3rd HBP Education Workshop Future Developments in Neurorobotic Technology

Prof. Dr.-Ing. habil. Alois Knoll

Technical University of Munich Department of Informatics Robotics and Embedded Systems http://neurorobotics.net/ Outline

• Introduction to Neurorobotics • Current Research in Neurorobotics • The Neurorobotics Subproject of the Human Brain Project • The Neurorobotics Platform • The Future of Neurorobotic Technology • Get Involved! Introduction to Neurorobotics Definition & History What is Neurorobotics?

Neurorobotics is an emerging interdisciplinary field of research at the intersection of state-of-the art research in robotics and in-silico neuroscience. It is about • building robot bodies with an embedded brain and embedded control systems by • mimicking the structure and function of the nervous systems of creatures. Basis Development of biologically accurate models of brains and bodies. Approach Let these robots live in both real and high-fidelity virtual environments, and observe their (developing/growing) skills to reverse-engineer the brain.

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 4 Why Link Brains to Robots?

• Algorithms are embedded in hardware • Sensors and effectors operate in real-time • 850,000 neurons The brain is massively parallel, but does not 10,000 neurons suffer from the problems of parallel computing: dead-locks, non-determinism, race-conditions, …

1,000,000 neurons • … and decomposition into parallel tasks is self-organized/evolved • 75,000,000 neurons 200,000,000 neurons Architecture is scalable from thousands to

Homo sapiens billions of “processors” • Performance is robust – with graceful degradation • Brains are extremely power and space efficient

~85,000,000,000 neurons, 1015 synapses (“peta-flop computer on 20 Watt”) • Calls for a “neurorobotics approach”

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 5 Goals of Neurorobotic Research

Robotics Robotics Neuroscience Learning • Apply findings • Use robots as a • Leverage robotic from neuroscience tool for testing embodiment to hypotheses study and develop • Overcome the neurobiological limitations of • Full observability models of learning standard control of brain models architectures during interaction • Endow virtual with the realistic • Use neuromorphic brains with the environments hardware for robot desired behavior control tasks

Provide an interdisciplinary experimental link

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 6 A Brief History of Neurorobotics 1950 William Grey Walter’s Tortoises Simple mobile robotic platforms with a “nervous system” of vacuum tubes capable of phototaxis and classical conditioning

1988 Feedback-Error-Learning Neural Network by Miyamoto et al. http://www.nsi.edu/ A hierarchical neural network model with brain-like ~nomad/darwinvii.html architecture and heterosynaptic plasticity is used to control the trajectory of a robotic manipulator

1990s Synthetic Neural Modeling and Brain-Based Devices by Krichmar, Edelman et al. Development of the Darwin series of robots (both virtual and physical) to study the neural bases of adaptive behavior 3rd HBP Education Workshop Future Developments in Neurorobotic Technology 7 The Early Beginnings of (Neuro-)Robotics: William Grey Walter’s Tortoises

Source: https://www.youtube.com/watch?v=lLULRlmXkKo

3rd HBP Education Workshop A Machine that Learns, pp60 – 63, W. Grey8 Walter doi:10.1038/scientificamerican0851-60 A Brief History of Neurorobotics

2003 Study on Active Vision by Suzuki, Floreano et al. A neurorobotics study demonstrates the importance of closed action-perception loops for the development of the visual system and thereby highlights how neurorobotics can advance machine learning

Today Large-Scale Brain Simulation, Neuromorphic Hardware and Biomimetic Robotics The tools developed within the Human Brain Project and advances in biomimetic robotics are opening up a new era of neurorobotic research with highly realistic brain simulations and body morphologies

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 9 Neurorobotics is an Emerging Field of Research!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 10 Current Research in Neurorobotics An Overview Neuromorphic Sensors and Computation for Robot Control

