Covers the use of transformers in robotics, focusing on embodied perception and innovative applications in humanoid locomotion and reinforcement learning.
Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.
Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.
