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• W4383109488 abstract "Multi-objective optimization problems are ubiquitous in robotics, e.g., the optimization of a robot manipulation task requires a joint consideration of grasp pose configurations, collisions and joint limits. While some demands can be easily hand-designed, e.g., the smoothness of a trajectory, several task-specific objectives need to be learned from data. This work introduces a method for learning data-driven SE(3) cost functions as diffusion models. Diffusion models can represent highly-expressive multimodal distributions and exhibit proper gradients over the entire space due to their score-matching training objective. Learning costs as diffusion models allows their seamless integration with other costs into a single differentiable objective function, enabling joint gradient-based motion optimization. In this work, we focus on learning SE(3) diffusion models for 6DoF grasping, giving rise to a novel framework for joint grasp and motion optimization without needing to decouple grasp selection from trajectory generation. We evaluate the representation power of our SE(3) diffusion models w.r.t. classical generative models, and we showcase the superior performance of our proposed optimization framework in a series of simulated and real-world robotic manipulation tasks against representative baselines. Videos, code and additional details are available at: https://sites.google.com/view/se3dif" @default.
• W4383109488 created "2023-07-05" @default.
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• W4383109488 date "2023-05-29" @default.
• W4383109488 modified "2023-10-18" @default.
• W4383109488 title "SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion" @default.
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• W4383109488 doi "https://doi.org/10.1109/icra48891.2023.10161569" @default.
• W4383109488 hasPublicationYear "2023" @default.
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