A Mobile Robot Platform with SpiNNaker and Silicon Retinas • On-board SpiNNaker system (48 chips, 864 cores, simulates up to 250000 neurons for less than 40 Watt) • Two silicon retinas (eDVS sensors) for spike-based neuromorphic stereo vision • Programmable via C, PyNN and Nengo Applications • Trajectory stabilization via optical flow • Stimulus tracking • Learning by demonstration

Source: Conradt, Galluppi, Stewart TUM (2014)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 12 Locomotion with Central Pattern Generators

Animal Locomotion • Animals are able to move seamlessly in their specific environments • Basic motion primitives are implemented by Central Pattern Generators (CPGs, oscillatory neural circuits in the spinal Source: https://www.youtube.com/watch?v=01j9SyNhavI cords of vertebrates) • High-level control through top- Animals can even down modulation from the live and move brain without a cortex

→ Animal locomotion control as Source: an inspiration for robotics https://en.wikipedia.org/wiki/Mi ke_the_Headless_Chicken Source: Ijspeert (2008)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 13 Locomotion with Central Pattern Generators

Case Study: A Salamander Robot • Modular biomimetic robot with four legs and an actuated spine • The movements for locomotion are generated by a CPG model which is implemented as a network of coupled nonlinear oscillators • The robot is capable of both salamander-like walking and swimming • Tool for both robotics and neuroscience

Source: Ijspeert et al. (2007, 2013, 2015)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 14 A Salamander Robot Driven by CPGs (Ijspeert et al.)

Source: http://biorob2.epfl.ch/utils/movieplayer.php?id=256

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 15 Neuromuscular Modeling and Simulation

• A detailed musculoskeletal model of the human body enables highly realistic embodiment for brain simulations • Muscles, tendons and ligaments are modeled as wires which are attached to the bones • The resulting mechanical model has 155 degrees of freedom (most industrial robots have only 6 degrees of freedom!) • Adding a neural simulation of the nervous system yields a neuromuscular model

Source: Nakamura et al. (2005, 2006)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 16 Example: Neuromuscular Simulation of the Biceps Stretch Reflex

• Simulation with pools of Integrate-and- Fire Neurons is connected to a realistic musculoskeletal simulation • Neural architecture based on the spine  Two motor neurons pools control biceps and triceps  Two sensory neuron pools encode velocities of biceps and triceps  One pool of interneurons for inhibitory disynaptic connections • Neural and musculoskeletal parameters are derived from experimental results

Source: Sreenivasa, Murai, Nakamura (2013)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 17 Cognitive Developmental Robotics

“Cognitive developmental robotics (CDR) aims to provide new understanding of how human’s higher cognitive functions develop by means of a synthetic approach that developmentally constructs cognitive functions. The core idea of CDR is “physical embodiment” that enables information structuring through interaction with the environment, including other agents.” Asada et al., 2009

• Interdisciplinary field of research including artificial intelligence, robotics, neuroscience, cognitive science, developmental psychology, … • Assumption of distinct phases for individual and social development • Only concerned with ontogenetic timescales (= single individual) and not with phylogenetic timescales (= evolution of a species)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 18 Example: Simulation of Early Fetal Development

Fetus Simulation with Spiking The analysis of simulation results Neural Networks suggests the dependence of body map development on • the environment • the nervous system • fetal movements

Simulation of a human fetus in a realistic environment with a nervous system consisting of spiking neurons.

Source: Yamada, Fujii & Kuniyoshi (2013)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 19 A Robot Baby (Kuniyoshi et al.)

Source: https://www.youtube.com/watch?v=dMCAQXyKcSc

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 20 The Neurorobotics Subproject of the Human Brain Project (SP10) Shaping the Future of Neurorobotics Our Motivation

Mankind has long dreamed of autonomous robots which possess a wide variety of skills, including … • Perception, “Out-of-the box”- navigation and recognition of arbitrary environments • Attending to, aiding and safe working with others • Goal-oriented behavior, learning, decision making • Sense of self, Consciousness

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 22 Our Motivation

But these robot should also have even more properties, like … • fault tolerance • low energy consumption • lightweight, cheap, compliant mechanics, compact design, ...

