1.
From biology to robotics and back
Manos Angelidis
Simulation Software Engineer @ The Human Brain
Project - Neurorobotics Platform

2.
Introduction
▶ Inspiration from nature has been one of the
cornerstones of scientific thought
▶ Biological processes can be observed and
translated into useful technologies
▶ What I cannot create I cannot understand -
R.Feynman
▶ Developing biotechnologies can help us
understand nature

3.
Research and technology fields
▶ Biomaterials
▶ Biotechnology - biochemistry
▶ Biorobotics
▶ Biomechanics
▶ Bio-inspired AI
▶ Neuromorphic computing

4.
A systematic approach to biologically inspired
research: BioRobotics
▶ Biorobotics lies in the intersection
between Neuroscience, Robotics and AI
▶ Basic idea is the “creation of machines
that imitate biological systems”
▶ Subcategories
• Locomotion
• Soft robotics
• Implants
• Neurorobotics
▶ Friends:
• Human - Robot interaction
• Brain - Machine interfaces
▶ End goal: Intelligent robots

5.
A body to test our hypotheses on cognition
▶ All our hypotheses on human level cognition need a
body to test their validity
▶ The brain is in constant interaction with the environment
▶ Sensorimotor integration is the basis behind intelligent
robotics
▶ Whatever we test on robots can give us insight on how it
works on the body

6.
▶ Various non-biological successful control methods
(PIDs, fuzzy-logic, neural nets)
• Path planning
• Motion planning
▶ Higher level cognitive controllers - incorporate high
level decision making
▶ The brain is the best controller
• Flexible - can learn different tasks with ease
• Memory efficient - stores only what is
important
• Low energy consumption
• Contains tons of a priori knowledge coming
from billions of years of evolution
Do as the brain does

7.
The need to simulate robots in virtual environments
▶ Impossible to obtain experimental data in many situations
▶ Impossible to perform millions of experiments in parallel
▶ Setting up complex experimental environments is costly and requires a lot of know-how

8.
First step - Sensory integration
▶ Perception of the environment is the first step in closed-loop control problems
▶ The brain processes different kinds of signals with the same “hardware”
▶ Common sensors used in closed loop intelligent robotics
•Lidars
•Cameras
•Proprioception - limb positioning
•Touch feedback
•Sounds
•Smells

9.
Second step - physically realistic simulation
▶ Simulating environments - realistic physics
• ODE
• Bullet
• Nvidia PhysX
• Dart
• Simbody
• Mujoco

10.
Third Step - AI control algorithms
▶ Neural Nets
▶ Artificial Brains
▶ High level controllers
▶ Reinforcement learning

11.
Putting it all together
▶ Realistic physical simulation of complex phenomena (fluids, rigids, soft bodies)
▶ Sensory integration
▶ Control algorithms
▶ Synchronization mechanisms
▶ Cool visualization

12.
One implementation - The Neurorobotics Platform

13.
The impact of biology study on AI
▶ Neural Networks - Brain Neurons
▶ Evolutionary algorithms - Biological species evolution
▶ Reinforcement learning - reward based systems - psychological behaviourism
▶ Cognitive robotics - Intelligent Systems
▶ Vision Systems - Visual cortex layered representations
▶ Working memory models - LSTMs

14.
Neural Networks and their relation to the brain
▶ RNNs - Recurrent neurons are observed in the brain
▶ CNNs - visual cortex layered representations
▶ Feedforward ANNs - Abstraction of Neurons

15.
Neurally Inspired Computation
▶ The Neuron as a computational unit - Spikes as
information carriers
▶ Networks of biological neurons - Spiking Neural
Networks
▶ Networks of abstract neurons (averaged firing rate)

16.
Neuromorphic hardware
▶ Intel Loihi
▶ IBM TrueNorth - currently trying other architectures
▶ SpiNNaker / BrainScale

17.
Examples of successful projects
▶ EPFL Salamandra Robotica
▶ Nengo Robot adaptive control
▶ Intel loihi object detection
▶ Teaching a robot to walk

18.
Why is it important for IT
▶ Supercomputing
▶ Hardware
▶ Algorithms
▶ Databases

19.
The future of bioinspired AI
▶ Better and specialized hardware - Intel Loihi, IBM True North, Spinnaker, companies developing
hardware
▶ Better software - Ecosystem growth
▶ Better testing environments - AI gym, pybullet etc
▶ Better algorithms
▶ Better architectures
▶ More data
▶ One-shot learning
▶ Unsupervised learning
▶ Learning to learn

20.
Conclusions
▶ Multidisciplinary research is the only viable approach for modern science
▶ Biology an be a source of unlimited inspiration
▶ AI methods and robotics can learn a lot from the brain