Humans seemingly incorporate potential touch signals in their perception. Our
goal is to equip robots with a similar capability, which we term Imagine2touch.
Imagine2touch aims to predict the expected touch signal based on a visual patch
representing the area to be touched. We use ReSkin, an inexpensive and compact
touch sensor to collect the required dataset through random touching of five
basic geometric shapes, and one tool. We train Imagine2touch on two out of
those shapes and validate it on the ood. tool. We demonstrate the efficacy of
Imagine2touch through its application to the downstream task of object
recognition. In this task, we evaluate Imagine2touch performance in two
experiments, together comprising 5 out of training distribution objects.
Imagine2touch achieves an object recognition accuracy of 58% after ten touches
per object, surpassing a proprioception baseline.

我们的目标是使机器人具备与人类类似的潜在触觉信号感知能力，通过使用一种名为 Imagine2touch 的方法，可以根据表示待触摸区域的视觉补丁来预测预期的触摸信号。我们使用了一种廉价而紧凑的触摸传感器 ReSkin 来收集所需的数据集，通过对五种基本几何形状和一种工具进行随机触摸。我们在其中两种形状上对 Imagine2touch 进行训练，并在 odd 工具上进行验证。我们通过将 Imagine2touch 应用于物体识别的下游任务来展示其有效性。在这个任务中，我们通过两个实验评估了 Imagine2touch 的性能，总共包括 5 个训练分布对象。在每个对象受到十次触摸后，Imagine2touch 的物体识别准确率达到了 58%，超过了一个本体感知基线。

Imagine2touch：使用低维信号的机器人操作预测性触觉感知

Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using  Efficient Low-Dimensional Signals

Neural implicit representation of geometric shapes has witnessed considerable
advancements in recent years. However, common distance field based implicit
representations, specifically signed distance field (SDF) for watertight shapes
or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from
degradation of reconstruction accuracy when converting to explicit surface
points and meshes. In this paper, we introduce a novel neural implicit
representation based on unsigned orthogonal distance fields (UODFs). In UODFs,
the minimal unsigned distance from any spatial point to the shape surface is
defined solely in one orthogonal direction, contrasting with the
multi-directional determination made by SDF and UDF. Consequently, every point
in the 3D UODFs can directly access its closest surface points along three
orthogonal directions. This distinctive feature leverages the accurate
reconstruction of surface points without interpolation errors. We verify the
effectiveness of UODFs through a range of reconstruction examples, extending
from simple watertight or non-watertight shapes to complex shapes that include
hollows, internal or assembling structures.

基于无符号正交距离场的新型神经隐式表示法可实现准确重构曲面点而无需插值误差。

无符号正交距离场：多样化 3D 形状的准确神经隐式表示

Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit  Representation for Diverse 3D Shapes

Query embedding (QE) -- which aims to embed entities and first-order logical
(FOL) queries in low-dimensional spaces -- has shown great power in multi-hop
reasoning over knowledge graphs. Recently, embedding entities and queries with
geometric shapes becomes a promising direction, as geometric shapes can
naturally represent answer sets of queries and logical relationships among
them. However, existing geometry-based models have difficulty in modeling
queries with negation, which significantly limits their applicability. To
address this challenge, we propose a novel query embedding model, namely Cone
Embeddings (ConE), which is the first geometry-based QE model that can handle
all the FOL operations, including conjunction, disjunction, and negation.
Specifically, ConE represents entities and queries as Cartesian products of
two-dimensional cones, where the intersection and union of cones naturally
model the conjunction and disjunction operations. By further noticing that the
closure of complement of cones remains cones, we design geometric complement
operators in the embedding space for the negation operations. Experiments
demonstrate that ConE significantly outperforms existing state-of-the-art
methods on benchmark datasets.

本研究提出一种新的查询嵌入模型，称为锥体嵌入（ConE），通过将实体和查询表示为二维锥形的笛卡尔积，处理与所有的一阶逻辑运算，包括合取，析取和否定，并在查询中引入几何补充算子，处理否定运算，从而在基准数据集上显著优于现有其他模型。