The ultimate dream/goal is to build robots with superhuman flexibility and adaptivity.

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 23 The Neurorobotics Platform within the Human Brain Project

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 24 Our Motivation

How can we achieve these goals? • … for complex manipulation tasks, soft bio-morphic robots may be a suitable solution • … however, bio-morphic bodies, like ECCEROBOT, are only in the very early stages of development • … and to make such soft bodies useful, we need to endow them with intelligent perception, cognition and actuation, possibly derived from the brain ECCEROBOT 2009 Source: http://www.youtube.com/watch?v=cI9H4FoA0b4

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 25 Our Mission

Contribution to Theory Contribution to Practice “Hilbert Program towards the “Modular Brains for Modular Ultimate (Cognitive) Robot” Bodies”

• Ground robot cognition and • Design an appropriate toolchain control in neural dynamics for connecting brains to robots • Advance machine learning • Implement the required through large-scale neural technology simulation and robotic embodiment

Define a New State of the Art in Robotics and Neuroscience!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 26 The Neurorobotics Workflow

1 2 3 4 Choose desired system Choose desired level Instrumentation Execution & platform & alignment Run the experiment

Detailed Brain Model • Detailed neurons • Point neurons • Abstract neurons

SpinLink port connection SpinLink ports • Determine signals to spiNNaker • Interact with users CPLD 2 of 7 asynchronous Ack 7 Data • Sensors level-change protocol{ 7 Data Ack Interface board • Coordinate all models CPLD Microcontroller • Actuators SpiNNaker chips • Brain Model Ack 9 Data 9 bit parallel Peripheral ports protocol { 9 Data Ack • Motors • Robot Model uC • Determine regions • Environment

...... • Sensory eDVS Robot eDVS Ethernet port • Coordinate simulators • Motor • HPC simulation • Re-instantiate Body/Environment • Analog neuromorphic representations for system • SpiNNaker sensors and motors

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 27 Example: Forerunner A Simplistic Complete Neurorobot

• Classic Task: balance a small ball in two dimensions Dynamic Vision Sensor • Use two antagonistic drive pairs • Perception by AER camera (silicon retina) Ball • Control by SpiNNaker SpiNNaker- based on simplified Based Actuators (2D) BBP column Controller

Jörg Conradt, TUM

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 28 Forerunner Simplifying the BBP Column

Blue Brain Column cell positions (colored by layer)

Blue Brain Column with all morphologies; The BBP Column with colored by cell type point neurons; this model runs on a PC or very fast on a super-computer (NEST simulation)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 29 Forerunner Simulation of a Point-Neuron Column

Spiking Point Neuron Model • Disregard neuronal morphologies: shrink to points • Retain connectivity graph (pathways) with synapse parameters • Fit point neuron models to match the dynamics of the different neuron types SpiNNaker-Implementation • Further simplification to volumetric mean-field models • Determine rate transfer functions from the point neuron model

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 30 Forerunner Sample Run of the Ball Balancer

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 31 The Neurorobotics Platform Providing the Tools for Future Robotics and Neuroscience Vision

“The Neurorobotics Platform will provide HBP brain models with a body, designing closed loop systems in which brain models are connected to simulated robots operating in a simulated physical environment. The Platform will make it possible to train brain models to perform specific functions and to replicate classical animal and human experiments.”

→ Open and agile development process based on SCRUM → State-of-the-art web development and open source software

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 33 The Neurorobotics Platform – Features

User Interface • Web-based application with live visualization of the simulation – runs in the browser! • Life-size visualization and intuitive interaction on display walls Closed Loop Engine • Coordination of the robot simulation and the brain simulation • Mapping of data via transfer functions Designers and Catalogues Design, store and access robots, environments, and experiments

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 34 The Neurorobotics Platform – Components

