Jürgen Schmidhuber

University of Applied Sciences and Arts of Southern Switzerland, Canobbio, Ticino, Switzerland

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Publications (284)158.06 Total impact

  • Juxi Leitner, M. Frank, A. Förster, J. Schmidhuber
    International Conference on Informatics in Control, Automation and Robotics (ICINCO), Wien, Austria; 09/2014
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    ABSTRACT: Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model.
  • Juxi Leitner, Alexander Förster, Jürgen Schmidhuber
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    ABSTRACT: We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the ‘right’ action, i.e. the action with the best possible improvement of the detector.
    World Congress on Computational Intelligence 2014 - International Joint Conference on Neural Networks (IJCNN), Beijing, China; 07/2014
  • Juxi Leitner, M. Luciw, A. Förster, J. Schmidhuber
    International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), Montreal, Canada; 06/2014
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    Jan Koutník, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber
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    ABSTRACT: Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when the long-term memory is required. This paper introduces a simple, yet powerful modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate. Rather than making the standard RNN models more complex, CW-RNN reduces the number of RNN parameters, improves the performance significantly in the tasks tested, and speeds up the network evaluation. The network is demonstrated in preliminary experiments involving two tasks: audio signal generation and TIMIT spoken word classification, where it outperforms both RNN and LSTM networks.
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    ABSTRACT: Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.
    Frontiers in Neurorobotics 01/2014; 7:25.
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    ABSTRACT: In human relationships, responsiveness---behaving in a sensitive manner that is supportive of another person's needs---plays a major role in any interaction that involves effective communication, caregiving, and social support. Perceiving one's partner ...
    Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (Video Session); 01/2014
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    ABSTRACT: Four principal features of autonomous control systems are left both unaddressed and unaddressable by present-day engineering methodologies: 1. The ability to operate effectively in environments that are only partially known beforehand at design time;; 2. A level of generality that allows a system to re-assess and re-define the fulfillment of its mission in light of unexpected constraints or other unforeseen changes in the environment;; 3. The ability to operate effectively in environments of significant complexity;; and 4. The ability to degrade gracefully – how it can continue striving to achieve its main goals when resources become scarce, or in light of other expected or unexpected constraining factors that impede its progress. We describe new methodological and engineering principles for addressing these shortcomings, that we have used to design a machine that becomes increasingly better at behaving in underspecified circumstances, in a goal-directed way, on the job, by modeling itself and its environment as experience accumulates. Based on principles of autocatalysis, endogeny, and reflectivity, the work provides an architectural blueprint for constructing systems with high levels of operational autonomy in underspecified circumstances, starting from only a small amount of designer-specified code – a seed. Using a value- driven dynamic priority scheduling to control the parallel execution of a vast number of lines of reasoning, the system accumulates increasingly useful models of its experience, resulting in recursive self-improvement that can be autonomously sustained after the machine leaves the lab, within the boundaries imposed by its designers. A prototype system has been implemented and demonstrated to learn a complex real-world task – real-time multimodal dialogue with humans – by on-line observation. Our work presents solutions to several challenges that must be solved for achieving artificial general intelligence.
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    ABSTRACT: To plan complex motions of robots with many degrees of freedom, our novel, very flexible framework builds task-relevant roadmaps (TRMs), using a new sampling-based optimizer called Natural Gradient Inverse Kinematics (NGIK) based on natural evolution strategies (NES). To build TRMs, NGIK iteratively optimizes postures covering task-spaces expressed by arbitrary task-functions, subject to constraints expressed by arbitrary cost-functions, transparently dealing with both hard and soft constraints. TRMs are grown to maximally cover the task-space while minimizing costs. Unlike Jacobian-based methods, our algorithm does not rely on calculation of gradients, making application of the algorithm much simpler. We show how NGIK outperforms recent related sampling algorithms. A video demo (http://youtu.be/N6x2e1Zf_yg) successfully applies TRMs to an iCub humanoid robot with 41 DOF in its upper body, arms, hands, head, and eyes. To our knowledge, no similar methods exhibit such a degree of flexibility in defining movements.
    2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 11/2013
  • Dan Cireşan, Jürgen Schmidhuber
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    ABSTRACT: Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.
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    Matthew Luciw, Vincent Graziano, Mark Ring, Jürgen Schmidhuber
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    ABSTRACT: In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot’s environment. The method is using only the visual inputs and the joint encoder readings, no camera calibration and information is necessary, nor is a kinematic model. We find that these ANNs can be trained to allow spatial perception in Cartesian (3D) coordinates. These lightweight networks are providing estimates that are comparable to current state of the art approaches and can easily be used together with existing operational space controllers.
    International Joint Conference on Neural Networks (IJCNN), Dallas, USA; 08/2013
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    ABSTRACT: Resource-boundedness must be carefully considered when designing and implementing artificial general intelligence (AGI) algorithms and architectures that have to deal with the real world. But not only must resources be represented explicitly throughout its design, an agent must also take into account their usage and the associated costs during reasoning and acting. Moreover, the agent must be intrinsically motivated to become progressively better at utilizing resources. This drive then naturally leads to effectiveness, efficiency, and curiosity. We propose a practical operational framework that explicitly takes into account resource constraints: activities are organized to maximally utilize an agent's bounded resources as well as the availability of a teacher, and to drive the agent to become progressively better at utilizing its resources. We show how an existing AGI architecture called AERA can function inside this framework. In short, the capability of AERA to perform self-compilation can be used to motivate the system to not only accumulate knowledge and skills faster, but also to achieve goals using less resources, becoming progressively more effective and efficient.
