Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han, Seungryong Kim, Taekyung Kim
2025-10-31
Summary
This paper explores how to use powerful image-generating AI, specifically diffusion models, to help robots learn tasks more effectively by giving them a better understanding of what they're seeing.
What's the problem?
Usually, when using pre-trained AI to help robots, the AI's 'vision' is fixed and doesn't change while the robot is learning. While these AI models are good at recognizing images generally, they aren't specifically tuned for the visual needs of controlling a robot. Simply telling the AI what to look for using text prompts, which works well in other areas, doesn't really help the robot learn to perform tasks better, and can even make things worse. This is because the images the AI was originally trained on are very different from what a robot actually 'sees' when trying to do something in the real world.
What's the solution?
The researchers developed a method called ORCA that creates special prompts for the AI. These prompts aren't just simple text descriptions; they're 'learnable,' meaning the AI adjusts them to better fit the specific task and environment the robot is in. Additionally, ORCA uses prompts that focus on the small, moment-to-moment changes in what the robot sees, providing detailed visual information that's crucial for control. Essentially, they're teaching the AI to pay attention to the right visual cues for robotic tasks.
Why it matters?
This work is important because it allows robots to leverage the power of advanced image AI without needing to retrain the AI itself, which is expensive and time-consuming. By creating task-specific visual understanding, ORCA helps robots perform complex tasks more successfully than previous methods, pushing the boundaries of what robots can achieve.
Abstract
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we explore leveraging pre-trained text-to-image diffusion models to obtain task-adaptive visual representations for robotic control, without fine-tuning the model itself. However, we find that naively applying textual conditions - a successful strategy in other vision domains - yields minimal or even negative gains in control tasks. We attribute this to the domain gap between the diffusion model's training data and robotic control environments, leading us to argue for conditions that consider the specific, dynamic visual information required for control. To this end, we propose ORCA, which introduces learnable task prompts that adapt to the control environment and visual prompts that capture fine-grained, frame-specific details. Through facilitating task-adaptive representations with our newly devised conditions, our approach achieves state-of-the-art performance on various robotic control benchmarks, significantly surpassing prior methods.