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From biology to robotics and back by Manos Angelidis


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What makes our brains so efficient in computing and adapting to their environment? Why are animals so efficient at performing simple tasks that robots still find extremely difficult to perform? How can we learn from the interaction between neuroscience and robotics, shed light to complex biological phenomena and at the same time design novel robots?

Biologically inspired robotics has been a long standing research field that draws inspiration from biomechanics, neuroscience and robotics, and aims in both explaining the mechanisms with which organisms interact with their environment, learn and adapt, and at the same time create better algorithms and robots that can perform at a near to human cognition level.

Deep Learning has been very successful in providing near -or even above- human level intelligence performance in certain problems, but has no biological plausibility and often needs an enormous amount of data and processing power to train properly.

We will discuss about a category of methods and algorithms that can pose as an alternative and complement to DL, especially for applications which are energy and computationally intense. We will explore the methods behind biologically inspired robotics, and see how the insights that we can gain from robotics can provide useful feedback about our hypotheses of human cognition.

About Manos Angelidis

Manos Angelidis holds a Masters degree in Mechanical Engineering, and a Masters degree in Biomedical Engineering from the National Technical University of Athens. He spent 3 years as a Biomedical Engineering researcher in Athens on the topic of computational biomechanics and fluid dynamics. For the last 3 years he has been working at fortiss GmbH, a research institute for software and AI, also doing PhD research at the department of Informatics of the Technical University of Munich on the topic of AI and Robotics. He has been a software architect, lead senior software engineer and scrum Master in the distributed team developing the Neurorobitcs Platform within the Human Brain Project. He has expertise in simulated physics and Neuromorphic Computing, and specializes in C++, Javascript, Python, and Agile Methodologies.

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From biology to robotics and back by Manos Angelidis

  1. 1. From biology to robotics and back Manos Angelidis Simulation Software Engineer @ The Human Brain Project - Neurorobotics Platform
  2. 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. 3. Research and technology fields ▶ Biomaterials ▶ Biotechnology - biochemistry ▶ Biorobotics ▶ Biomechanics ▶ Bio-inspired AI ▶ Neuromorphic computing
  4. 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. 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. 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. 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. 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. 9. Second step - physically realistic simulation ▶ Simulating environments - realistic physics • ODE • Bullet • Nvidia PhysX • Dart • Simbody • Mujoco
  10. 10. Third Step - AI control algorithms ▶ Neural Nets ▶ Artificial Brains ▶ High level controllers ▶ Reinforcement learning
  11. 11. Putting it all together ▶ Realistic physical simulation of complex phenomena (fluids, rigids, soft bodies) ▶ Sensory integration ▶ Control algorithms ▶ Synchronization mechanisms ▶ Cool visualization
  12. 12. One implementation - The Neurorobotics Platform
  13. 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. 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. 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. 16. Neuromorphic hardware ▶ Intel Loihi ▶ IBM TrueNorth - currently trying other architectures ▶ SpiNNaker / BrainScale
  17. 17. Examples of successful projects ▶ EPFL Salamandra Robotica ▶ Nengo Robot adaptive control ▶ Intel loihi object detection ▶ Teaching a robot to walk
  18. 18. Why is it important for IT ▶ Supercomputing ▶ Hardware ▶ Algorithms ▶ Databases
  19. 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. 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