    Proceedings of the 6th international conference on Artificial General Intelligence; 07/2013
  • Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, Faustino Gomez
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    ABSTRACT: The idea of using evolutionary computation to train artificial neural networks, or neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 years. However, as RL tasks become more challenging, the networks required become larger, as do their genomes. But, scaling NE to large nets (i.e. tens of thousands of weights) is infeasible using direct encodings that map genes one-to-one to network components. In this paper, we scale-up our compressed network encoding where network weight matrices are represented indirectly as a set of Fourier-type coefficients, to tasks that require very-large networks due to the high-dimensionality of their input space. The approach is demonstrated successfully on two reinforcement learning tasks in which the control networks receive visual input: (1) a vision-based version of the octopus control task requiring networks with over 3 thousand weights, and (2) a version of the TORCS driving game where networks with over 1 million weights are evolved to drive a car around a track using video images from the driver's perspective.
    Proceedings of the 15th annual conference on Genetic and evolutionary computation; 07/2013
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    ABSTRACT: Building artificial agents and robots that can act in an intelligent way is one of the main research goals in artificial intelligence and robotics. Yet it is still hard to integrate functional cognitive processes into these systems. We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture is introduced to facilitate visual perception and cognitive robotics research. It aims to mimic processes in the human brain performing visual cognition tasks. A number of experiments with this icVision framework are described. We showcase both detection and identification in the image plane (2D), using machine learning. In addition we show how a biologically inspired attention mechanism allows for fully autonomous learning of visual object representations. Furthermore localising the detected objects in 3D space is presented, which in turn can be used to create a model of the environment.
    Biologically Inspired Cognitive Architectures. 07/2013; 5:29–41.
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    ABSTRACT: Robust object manipulation is still a hard problem in robotics, even more so in high degree-of-freedom (DOF) humanoid robots. To improve performance a closer integration of visual and motor systems is needed. We herein present a novel method for a robot to learn robust detection of its own hands and fingers enabling sensorimotor coordination. It does so solely using its own camera images and does not require any external systems or markers. Our system based on Cartesian Genetic Programming (CGP) allows to evolve programs to perform this image segmentation task in real-time on the real hardware. We show results for a Nao and an iCub humanoid each detecting its own hands and fingers.
    IEEE Congress on Evolutionary Computing (CEC), Cancun, Mexico; 06/2013
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    ABSTRACT: Do two data samples come from different distributions? Recent studies of this fundamental problem focused on embedding probability distributions into sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to compare distributions by the distance between their embeddings. We show that Regularized Maximum Mean Discrepancy (RMMD), our novel measure for kernel-based hypothesis testing, yields substantial improvements even when sample sizes are small, and excels at hypothesis tests involving multiple comparisons with power control. We derive asymptotic distributions under the null and alternative hypotheses, and assess power control. Outstanding results are obtained on: challenging EEG data, MNIST, the Berkley Covertype, and the Flare-Solar dataset.
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    Simon Harding, J. Leitner, Jürgen Schmidhuber
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    ABSTRACT: Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.
    03/2013: pages 31-44;
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    ABSTRACT: Like a scientist or a playing child, PowerPlay (Schmidhuber, 2011) not only learns new skills to solve given problems, but also invents new interesting problems by itself. By design, it continually comes up with the fastest to find, initially novel, but eventually solvable tasks. It also continually simplifies or compresses or speeds up solutions to previous tasks. Here we describe first experiments with PowerPlay. A self-delimiting recurrent neural network SLIM RNN (Schmidhuber, 2012) is used as a general computational problem solving architecture. Its connection weights can encode arbitrary, self-delimiting, halting or non-halting programs affecting both environment (through effectors) and internal states encoding abstractions of event sequences. Our PowerPlay-driven SLIM RNN learns to become an increasingly general solver of self-invented problems, continually adding new problem solving procedures to its growing skill repertoire. Extending a recent conference paper (Srivastava, Steunebrink, Stollenga, & Schmidhuber, 2012), we identify interesting, emerging, developmental stages of our open-ended system. We also show how it automatically self-modularizes, frequently re-using code for previously invented skills, always trying to invent novel tasks that can be quickly validated because they do not require too many weight changes affecting too many previous tasks.
    Neural networks: the official journal of the International Neural Network Society 02/2013; · 1.88 Impact Factor

Publication Stats

4k Citations
158.06 Total Impact Points


  • 2013
    • University of Applied Sciences and Arts of Southern Switzerland
      Canobbio, Ticino, Switzerland
  • 1970–2013
    • University of Lugano
      Lugano, Ticino, Switzerland
  • 1999–2012
    • Istituto Dalle Molle di Studi sull'Intelligenza Artificiale
      Lugano, Ticino, Switzerland
  • 1991–2010
    • Technische Universität München
      München, Bavaria, Germany
  • 2009
    • IT University of Copenhagen
      København, Capital Region, Denmark
  • 2006
    • University of Amsterdam
      • Institute of Informatics
      Amsterdam, North Holland, Netherlands
  • 2004
    • California Institute of Technology
      Pasadena, California, United States
  • 2003
    • University of Alicante
      Alicante, Valencia, Spain
  • 1991–1993
    • University of Colorado at Boulder
      • Department of Computer Science (CS)
      Boulder, Colorado, United States