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  • 14 January 2011

    Challenge the future

    Delft University of Technology

    Intelligent Control and Robotics

    Jens Kober

    Delft Center for Systems and Control Cognitive Robotics

    Delft University of Technology The Netherlands

    j.kober@tudelft.nl www.jenskober.de

    Learning and Autonomous Control

  • Cognitive Robotics

    Robot Dynamics (dynamic motion control, motor control)

    Intelligent Vehicles (perception and modeling, dynamics,

    human factors)

    Human-Robot Interaction (physical human-robot interaction)

    Learning and Autonomous Control (intelligent control,

    cognition)

  • Robot Dynamics

  • Intelligent Vehicles

  • Human-Robot Interaction

  • Robert Babuska

    professor

    learning and adaptive control

    Jens Kober

    assistant professor

    robotics, learning motor skills

    Javier Alonso Mora

    assistant professor

    robotics, motion planning

    Learning and Autonomous Control

  • Systems and control

    model-based design

    Artificial intelligence, machine learning

    learn a task from experience, without being explicitly programmed for that task

    Intelligent control

    Intelligent Control

  • Potential of Machine Learning

    adapt to changes in environment

    find new, better solutions

    teach by demonstration or imitation

    in addition: modeling is tedious, time consuming, expensive

  • Nature-Inspired Models

    y(k+1) = A(q)y(k) + B(q)u(k)

    Traditional control Inspiration in nature

    if then artificial neural networks, deep learning

    rule-based models

  • Evolutionary Optimization

    xopt = argmax F(x) x

    Traditional optimization Inspiration in nature

    Population

    011000100100101011101100101011

    Fitness Function

    Best individuals

    CrossoverMutation

    Genetic and evolutionary algorithms

  • Swarm Intelligence Inspiration in nature

    Ant algorithms, particle swarm optimization

  • Growing Interest in Robotics

  • 14 January 2011

    Challenge the future

    Delft University of Technology

    Uncertainty due to environment

    Robustness

    Compliance Learning from mistakes

    Safety

    Autonomy

    Power efficiency

    Situation awareness

    Challenges

    Interaction with humans

  • Future Robots

    Need to operate under unforeseen circumstances

    Interact with humans in an intelligent way

    Learn from mistakes, improve over time (performance,

    energy)

    Learning and adaptation is essential!

    Impossible to preprogram all tasks or behaviors

    Economic point of view: short design time and low costs

  • 14 January 2011

    Challenge the future

    Delft University of Technology

    Adaptive and learning control systems

    Reinforcement learning, optimal control

    Nonlinear adaptive control

    Deep learning for robotics

    Data-effective learning algorithms

    Sensor fusion

    Supervisory and distributed control design

    Motion planning, coordination

    Multi-agent systems

    Main Research Topics

    Emphasis on novel, generic, widely applicable techniques

    Realistic / real-world case studies and applications

  • Unsupervised learning

    (without teacher)

    Spectrum of Learning Techniques

    Reinforcement learning

    (learn directly to control)

    Supervised learning

    (with teacher)

  • Construct a Robot Model from Data

    actuate motors and observe the response

    Two ways to model the system:

    1. Physical modeling

    2. System identification

    Maarten Vaandrager

  • Unsupervised learning

    (without teacher)

    Spectrum of Learning Techniques

    Reinforcement learning

    (learn directly to control)

    Supervised learning

    (with teacher)

  • Unsupervised Learning - Clustering

    Discover automatically groups and structures in data

    Applications: Robot perception, vision

    Data-driven construction of

    dynamic models

  • Construction of Nonlinear Models

    Nonlinear and uncertain behavior

    Experimental data

  • Unsupervised learning

    (without teacher)

    Spectrum of Learning Techniques

    Reinforcement learning

    (learn directly to control)

    Supervised learning

    (with teacher)

  • Reinforcement Learning

    Goal:

    Adapt the control strategy so that the sum of rewards over time is maximal.

    Goals Performance evaluation

    reward

    state

    Adaptive

    controller System

    action

    Inspiration - animal learning (reward desired behavior)

  • Approximation in high-dimensional continuous spaces

    Computationally effective methods

    Constrained learning and convergence

    Reinforcement Learning for Control

  • Learning in Real World, in Real Time

  • E. Schuitema (collaboration with the Biorobotics Lab, TU Delft - M. Wisse and P. Jonker).

  • Examples of Recent MSc Projects

  • Effective Real-Time Reinforcement Learning

  • Learning for Disturbance Compensation

    C Model based controller

    F Learning disturbance compensator

  • RL-based Compensation

  • New MSc Project Vacancies

  • Hip Surgery Robot Design

    3D modeling of bone based on X-ray images

    Design of a specialized two-degree-of-freedom robot

    Robot surgeon interaction

  • Reinforcement Learning for Walking Robot

    Movement primitives and RL

    Dimensionality reduction

  • Robotic Platforms

  • Collaboration with AE

  • Relevant Elective Courses

    SC42020 Modern Robotics

    ME41025 Robotics Practicals

    SC42090 Robot Motion Planning and Control

    SC42050 Knowledge Based Control Systems

    IN4320 Machine Learning

    IN4085 Pattern Recognition

    CS4180 Deep Learning

    IN4010 Artificial Intelligence Techniques

  • Questions?

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