Robotic Dynamics Prediction Using Fine-Tuned Diffusion Models
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Solution Overview
Problem
Generating a dynamics model for predicting the surrounding of a robotic device requires significant training effort, especially for vision-based control tasks.
Innovation Solution
Employ a pre-trained diffusion model, fine-tuned with training tuples that include control actions, images before and after action execution, and optionally spatial conditioning information, to generate a dynamics model that predicts the robotic device's surrounding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dynamics model is trained from scratch for vision-based control tasks, then the prediction capability for robotic control scenarios is improved, but the training effort and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by using a pre-trained diffusion model that has already learned general image generation capabilities before being adapted to robotic control tasks. This pre-training phase performs the heavy lifting of learning fundamental visual patterns, so that when the model is later fine-tuned for specific robotic dynamics prediction, it requires significantly less training effort while still achieving high prediction capability.
2Loss of time
If a pre-trained diffusion model is used to generate a dynamics model, then the computational cost and training time are reduced, but the model requires adaptation to specific robotic control scenarios
Solution Approach 1:
The patent applies parameter changes by fine-tuning the pre-trained diffusion model on robot-specific data. This involves adjusting the model's parameters (weights and biases) to adapt it from general image generation to specific robotic control scenarios. The fine-tuning process modifies the pre-trained model's parameters using a smaller dataset of robot actions and corresponding environmental changes, thereby reducing training time while maintaining scenario-specific accuracy.
Data Source
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AI summary
Various aspects relate to a method of generating a dynamics model for predicting a surrounding of a robotic device resulting from an interaction of the robotic device, the method including: receiving a pre-trained diffusion model configured to generate an output image responsive to inputting an input image and a text prompt that indicates how to modify the input image; receiving a plurality of training tuples, each including a control action, a first image representing a surrounding of the robotic device prior to the robotic device executing the control action, and a second image representing the surrounding of the robotic device after execution of the control action; generating the dynamics model by training the pre-trained diffusion model for each training tuple to generate the second image of the training tuple as output image responsive to inputting the first image as input image and the control action as text prompt.