Sangho An

Enhancing Visuomotor Manipulation Control with a Predictive-Dynamics-Aware World-Model-Conditioned Diffusion Policy

Sangho An¹†, Geunha Kim²†, and Jun Moon¹∗

The 26th International Conference on Control, Automation and Systems (ICCAS 2026) · Presented · Oct 28, 2026

Oral presentation, Paper WeBT8.3 · Wednesday, Oct 28, 2026, 16:10–16:25 · Accepted Jul 31, 2026

As an undergraduate researcher at Hanyang University’s Control & Optimization Lab, I studied robot manipulation from reinforcement learning through imitation learning. This work extends Diffusion Policy, a strong imitation-learning policy, by conditioning its denoising network on a latent feature from a lightweight GRU-based world model. The world model is used only as a conditioning input rather than as a simulator, preserving a practical computational footprint for real-robot deployment.

• Addressed the limitation of image-only Diffusion Policy features, which do not explicitly capture temporal scene progression or object interactions.
• Designed the world-model-conditioned Diffusion Policy architecture, experiment plan, and implementation while learning from a senior coauthor.
• On real Franka Emika Panda pick-and-place experiments, improved success rate from 45.0% to 72.5% under the same training conditions, a 27.5 percentage-point gain.
• Kept added inference latency to approximately 70 ms and achieved the improvement through architectural expansion without increasing the training dataset.