Generative-AI-Driven Jumping Robot Design Using Diffusion Models

Astract-Recent advances in foundation models are significantly expanding the capabilities of AI models. As part of this progress, this paper introduces a robot design framework that uses a diffusion model approach for generating 3D mesh structures. Specifically, we focus on generating directly fabri-cable robot structures that require no post-processing guided by human-imposed design constraints. Our approach can find the optimal design of the robot by optimizing or composing embedding vectors of the model. The efficacy of the framework is validated through an application to design, fabricate, and evaluate a jumping robot. Our solution is an optimized jumping robot with a 41% increase in jump height compared to the state-of-the-art design. Additionally, when the robot is augmented with an optimized foot, it can land reliably with a success ratio of 88% in contrast to the 4% success ratio of the base robot.

Authors

• Byungchul Kim
• Tsun-Hsuan Wang
• Daniela Rus

Keywords

• Three-dimensional displays
• Generative AI
• Foundation models
• Diffusion models
• Vectors
• Reliability
• Robots
• Design optimization
• Diffusion Model
• Robot Design
• Jumping Robot
• Embedding Vectors
• Mesh Structure
• Foundation Model
• Part In Progression
• Jump Height
• Robot Structure
• Center Of Mass
• Actuator
• Search Space
• Multi-objective Optimization
• Design Space
• Design Results
• Pareto Front
• Iterative Design
• Robot Performance
• Design Pipeline
• Symmetric Design
• Jump Performance
• Sampling-based Methods
• Physics-based Simulation
• Computational Design
• Generative Artificial Intelligence