Publication
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Julen Urain; Niklas Funk; Jan Peters; Georgia Chalvatzaki
In: IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023. IEEE International Conference on Robotics and Automation (ICRA), Pages 5923-5930, IEEE, 2023.
Abstract
Multi-objective optimization problems are ubiqui-
tous 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 differen-
tiable 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 de-
couple 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 per-
formance 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