This paper develops a system that allows a design tool to learn and improve its performance over time by adapting to user feedback, and demonstrates its effectiveness in a real-world setting. Practitioners might care about this approach for building more robust and adaptable AI systems.
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This paper develops a framework called DeformSmith that generates deformable assets for robots, such as 3D models with realistic physical behavior, to simulate and interact with in the real world. Practitioners in robotics and computer vision might care about DeformSmith because it can help create more realistic and interactive simulations of deformable objects.