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.
翻译:多目标优化问题在机器人中普遍存在,例如,优化机器人操纵任务需要共同考虑掌握的配置、碰撞和联合界限。虽然有些需求可以容易地手工设计,例如轨道的顺利性,但需要从数据中学习若干任务特定目标。这项工作引入了一种方法,学习数据驱动SE(3)的成本功能作为扩散模型。扩散模型可以代表高度表达的多式联运分布,并在整个空间展示适当的梯度,因为其匹配培训目标。由于传播模型能够与其他成本无缝地结合到一个单一的不同目标功能中,使得基于梯度的联合运动优化成为可能。在这项工作中,我们侧重于学习SE(3)扩散模型,以掌握6DoF,从而形成一个用于联合掌握和运动优化的新框架,而不必从轨迹生成中分辨选择。我们评价我们的S(3)扩散模型的体现能力,并由于它们的得分比对等化模型,因此在整个空间展示出适当的梯度。我们提出的优化框架在一系列模拟和真实世界机器人操作上优异基线上的表现。</s>