Environment Designer Robot Designer Brain Interfaces & Experiment Designer

Body Integrator Design

Closed Loop Engine World Simulation Engine Infrastructure Run

High-Fidelity Experiment Web-based Experiment

Simulation Viewer Simulation Viewer Visualize

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 35 Virtual Bodies – Overview

HBP Mouse

Roboy iCub LAURON V

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 36 Virtual Bodies – The HBP Mouse

200.000 neuron mouse brain model

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 37 Passive Body Simulation

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 38 Mapping Touch to the Sensory Cortex

1 Schema of the mouse somato- 2 Mapping to S1 region of brain model sensory map from literature

Hind limbs Trunk

Forelimbs

Whiskers

Mouth Nose 3 Automatic creation of touch receptors Sensory cortex limits cortical maps and drives on skin and top-down plasticity in thalamocortical circuits Zembrzycki et al. 2013 whiskers

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 39 Mapping Touch to the Sensory Cortex – Demo

Simulated activation of S1 with Mouse body tactile stimulation

Trunk

Hind limbs Forelimbs whiskers mouth nose

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 40 The HBP Neurorobotics Platform – Connecting Brains to Robots

• The robot simulation and the brain simulation are running in different simulators (GAZEBO + NEST) • The different simulations are coordinated by a closed loop engine • Transfer functions translate between neural spikes from the NEST simulation and the sensors and actuators of the robot • Transfer functions are specified in a Python-based domain-specific language • Spike trains and robot state variables Transfer function editor of the HBP are visualized in real-time Neurorobotics Platform

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 41 The Closed Loop Engine – Overview

Cognition BRAIN Transfer Functions

Perception Action

BODY

Closed Loop Engine Loop Closed ENV

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 42 The Closed Loop Engine – Dataflow

Camera Images, Network Stimuli Transfer Functions Sensors

Neuronal Closed World Simulator Loop Simulator (Nest) Engine (Gazebo)

Transfer Membrane Functions Control Potentials Messages

Source: Hinkel et al. (2015)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 43 A Simple Example – The Braitenberg Vehicle

EyeSensorTransmit

WheelSensorTransmi t [Brai86] [Image: Clearpath robotics]

Adapted from: Hinkel et al. (2015)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 44 The Braitenberg Vehicle on the Neurorobotics Platform – Demo

Spike trains

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 45 The HBP Neurorobotics Platform – Overview

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 46 The Future of Neurorobotic Technology Neurorobotics Research in the Human Brain Project Biomimetic Robots

Roboy (2012 – )

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 48 Biomimetic Musculoskeletal Robots – Motivation

Embodiment • The morphology of the body shapes the interaction with the environment • Intelligent body design eases the control task through implicit “computation” by the body (morphological computation) • Highly realistic input and output data for brain simulations Advanced Performance • Artificial muscle allow for human-like movements (variable stiffness, elasticity) • Inherently safe operation

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 49

Mimicking the Human Body – Eccerobot

Alan Alan Diamond S. Wittmeier et al. Toward Anthropomimetic Robotics: Development, Simulation, and Control of a Musculoskeletal Torso. Artificial Life 19: 171 – 193 (2013)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 50 Mimicking the Human Body – Anthrob

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 51 Mimicking the Human Body – Roboy

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 52 Mimicking Biological Muscles – Myorobotics

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 53 Myorobotics

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 54 Intermediate Summary

• Biomimetic musculoskeletal robots are essential for meaningful interaction between brain simulations and the physical environment • Analogously to biological bodies, appropriate robot designs can ease the control task through embodiment • Special features like variable stiffness, inherent compliance or elastic energy storage make biomimetic robots also highly interesting for other fields in robotics However • Classical control algorithms are not suited for controlling these robots • Simulated brains cannot be programmed like standard computers  Appropriate learning techniques are required!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 55 State in Machine Learning: Deep Learning Deep Neural Networks • Large artificial neural networks with analog neurons (mostly feedforward architectures with many hidden layers, multi layer perceptrons, convolutional networks) • Learning is mostly based on well-known algorithms from classical artificial neural networks research (e.g. backpropagation) • Huge networks sizes and parameter spaces require massive compute power and large databases for training • Mostly applied in the fields of image classification and natural language processing • Differently from previous approaches in machine learning (e.g. SVMs) features of the training data are not engineered manually but autonomously learned by the network 3rd HBP Education Workshop Future Developments in Neurorobotic Technology 56 Deep Learning vs. Brain

