P08 Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Conditional action diffusion, receding-horizon control, and the distinction between sampling and robot time.
Ongoing notes on literature, research, and engineering practice.
Reading notes organized around research questions, methods, experiments, and limitations.
Conditional action diffusion, receding-horizon control, and the distinction between sampling and robot time.
SALE objectives, TD7 engineering components, and the scope of empirical evidence.
TD-residual advantages, reward-to-go, lambda returns, and critic error.
18 bilingual tutorials on learning implementation, robot simulation, navigation scenes, VLA, and engineering toolchains.
Use pinned upstream code to check fields, image orientation, action queues, and a single request.
Validate units, composition layers, joint frames, and physical properties in stages.
Define shape contracts and distinguish axis changes, reshaping, views, and copies.
RL foundations, probability, policy optimization, and action modeling.
Benchmark selection, statistical evaluation, and reproducible protocols.
Research questions, method analysis, and academic discussions.
Toolchains, implementation, deployment, and troubleshooting.
Algorithm implementation, experiments, results, and reproducibility.
Align environment versions, training budgets, checkpoint selection, held-out testing, and aggregation.
Turn sample efficiency, adaptation, rewards, and timing into controlled research questions.
Connections and controlled experiments across action generation, policy adaptation, execution, human motion, and navigation worlds.
Dependency installation, static generation, browser verification, and source-preserving Git publication.