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.
You can download the slides from SlideShare:
About Manos Angelidis