Deep neural networks are assumed to process information similarly to the brain. However, there are still some important aspects missing: • Most deep neural networks are simple feedforward networks with unidirectional bottom-up processing logic. The brain contains lots of top-down and lateral connections. • Deep neural networks are mostly trained offline on large datasets with supervised learning. The brain continuously learns online using unsupervised learning, supervised learning and reinforcement learning at the same time. • Deep neural networks learn from artificial datasets. The brain learns through multimodal real-time interaction with the environment through a physical body.

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 57 The Contribution of Neurorobotics to Future Machine Learning

Neurorobotics connects physical bodies to highly realistic simulations of biological brains. It extends current deep learning approaches in several ways: • The use of biologically more realistic neural network models (e.g. spiking neural networks) with complex connectivity yields rich neural dynamics which can perform meaningful computation. • Closed-loop interaction through physical bodies in real-time naturally structures sensory data. • The embodiment enabled by appropriate physical bodies can ease the learning task by outsourcing computation to the physical structure of the robot. Neurorobotics has a huge potential to play a key rule in future machine learning!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 58 Example 1: Cerebellar Control of a Biomimetic Actuator

Motivation • Humans and animals have developed remarkable motor skills and can move and interact seamlessly in arbitrary environments • Neurorobotics technology delivers the tools to to investigate the underlying control principles and to apply them for robot control • Integration of neuroscientific modelling, neuromorphic hardware, neural learning and biomimetic antagonist actuation in a single neurorobotic system • Benefit from energy efficient operation of neuromorphic hardware in robot control

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 59 Example 1: Cerebellar Control of a Biomimetic Actuator

Approach Implement a spiking neuron model of the cerebellum on SpiNNaker in order to control a single Myorobotics joint ( proof of concept) Hardware Setup • The cerebellum model is running on a single SpiNNaker core (250,000 spiking neurons, 1 W energy consumption) • A dedicated SpiNN-IO hardware interface connects the neural simulation to a single- joint Myorobotics arm • Sensor readings are converted to spike trains and output spike rates are converted to to motor output Source: Denk et al. (2013), TUM NST 3rd HBP Education Workshop Future Developments in Neurorobotic Technology 60 Example 1: The Cerebellar Model

Granule Cells: Mossy Fibers: sparse, high-dim. state sensor data, representation Purkinje Cells: motor plan sample + weight parallel fibers (GrC) Deep Cerebellar Nuclei: Integrate excitatory (MoF) + inhibitory (PuC) input Learning Rule: relates action (GrC) to penalty signal (InO)

Supported by SP11 (Ros Lab, UGr) Inferior Olive: teaching signal on climbing fibers to shape GrC-PuC synapses

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 61 Example 1: Results

Naive Network Entrained Network

Source: TUM NST

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 62 Example 1: Cerebellar Control of a Biomimetic Actuator

Results The cerebellar model is able to learn to make the robot follow the desired trajectory → Demonstration of a complete closed-loop neurorobotic system with neuromorphic control and biomimetic actuation → Starting point for more complex setups with many joints and advanced control tasks

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 63 Example 1: Demo

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 64 Example 2: Differential Extrinsic Plasticity

Motivation • It is still unclear how specific behavior is acquired autonomously during phylogenetic and ontogenetic development processes (e.g. how do infants learn to crawl?) • Classical learning rules like Hebb’s law rely on external input to drive the learning process • Existing learning rules do not incorporate any notion of morphology or interaction with the environment Source: https://www.youtube.com/watch?v=NGh2YwZWd38

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 65 Example 2: Differential Extrinsic Plasticity

Approach • The Differential Extrinsic Plasticity (DEP) learning rule relates behavior directly to changes on the synaptic level • A simple analog neural network directly transforms sensory input to motor commands while the weights are adapted with DEP • Synaptic weight updates are driven by extrinsic environmental changes which are not captured by the internal body model (“chaining together what changes together”) • By directly incorporating body dynamics as a driving factor in the learning rule, complexity is “outsourced” to the physical structure of the robot via embodiment  The actual learning rule becomes very simple!

