Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Covers corrected exercises from the 2020 exam in the field of robotics, including topics such as accuracy, speed, DC motors, optimal gear ratio, dynamics of robot arms, encoders, and kinematics.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
