Intelligent Systems
Note: This research group has relocated.

34 results (View BibTeX file of all listed publications)

2024

no image
Machine learning of a density functional for anisotropic patchy particles

Simon, A., Weimar, J., Martius, G., Oettel, M.

Journal of Chemical Theory and Computation, 2024 (article)

link (url) DOI [BibTex]

2024

Simon, A., Weimar, J., Martius, G., Oettel, M. Machine learning of a density functional for anisotropic patchy particles Journal of Chemical Theory and Computation, 2024 (article)

link (url) DOI [BibTex]

2023

Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation
Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation

Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G.

Advanced Intelligent Systems, 5(8), August 2023, Inside back cover (article)

Abstract
Intelligent interaction with the physical world requires perceptual abilities beyond vision and hearing; vibrant tactile sensing is essential for autonomous robots to dexterously manipulate unfamiliar objects or safely contact humans. Therefore, robotic manipulators need high-resolution touch sensors that are compact, robust, inexpensive, and efficient. The soft vision-based haptic sensor presented herein is a miniaturized and optimized version of the previously published sensor Insight. Minsight has the size and shape of a human fingertip and uses machine learning methods to output high-resolution maps of 3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing performance, with a mean absolute force error of 0.07 N and contact location error of 0.6 mm across its surface area. Minsight's utility is shown in two robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables the robot to track the movements of a human finger based only on tactile data. Second, the informative value of the sensor output is shown by detecting whether a hard lump is embedded within a soft elastomer with an accuracy of 98%. These findings indicate that Minsight can give robots the detailed fingertip touch sensing needed for dexterous manipulation and physical human–robot interaction.

DOI Project Page [BibTex]

Predicting the Force Map of an {ERT}-Based Tactile Sensor Using Simulation and Deep Networks
Predicting the Force Map of an ERT-Based Tactile Sensor Using Simulation and Deep Networks

Lee, H., Sun, H., Park, H., Serhat, G., Javot, B., Martius, G., Kuchenbecker, K. J.

IEEE Transactions on Automation Science and Engineering, 20(1):425-439, January 2023 (article)

Abstract
Electrical resistance tomography (ERT) can be used to create large-scale soft tactile sensors that are flexible and robust. Good performance requires a fast and accurate mapping from the sensor's sequential voltage measurements to the distribution of force across its surface. However, particularly with multiple contacts, this task is challenging for both previously developed approaches: physics-based modeling and end-to-end data-driven learning. Some promising results were recently achieved using sim-to-real transfer learning, but estimating multiple contact locations and accurate contact forces remains difficult because simulations tend to be less accurate with a high number of contact locations and/or high force. This paper introduces a modular hybrid method that combines simulation data synthesized from an electromechanical finite element model with real measurements collected from a new ERT-based tactile sensor. We use about 290,000 simulated and 90,000 real measurements to train two deep neural networks: the first (Transfer-Net) captures the inevitable gap between simulation and reality, and the second (Recon-Net) reconstructs contact forces from voltage measurements. The number of contacts, contact locations, force magnitudes, and contact diameters are evaluated for a manually collected multi-contact dataset of 150 measurements. Our modular pipeline's results outperform predictions by both a physics-based model and end-to-end learning.

DOI Project Page [BibTex]

no image
Discovering causal relations and equations from data

Camps-Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer-Ballester, E., Vinuesa, R., Diaz, E., Zanna, L., Runge, J.

Physics Reports, 1044, pages: 1-68, 2023 (article)

Abstract
{Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.

DOI [BibTex]

Camps-Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer-Ballester, E., Vinuesa, R., Diaz, E., Zanna, L., Runge, J. Discovering causal relations and equations from data Physics Reports, 1044, pages: 1-68, 2023 (article)

DOI [BibTex]

no image
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

Franca, F. D., Virgolin, M., Kommenda, M., Majumder, M., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C., Hochhalter, J., Randall, D., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, J., Machado, M., Wilstrup, C., Cava, W. L.

arXiv, 2023 (article)

link (url) [BibTex]

Franca, F. D., Virgolin, M., Kommenda, M., Majumder, M., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C., Hochhalter, J., Randall, D., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, J., Machado, M., Wilstrup, C., Cava, W. L. Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition arXiv, 2023 (article)

link (url) [BibTex]

2022

Guiding the Design of Superresolution Tactile Skins with Taxel Value Isolines Theory
Guiding the Design of Superresolution Tactile Skins with Taxel Value Isolines Theory

Sun, H., Martius, G.