Source: Der, Martius (2015)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 66 Example 2: Differential Extrinsic Plasticity

Neural Network Model

Source: Der, Martius (2015)

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 67 Example 2: Differential Extrinsic Plasticity

Simulation Results A humanoid robot falls on the Through body-environment ground. The contact with the coupling, each robot gets a notion environment triggers the DEP rule of what the other is doing, which which finally yields a crawling enables the emergence of movement pattern without any synchronized motion patterns. other external input.

Source: http://playfulmachines.com/

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 68 Example 2: Differential Extrinsic Plasticity

Results with a Myorobotics Arm DEP enables learning of specific Latent velocity correlations are behaviors through external amplified and yield stable behavior. influence like handshaking.

Source: http://playfulmachines.com/

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 69 Future Work

Our Goal Extend, scale and integrate the existing approaches across hierarchy and time. Incorporating further Levels of Hierarchy • Neural modeling of the spinal cord for low-level control tasks (reflexes, central pattern generators. • Use low-level control mechanisms in cortical models to learn high-level behavior in closed action-perception loops ( enable deep learning in complex recurrent networks through neurorobotics!). Scaling across Time Master complexity by simulating ontogenetic neural development ( developmental robotics) based on approaches like DEP.

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 70 Future Work

Build an Integrated Neurorobotic System Implement the different levels of hierarchy and the different scales of time in a single neurorobotic system. → Solve real-world tasks with neurorobotic technology

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 71 Get Involved! Community Building, Events and Activities Our Journal: frontiers in Neurorobotics

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 73 Our Journal: frontiers in Neurorobotics

• The only journal which is dedicated exclusively to neurorobotics • High-quality peer-reviewed articles • Strategic alliance with Nature Publishing Group • Open Access http://www.frontiersin.org/neurorobotics

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 74 Linking to Top Japanese Researchers and Universities

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 75 Organized Workshops & Symposia

Symposium at EAPCogSci 2015, Turino Brain-Supported Learning Algorithms for Robots

Workshop at IROS 2015, Hamburg Advances in Biologically Inspired Brain-Like Cognition and Control for Learning Robots

Student Event at Technical University of Munich Symposium Neurorobotics

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 76 HBP Neurorobotics Performance Shows

• Quarterly meeting of the HBP Neurorobotics team • Changing locations (Munich, Geneva, Pisa, Karlsruhe…) • Presentation and discussion of the latest achievements • Setting goals for future research and development • Talks by invited experts and collaborators

Interested students and researchers are cordially invited to join!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 77 Outlook and Future Work

• HBP-NR will explore the possibilities of reverse-engineering the brain by building “closing-the-loop-robots” – but at the same time, HBP will provide new low-power, highly flexible controllers for soft robots implementing insights about brains • There will be a complete platform and toolchain in order to make it simple even for non-roboticists to build virtual and/or real modular robots: modular brains for modular bodies • HBP / NR is an open project and strives to build an open community – whoever wants to contribute is very welcome!

3rd HBP Education Workshop Future Developments in Neurorobotic Technology 78 Thank your for your attention! For more information and exciting opportunities to participate visit us on www.neurorobotics.net and follow us on Twitter! @HBPNeurorobotic Contact

Prof. Dr. habil. Alois Knoll

fortiss GmbH – An-Institut der Technischen Universität München and Technische Universität München Boltzmannstraße 3| 85748 Garching | Germany Tel. +49 (0)89 289-18106 [email protected] | wwwknoll.in.tum.de