Week 1 Jan 20

LectureIntroduction

• Introduction to embodied learning

Week 2 Jan 27

LectureDeep Learning for Structured Outputs

TutorialHPC tutorial

• Deep learning for structured outputs
• Graphical models, energy-based models
• Autoregressive models
• Normalizing flows

Week 3 Feb 3

Lecture3D Vision, Mapping

TutorialEmbodied Simulators [Slides] [Code]

• Diffusion models
  • Probabilistic foundation
  • Applications in embodied learning
• 3D network designs
  • Bird’s eye view networks
  • Point cloud networks
  • Equivariance
  • Sensor fusion
  • Multi-task architecture
• Physical grounding
  • Stereo, self-supervised depth

Week 4 Feb 13

LectureSelf-Supervised Representation Learning and Object Discovery

TutorialVideo Learning [Slides]

• Physical grounding
  • Stereo, self-supervised depth
  • Optical flow
  • Unsupervised flow, depth and pose
• Mapping
  • Soft mapping
  • Registration
• Representation learning
  • DAE, MAE
  • Energy-based models
  • SSL, JEPA