Science Robotics, 7(63):eabm0608, February 2022 (article)

Abstract
Tactile feedback is essential to make robots more agile and effective in unstructured environments. However, high-resolution tactile skins are not widely available; this is due to the large size of robust sensing units and because many units typically lead to fragility in wiring and to high costs. One route toward high-resolution and robust tactile skins involves the embedding of a few sensor units (taxels) into a flexible surface material and the use of signal processing to achieve sensing with superresolution accuracy. Here, we propose a theory for geometric superresolution to guide the development of tactile sensors of this kind and link it to machine learning techniques for signal processing. This theory is based on sensor isolines and allows us to compute the possible force sensitivity and accuracy in contact position and force magnitude as a spatial quantity before building a sensor. We evaluate the influence of different factors, such as elastic properties of the material, structure design, and transduction methods, using finite element simulations and by implementing real sensors. We empirically determine sensor isolines and validate the theory in two custom-built sensors with 1D and 2D measurement surfaces that use barometric units. Using machine learning methods to infer contact information, our sensors obtain an average superresolution factor of over 100 and 1200, respectively. Our theory can guide future tactile sensor designs and inform various design choices. We propose a theory using taxel value isolines to guide superresolution tactile sensor design and evaluate it empirically.

Authors copy link (url) DOI Project Page [BibTex]

A Soft Thumb-Sized Vision-Based Sensor with Accurate All-Round Force Perception
A Soft Thumb-Sized Vision-Based Sensor with Accurate All-Round Force Perception

Sun, H., Kuchenbecker, K. J., Martius, G.

Nature Machine Intelligence, 4(2):135-145, February 2022 (article)

Abstract
Vision-based haptic sensors have emerged as a promising approach to robotic touch due to affordable high-resolution cameras and successful computer-vision techniques. However, their physical design and the information they provide do not yet meet the requirements of real applications. We present a robust, soft, low-cost, vision-based, thumb-sized 3D haptic sensor named Insight: it continually provides a directional force-distribution map over its entire conical sensing surface. Constructed around an internal monocular camera, the sensor has only a single layer of elastomer over-molded on a stiff frame to guarantee sensitivity, robustness, and soft contact. Furthermore, Insight is the first system to combine photometric stereo and structured light using a collimator to detect the 3D deformation of its easily replaceable flexible outer shell. The force information is inferred by a deep neural network that maps images to the spatial distribution of 3D contact force (normal and shear). Insight has an overall spatial resolution of 0.4 mm, force magnitude accuracy around 0.03 N, and force direction accuracy around 5 degrees over a range of 0.03--2 N for numerous distinct contacts with varying contact area. The presented hardware and software design concepts can be transferred to a wide variety of robot parts.

Paper link (url) DOI Project Page [BibTex]

no image
Inferring Markovian quantum master equations of few-body observables in interacting spin chains

Carnazza, F., Carollo, F., Zietlow, D., Andergassen, S., Martius, G., Lesanovsky, I.

New Journal of Physics, 24, IOP Publishing, 2022 (article)

DOI [BibTex]

Carnazza, F., Carollo, F., Zietlow, D., Andergassen, S., Martius, G., Lesanovsky, I. Inferring Markovian quantum master equations of few-body observables in interacting spin chains New Journal of Physics, 24, IOP Publishing, 2022 (article)

DOI [BibTex]

no image
When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents

Khajehabdollahi, S., Prosi, J., Giannakakis, E., Martius, G., Levina, A.

