AIGC Generation Models and Diffusion‑Based Planning for Embodied AI

Introduction – The article introduces AIGC (AI‑generated content) generation models, emphasizing diffusion models as the most powerful image‑generation paradigm and their ability to produce high‑quality results from text prompts.

Background on Generative Models – It reviews the evolution from VAE (encoder‑decoder) to GAN, normalizing flow, and finally diffusion models, explaining why diffusion models are advantageous: they require no discriminator, have stable training, and only need a reverse denoising process.

Diffusion Model Mechanics – The forward diffusion process gradually adds Gaussian noise to an image, while the reverse process learns a denoising network parameterized by \(\theta\) that removes noise step‑by‑step, enabling high‑fidelity image synthesis after many iterations.

Adapting Diffusion to Reinforcement Learning – The article proposes treating model‑based RL as a trajectory‑generation problem. By concatenating state \(s\) and action \(a\) into a tensor, a 1‑D convolution (or UNet) can diffuse and then denoise the trajectory, producing robot control sequences.

Guidance Functions – To overcome limited expert data, a guidance function (derived from a differentiable reward or goal‑condition) is added during denoising, steering the generated trajectory toward desired behaviors (e.g., navigating around obstacles or inserting a plug).

Self‑Evolution via Synthetic Data – The method, named AdaptDiffuser (ICML 2023 oral), iteratively generates diverse synthetic trajectories, selects high‑reward samples, and retrains the diffusion model, allowing it to self‑evolve and adapt to new tasks such as unseen mazes or complex manipulation.

Embodied AI and Large Models – The discussion extends to integrating large vision‑language models (e.g., Segment‑Anything, LLaMA) for embodied AI, highlighting three challenges: first‑person perception, coupled planning‑perception, and fine‑grained goal‑conditioned interaction.

EgoCOT Dataset & Embodied GPT – A new first‑person video dataset (EgoCOT) is introduced, providing detailed part‑level annotations (handles, knobs, etc.). Using an embodied‑former , the system maps visual inputs to language queries, generates structured plans, and refines them through multi‑round dialogue and self‑attention.

Demo Highlights – Examples include opening a cabinet, hammering a nail, and generating step‑by‑step plans for making coffee, demonstrating the model’s ability to produce executable, fine‑grained actions.

Conclusion – The presentation summarizes the potential of diffusion‑based planning, self‑evolving data generation, and multimodal embodied models for advancing robot intelligence toward AGI‑level capabilities.