This work proposes an optimization-based manipulation planning framework
where the objectives are learned functionals of signed-distance fields that
represent objects in the scene. Most manipulation planning approaches rely on
analytical models and carefully chosen abstractions/state-spaces to be
effective. A central question is how models can be obtained from data that are
not primarily accurate in their predictions, but, more importantly, enable
efficient reasoning within a planning framework, while at the same time being
closely coupled to perception spaces. We show that representing objects as
signed-distance fields not only enables to learn and represent a variety of
models with higher accuracy compared to point-cloud and occupancy measure
representations, but also that SDF-based models are suitable for
optimization-based planning. To demonstrate the versatility of our approach, we
learn both kinematic and dynamic models to solve tasks that involve hanging
mugs on hooks and pushing objects on a table. We can unify these quite
different tasks within one framework, since SDFs are the common object
representation. Video: this https URL

本文提出了一种基于优化的操作规划框架，其中目标是学习表示场景中物体的符号距离场的学习功能。我们通过代表物体的符号距离场来展示表示对象的方法不仅可以学习和表示多种模型，而且比点云和占用度量表示具有更高的准确性，并适用于基于优化的规划。我们通过学习运动学和动力学模型来解决在钩子上挂杯子和在桌子上推动物体等任务，这些任务都可以在相同的研究框架下解决，因为符号距离场是对象的共同表示。