Artificial Life, 28(4):458-478, 2022 (article)

DOI [BibTex]

Khajehabdollahi, S., Prosi, J., Giannakakis, E., Martius, G., Levina, A. When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents Artificial Life, 28(4):458-478, 2022 (article)

DOI [BibTex]

no image
Inference of affordances and active motor control in simulated agents

Scholz, F., Gumbsch, C., Otte, S., Butz, M. V.

Frontiers in Neurobiotics, 16, 2022 (article)

DOI [BibTex]

Scholz, F., Gumbsch, C., Otte, S., Butz, M. V. Inference of affordances and active motor control in simulated agents Frontiers in Neurobiotics, 16, 2022 (article)

DOI [BibTex]

Intelligent problem-solving as integrated hierarchical reinforcement learning
Intelligent problem-solving as integrated hierarchical reinforcement learning

Eppe, M., Gumbsch, C., Kerzel, M., Nguyen, P. D. H., Butz, M. V., Wermter, S.

Nature Machine Intelligence, 4(1):11-20, 2022 (article)

Abstract
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, so far, the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. We first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.

link (url) DOI [BibTex]

Eppe, M., Gumbsch, C., Kerzel, M., Nguyen, P. D. H., Butz, M. V., Wermter, S. Intelligent problem-solving as integrated hierarchical reinforcement learning Nature Machine Intelligence, 4(1):11-20, 2022 (article)

link (url) DOI [BibTex]

2021

no image
Falsification of hybrid systems with symbolic reachability analysis and trajectory splicing

Bogomolov, B. S., Frehse, G., Gurung, A., Li, D., Martius, G., Ray, R.

Nonlinear Analysis: Hybrid Systems, 42, pages: 101093, Elsevier, November 2021 (article)

link (url) DOI [BibTex]

2021

Bogomolov, B. S., Frehse, G., Gurung, A., Li, D., Martius, G., Ray, R. Falsification of hybrid systems with symbolic reachability analysis and trajectory splicing Nonlinear Analysis: Hybrid Systems, 42, pages: 101093, Elsevier, November 2021 (article)

link (url) DOI [BibTex]

How to Train Your Differentiable Filter
How to Train Your Differentiable Filter

Kloss, A., Martius, G., Bohg, J.

Autonomous Robots, 45(4):561-578, Springer, June 2021 (article)

Abstract
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

arXiv paper link (url) DOI [BibTex]

Kloss, A., Martius, G., Bohg, J. How to Train Your Differentiable Filter Autonomous Robots, 45(4):561-578, Springer, June 2021 (article)

arXiv paper link (url) DOI [BibTex]

no image
Machine learning time-local generators of open quantum dynamics

Mazza, P. P., Zietlow, D., Carollo, F., Andergassen, S., Martius, G., Lesanovsky, I.

Physical Review Research, 3(2):023084, April 2021 (article)

pdf DOI Project Page [BibTex]

Mazza, P. P., Zietlow, D., Carollo, F., Andergassen, S., Martius, G., Lesanovsky, I. Machine learning time-local generators of open quantum dynamics Physical Review Research, 3(2):023084, April 2021 (article)

pdf DOI Project Page [BibTex]

Emergent Goal-Anticipatory Gaze in Infants via Event-Predictive Learning and Inference
Emergent Goal-Anticipatory Gaze in Infants via Event-Predictive Learning and Inference

Gumbsch, C., Adam, M., Elsner, B., Butz, M. V.

Cognitive Science, 45(8), Wiley, January 2021 (article)

link (url) DOI [BibTex]

Gumbsch, C., Adam, M., Elsner, B., Butz, M. V. Emergent Goal-Anticipatory Gaze in Infants via Event-Predictive Learning and Inference Cognitive Science, 45(8), Wiley, January 2021 (article)

link (url) DOI [BibTex]

Jumping over baselines with new methods to predict activation maps from resting-state fMRI
Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Lacosse, E., Scheffler, K., Lohmann, G., Martius, G.

