Lecture
Introduction: Neurobotics
Description
This lecture introduces the concept of Neurobotics, which combines neuroscience, robotics, and artificial intelligence to study embodied autonomous neural systems. The instructor explains the types of learning involved, such as unsupervised learning for sensory representations and reward-based reinforcement learning for actions and behaviors. Simple examples from Valentino Breitenberg's book 'Vehicles' are used to illustrate how behaviors emerge in neurobotic systems.
In MOOCs (3)
Neuro Robotics
At the same time, several different tutorials on available data and data tools, such as those from the Allen Institute for Brain Science, provide you with in-depth knowledge on brain atlases, gene exp
Neurorobotics
The MOOC on Neuro-robotics focuses on teaching advanced learners to design and construct a virtual robot and test its performance in a simulation using the HBP robotics platform. Learners will learn t
Neurorobotics
The MOOC on Neuro-robotics focuses on teaching advanced learners to design and construct a virtual robot and test its performance in a simulation using the HBP robotics platform. Learners will learn t
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (44)
Self-supervised Learning for Autonomous Vehicles
Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Machine Learning for Physicists/Chemists: Image Classification
Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.
Acquiring Data for Learning
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Perception: Data-Driven Approaches
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.