Planning with Diffusion for Flexible Behavior Synthesis
Offered By: Generative Memory Lab via YouTube
Course Description
Overview
Explore cutting-edge research on flexible behavior synthesis in this 40-minute presentation by MIT EECS PhD student Yilun Du. Delve into the innovative approach of planning with diffusion for generating adaptive trajectories and behaviors. Learn about neural networks combined with trajectory optimization, generative modeling for planning, and compositional trajectory generation. Discover the Diffuser model's capabilities in variable-length predictions and flexible behavior synthesis through distribution composition. Examine advanced techniques such as goal planning through inpainting, test-time cost specification, and offline reinforcement learning with value guidance. Gain insights into the application of test-time cost functions and their impact on behavior synthesis.
Syllabus
Intro
Neural nets + trajectory optimization
Is the model the bottleneck?
Planning as generative modeling
A generative model of trajectories
Compositional trajectory generation
Sampling from Diffuser
Variable-length predictions
Flexible Behavior Synthesis through Composing Distributions
Goal Planning through Inpainting
Test-Time Cost Specification
Offline Reinforcement Learning through Value Guidance
Test-Time Cost Functions
Taught by
Generative Memory Lab
Related Courses
Sparse Representations in Image Processing: From Theory to PracticeTechnion - Israel Institute of Technology via edX Sparse Representations in Signal and Image Processing
Technion - Israel Institute of Technology via edX Stable Diffusion - Inpainting, Outpainting, Upscaling & More
Nerdy Rodent via YouTube Neural Network Architectures for Images
Paul Hand via YouTube ComfyUI - Node Based Stable Diffusion UI
Olivio Sarikas via YouTube