference on Learning Representations , 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Numerical Quadrature for Probabilistic Policy Search

Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.

IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras

Matsuki, H., von Stumberg, L., Usenko, V., Stueckler, J., Cremers, D.

IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robots and Systems (IROS), Robotics and Automation Letters (RA-L), IEEE, 2018 (article)

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[BibTex]

[BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


Systematic self-exploration of behaviors for robots in a dynamical systems framework
Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Light field intrinsics with a deep encoder-decoder network

Alperovich, A., Johannsen, O., Strecke, M., Goldluecke, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


Learning equations for extrapolation and control
Learning equations for extrapolation and control

Sahoo, S. S., Lampert, C. H., Martius, G.

In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

Abstract
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

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Code Arxiv Poster Slides link (url) Project Page [BibTex]

Code Arxiv Poster Slides link (url) Project Page [BibTex]


Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Sun, H., Martius, G.

Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

Abstract
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Sublabel-accurate convex relaxation with total generalized variation regularization

(DAGM Best Master's Thesis Award)

Strecke, M., Goldluecke, B.

In German Conference on Pattern Recognition (Proc. GCPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

2013


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Efficient 3D Object Perception and Grasp Planning for Mobile Manipulation in Domestic Environments

Stueckler, J., Steffens, R., Holz, D., Behnke, S.

Robotics and Autonomous Systems (RAS), 61(10):1106-1115, 2013 (article)

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link (url) DOI [BibTex]

2013


link (url) DOI [BibTex]


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NimbRo@Home: Winning Team of the RoboCup@Home Competition 2012

Stueckler, J., Badami, I., Droeschel, D., Gräve, K., Holz, D., McElhone, M., Nieuwenhuisen, M., Schreiber, M., Schwarz, M., Behnke, S.

In RoboCup 2012, Robot Soccer World Cup XVI, pages: 94-105, Springer, 2013 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the British Machine Vision Conference (BMVC), 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Mobile bin picking with an anthropomorphic service robot

Nieuwenhuisen, M., Droeschel, D., Holz, D., Stueckler, J., Berner, A., Li, J., Klein, R., Behnke, S.

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pages: 2327-2334, May 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Behavior as broken symmetry in embodied self-organizing robots

Der, R., Martius, G.

In Advances in Artificial Life, ECAL 2013, pages: 601-608, MIT Press, 2013 (incollection)

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[BibTex]

[BibTex]


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Information Driven Self-Organization of Complex Robotic Behaviors

Martius, G., Der, R., Ay, N.

PLoS ONE, 8(5):e63400, Public Library of Science, 2013 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner

Schadler, M., Stueckler, J., Behnke, S.

In Proc. of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-6, October 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Joint detection and pose tracking of multi-resolution surfel models in RGB-D

McElhone, M., Stueckler, J., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 131-137, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Distinctive 3D surface entropy features for place recognition.

Fiolka, T., Stueckler, J., Klein, D. A., Schulz, D., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 204-209, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis

Zahedi, K., Martius, G., Ay, N.

Frontiers in Psychology, 4(801), 2013 (article)

Abstract
One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost.

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link (url) DOI [BibTex]


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Combining contour and shape primitives for object detection and pose estimation of prefabricated parts

Berner, A., Li, J., Holz, D., Stueckler, J., Behnke, S., Klein, R.

In Proc. of the 20th IEEE International Conference on Image Processing (ICIP), pages: 3326-3330, sep 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Robustness of guided self-organization against sensorimotor disruptions

Martius, G.

Advances in Complex Systems, 16(02n03):1350001, 2013 (article)

Abstract
Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g.~to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

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DOI [BibTex]

DOI [BibTex]


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Hierarchical Object Discovery and Dense Modelling From Motion Cues in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), IJCAI/AAAI, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

2008


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Hierarchical Reactive Control for Humanoid Soccer Robots

Behnke, S., Stueckler, J.

International Journal of Humanoid Robots (IJHR), 5(3):375-396, 2008 (article)

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link (url) [BibTex]

2008


link (url) [BibTex]


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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In-lane Localization in Road Networks using Curbs Detected in Omnidirectional Height Images

Stueckler, J., Schulz, H., Behnke, S.

In Proceedings of Robotik 2008, 2008 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Structure from Behavior in Autonomous Agents

Martius, G., Fiedler, K., Herrmann, J.

In Proc. IEEE Intl. Conf. Intelligent Robots and Systems (IROS 2008), pages: 858 - 862, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Orthogonal wall correction for visual motion estimation

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 1-6, May 2008 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2006


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Let It Roll – Emerging Sensorimotor Coordination in a Spherical Robot

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

In Proc, Artificial Life X, pages: 192-198, Intl. Society for Artificial Life, MIT Press, August 2006 (inproceedings)

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[BibTex]

2006


[BibTex]


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From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development

Der, R., Martius, G.

In Proc. From Animals to Animats 9, SAB 2006, 4095, pages: 406-421, LNCS, Springer, 2006 (inproceedings)

Abstract
Self-organization and the phenomenon of emergence play an essential role in living systems and form a challenge to artificial life systems. This is not only because systems become more lifelike, but also since self-organization may help in reducing the design efforts in creating complex behavior systems. The present paper studies self-exploration based on a general approach to the self-organization of behavior, which has been developed and tested in various examples in recent years. This is a step towards autonomous early robot development. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realized by an internal forward model. Starting from tabula rasa initial conditions we overcome the bootstrapping problem and show emerging self-exploration. Apart from that, we analyze the effect of limited actions, which lead to deprivation of the world model. We show that our paradigm explicitly avoids this by producing purposive actions in a natural way. Examples are given using a simulated simple wheeled robot and a spherical robot driven by shifting internal masses.

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[BibTex]

[BibTex]


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See, walk, and kick: Humanoid robots start to play soccer

Behnke, S., Schreiber, M., Stueckler, J., Renner, R., Strasdat, H.

In Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 497-503, December 2006 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Rocking Stamper and Jumping Snake from a Dynamical System Approach to Artificial Life

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

Adaptive Behavior, 14(2):105-115, 2006 (article)

Abstract
Dynamical systems offer intriguing possibilities as a substrate for the generation of behavior because of their rich behavioral complexity. However this complexity together with the largely covert relation between the parameters and the behavior of the agent is also the main hindrance in the goal-oriented design of a behavior system. This paper presents a general approach to the self-regulation of dynamical systems so that the design problem is circumvented. We consider the controller (a neural net work) as the mediator for changes in the sensor values over time and define a dynamics for the parameters of the controller by maximizing the dynamical complexity of the sensorimotor loop under the condition that the consequences of the actions taken are still predictable. This very general principle is given a concrete mathematical formulation and is implemented in an extremely robust and versatile algorithm for the parameter dynamics of the controller. We consider two different applications, a mechanical device called the rocking stamper and the ODE simulations of a "snake" with five degrees of freedom. In these and many other examples studied we observed various behavior modes of high dynamical complexity.

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DOI [BibTex]

DOI [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.

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[BibTex]

2005


[BibTex]