Scientific Reports, 11, pages: 3480, Nature Group, 2021 (article)

Abstract
Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

DOI Project Page [BibTex]

Lacosse, E., Scheffler, K., Lohmann, G., Martius, G. Jumping over baselines with new methods to predict activation maps from resting-state fMRI Scientific Reports, 11, pages: 3480, Nature Group, 2021 (article)

DOI Project Page [BibTex]

no image
A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks

Landgraf, C., Meese, B., Pabst, M., Martius, G., Huber, M. F.

Sensors, 21(6):2030, MDPI, 2021 (article)

DOI [BibTex]

Landgraf, C., Meese, B., Pabst, M., Martius, G., Huber, M. F. A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks Sensors, 21(6):2030, MDPI, 2021 (article)

DOI [BibTex]

no image
Self-tuning serverless task farming using proactive elasticity control

Kehrer, S., Zietlow, D., Scheffold, J., Blochinger, W.

Cluster Computing, 24(2):799-817, Springer, 2021 (article)

DOI [BibTex]

Kehrer, S., Zietlow, D., Scheffold, J., Blochinger, W. Self-tuning serverless task farming using proactive elasticity control Cluster Computing, 24(2):799-817, Springer, 2021 (article)

DOI [BibTex]

no image
Editorial: Complexity and Self-Organization

Gershenson, C., Polani, D., Martius, G.

Frontiers in Robotics and AI, 8, 2021 (article)

link (url) DOI [BibTex]

Gershenson, C., Polani, D., Martius, G. Editorial: Complexity and Self-Organization Frontiers in Robotics and AI, 8, 2021 (article)

link (url) DOI [BibTex]

no image
The Impact of Action Effects on Infants’ Predictive Gaze Shifts for a Non-Human Grasping Action at 7, 11, and 18 Months

Adam, M., Gumbsch, C., Butz, M. V., Elsner, B.

Frontiers in Psychology, 12, pages: 695550, 2021 (article)

link (url) DOI [BibTex]

2020

no image
Analytical classical density functionals from an equation learning network

Lin, S., Martius, G., Oettel, M.

The Journal of Chemical Physics, 152(2):021102, 2020 (article)

arXiv Supplementary Preprint_PDF DOI Project Page [BibTex]

2019

Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives

Gumbsch, C., Butz, M. V., Martius, G.

IEEE Transactions on Cognitive and Developmental Systems, 13(2):298-311, June 2019 (article)

Abstract
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of complex motor skills and the flexible adaption of behavior to new circumstances, the problem of learning meaningful, compositional abstractions from sensorimotor experiences remains an open challenge. Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch. The SUBMODES architecture bootstraps sensorimotor exploration using a self-organizing neural controller. While exploring the behavioral capabilities of its own body, the system learns modular structures that predict the sensorimotor dynamics and generate the associated behavior. In line with recent theories of event perception, the system uses unexpected prediction error signals, i.e., surprise, to detect transitions between successive behavioral primitives. We show that, when applied to two robotic systems with completely different body kinematics, the system manages to learn a variety of complex behavioral primitives. Moreover, after initial self-exploration the system can use its learned predictive models progressively more effectively for invoking model predictive planning and goal-directed control in different tasks and environments.

arXiv PDF video link (url) DOI Project Page [BibTex]

2019

Gumbsch, C., Butz, M. V., Martius, G. Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives IEEE Transactions on Cognitive and Developmental Systems, 13(2):298-311, June 2019 (article)

arXiv PDF video link (url) DOI Project Page [BibTex]

no image
Even Delta-Matroids and the Complexity of Planar Boolean CSPs

Kazda, A., Kolmogorov, V., Rolinek, M.

ACM Transactions on Algorithms, 15(2):1-33, 2019, Article No. 22 (article)

DOI [BibTex]

Kazda, A., Kolmogorov, V., Rolinek, M. Even Delta-Matroids and the Complexity of Planar Boolean CSPs ACM Transactions on Algorithms, 15(2):1-33, 2019, Article No. 22 (article)

DOI [BibTex]

Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration
Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration

Sun, H., Martius, G.

