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。

用符号距离场学习模型进行操作规划

Learning Models as Functionals of Signed-Distance Fields for  Manipulation Planning

Many functional elements of human homes and workplaces consist of rigid
components which are connected through one or more sliding or rotating
linkages. Examples include doors and drawers of cabinets and appliances;
laptops; and swivel office chairs. A robotic mobile manipulator would benefit
from the ability to acquire kinematic models of such objects from observation.
This paper describes a method by which a robot can acquire an object model by
capturing depth imagery of the object as a human moves it through its range of
motion. We envision that in future, a machine newly introduced to an
environment could be shown by its human user the articulated objects particular
to that environment, inferring from these "visual demonstrations" enough
information to actuate each object independently of the user.
Our method employs sparse (markerless) feature tracking, motion segmentation,
component pose estimation, and articulation learning; it does not require prior
object models. Using the method, a robot can observe an object being exercised,
infer a kinematic model incorporating rigid, prismatic and revolute joints,
then use the model to predict the object's motion from a novel vantage point.
We evaluate the method's performance, and compare it to that of a previously
published technique, for a variety of household objects.

该论文介绍了一种通过捕捉物体的深度图像来获取其运动学模型的方法，以便于机器人能够独立使用这些物体，这种方法不需要先前的物体模型，并使用了稀疏（无标记）特征跟踪、运动分割、组件姿态估计和运动学习等技术。