SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion (2209.03855v4)

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.

• The paper introduces a novel framework that integrates diffusion models to learn smooth cost functions in SE(3) for optimizing both grasp selection and motion planning.
• It achieves superior 6DoF grasp pose generation by outperforming traditional methods, enabling diverse and successful grasp configurations in simulated and real-world tests.
• The joint optimization framework jointly addresses grasp and trajectory planning, paving the way for advanced autonomous manipulation in complex environments.

Overview of SE(3)-DiffusionFields for Robotic Manipulation Optimization

The research paper, titled "SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion," introduces a novel approach leveraging diffusion models to optimize robotic manipulation tasks involving complex trajectory planning and grasping strategies. The authors propose a framework that coalesces the discrete elements of grasp selection and trajectory planning into a unified optimization problem, allowing for the concurrent determination of grasp poses and robotic motions in SE(3) space. This paper addresses the challenges inherent in maintaining smooth and continuously differentiable cost functions over SE(3), paving the way for improved motion generation in real-world robotic applications.

Key Contributions

1. Smooth Cost Functions in SE(3): The paper presents an innovative method to learn cost functions in SE(3) using diffusion models. The essence of diffusion models in this context is their capability to represent complex, multimodal distributions, which can be smoothly integrated into gradient-based optimization frameworks. The authors demonstrate that their SE(3)-DiffusionFields (SE(3)-DiF) method can effectively manage the intrinsic complexities of SE(3) manifolds.
2. 6DoF Grasp Pose Generation: The research explores devising SE(3) models for generating six degrees of freedom (6DoF) grasp poses, a critical component in autonomous manipulation. The model's ability to yield diverse and viable grasp configurations is rigorously validated against standard grasp generative models, showcasing superior performance in both simulated environments and real-world setups.
3. Joint Grasp and Motion Optimization: By integrating learned SE(3) diffusion models with other differentiable objectives, the authors establish a framework for joint optimization of grasp selection and movement trajectories. This integration allows for addressing multi-objective optimization scenarios where grasp suitability and trajectory efficiency are simultaneously critical.

Experimental Evaluation

The paper provides comprehensive evaluations of the proposed SE(3)-DiF methodology across multiple tasks:

• Grasp Pose Generation: The paper evaluates the SE(3)-DiF against variational autoencoders (VAEs) and classifiers, with results indicating that the proposed diffusion approach delivers more diverse and successful grasp configurations.
• Simulated and Real-World Manipulation Tasks: Experiments in both simulated environments and real robotic settings affirm the efficacy of SE(3)-DiF in joint trajectory and grasp optimization tasks. The proposed approach consistently demonstrates higher success rates with fewer initial samples.

Implications for Future Research

The introduction of diffusion models into SE(3) space provides a foundation for further exploration in autonomous robotic manipulation. The ability to handle the complex interactions between robot grasping and motion planning in a seamless gradient-based optimization loop marks a significant shift from traditional decoupled approaches. Future research could explore the application of SE(3)-DiF models to dynamic environments where object positions are not static. Additionally, integrating real-time sensor data for dynamic grasp adjustments and extending the diffusion model to other types of manipulators or robotic tasks could provide valuable directions for subsequent studies.

The methodology outlined in this work not only bridges gaps in current optimization paradigms but also sets a precedent for the utilization of advanced learning models in the nuanced domain of human-robot interaction.