Frontiers in Neurorobotics, 13, pages: 51, 2019 (article)

Abstract
Robust haptic sensation systems are essential for obtaining dexterous robots. Currently, we have solutions for small surface areas such as fingers, but affordable and robust techniques for covering large areas of an arbitrary 3D surface are still missing. Here, we introduce a general machine learning framework to infer multi-contact haptic forces on a 3D robot’s limb surface from internal deformation measured by only a few physical sensors. The general idea of this framework is to predict first the whole surface deformation pattern from the sparsely placed sensors and then to infer number, locations and force magnitudes of unknown contact points. We show how this can be done even if training data can only be obtained for single-contact points using transfer learning at the example of a modified limb of the Poppy robot. With only 10 strain-gauge sensors we obtain a high accuracy also for multiple-contact points. The method can be applied to arbitrarily shaped surfaces and physical sensor types, as long as training data can be obtained.

link (url) DOI Project Page [BibTex]

2018

no image
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.

DOI [BibTex]

2018

Botella-Soler, V., Deny, S., Martius, G., Marre, O., Tkačik, G. Nonlinear decoding of a complex movie from the mammalian retina PLOS Computational Biology, 14(5):1-27, Public Library of Science, May 2018 (article)

DOI [BibTex]

2017

no image
Self-Organized Behavior Generation for Musculoskeletal Robots

Der, R., Martius, G.

Frontiers in Neurorobotics, 11, pages: 8, 2017 (article)

Videos link (url) DOI Project Page [BibTex]

2017

Der, R., Martius, G. Self-Organized Behavior Generation for Musculoskeletal Robots Frontiers in Neurorobotics, 11, pages: 8, 2017 (article)

Videos link (url) DOI Project Page [BibTex]

2015

no image
Novel plasticity rule can explain the development of sensorimotor intelligence

Der, R., Martius, G.

Proceedings of the National Academy of Sciences, 112(45):E6224-E6232, 2015 (article)

Abstract
Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system-specific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking, which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.

link (url) DOI Project Page [BibTex]

2015

Der, R., Martius, G. Novel plasticity rule can explain the development of sensorimotor intelligence Proceedings of the National Academy of Sciences, 112(45):E6224-E6232, 2015 (article)

link (url) DOI Project Page [BibTex]

no image
Quantifying Emergent Behavior of Autonomous Robots

Martius, G., Olbrich, E.

Entropy, 17(10):7266, 2015 (article)

link (url) DOI [BibTex]

2013

no image
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)

link (url) DOI [BibTex]

2013

Martius, G., Der, R., Ay, N. Information Driven Self-Organization of Complex Robotic Behaviors PLoS ONE, 8(5):e63400, Public Library of Science, 2013 (article)

link (url) DOI [BibTex]

no image
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.

link (url) DOI [BibTex]

no image
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.

DOI [BibTex]

Martius, G. Robustness of guided self-organization against sensorimotor disruptions Advances in Complex Systems, 16(02n03):1350001, 2013 (article)

DOI [BibTex]

2012

no image
Variants of guided self-organization for robot control

Martius, G., Herrmann, J.

Theory in Biosci., 131(3):129-137, Springer Berlin / Heidelberg, 2012 (article)

link (url) DOI [BibTex]

2012

Martius, G., Herrmann, J. Variants of guided self-organization for robot control Theory in Biosci., 131(3):129-137, Springer Berlin / Heidelberg, 2012 (article)

link (url) DOI [BibTex]

2009

no image
A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior

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

Algorithms, 2(1):398-409, 2009 (article)

Abstract
Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short timescales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer timescales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot. By interacting with the real physical system formed by the robotic hardware and the environment, the controller achieves a sensitive and body-specific actuation of the robot.

link (url) [BibTex]

2009

Hesse, F., Martius, G., Der, R., Herrmann, J. M. A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior Algorithms, 2(1):398-409, 2009 (article)

link (url) [BibTex]

2006

no image
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.

DOI [BibTex]

2006

Der, R., Hesse, F., Martius, G. Rocking Stamper and Jumping Snake from a Dynamical System Approach to Artificial Life Adaptive Behavior, 14(2):105-115, 2006 (article)

DOI [BibTex]