11,287 research outputs found
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
Oligodendrocyte precursor cells: the multitaskers in the brain
In the central nervous system, oligodendrocyte precursor cells (OPCs) are recognized as the progenitors responsible for the
generation of oligodendrocytes, which play a critical role in myelination. Extensive research has shed light on the mechanisms
underlying OPC proliferation and diferentiation into mature myelin-forming oligodendrocytes. However, recent advances
in the feld have revealed that OPCs have multiple functions beyond their role as progenitors, exerting control over neural
circuits and brain function through distinct pathways. This review aims to provide a comprehensive understanding of OPCs
by frst introducing their well-established features. Subsequently, we delve into the emerging roles of OPCs in modulating brain function in both healthy and diseased states. Unraveling the cellular and molecular mechanisms by which OPCs
infuence brain function holds great promise for identifying novel therapeutic targets for central nervous system diseases
Global dynamics of a parabolic type equation arising from the curvature flow
This paper studies a type of degenerate parabolic problem with nonlocal term
\begin{equation*}
\begin{cases}
u_t=u^p(u_{xx}+u-\bar{u}) & 0<t<T_{{\max}},\ 0<x<a,
u_x(0,t)=u_x(a,t)=0 & 0<t<T_{{\max}},
u(x,0)=u_0(x) & 0<x<a,
\end{cases} \end{equation*} where , . In this paper, the
classification of the finite-time blowup/global existence phenomena based on
the associated energy functional and explicit expression of all nonnegative
steady states are demonstrated. Moreover, we combine the applications of
Lojasiewicz-Simon inequality and energy estimates to derive that any bounded
solution with positive initial data converges to some steady state as
Simple Image-level Classification Improves Open-vocabulary Object Detection
Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a
given set of base categories on which the detection model is trained. Recent
OVOD methods focus on adapting the image-level pre-trained vision-language
models (VLMs), such as CLIP, to a region-level object detection task via, eg.,
region-level knowledge distillation, regional prompt learning, or region-text
pre-training, to expand the detection vocabulary. These methods have
demonstrated remarkable performance in recognizing regional visual concepts,
but they are weak in exploiting the VLMs' powerful global scene understanding
ability learned from the billion-scale image-level text descriptions. This
limits their capability in detecting hard objects of small, blurred, or
occluded appearance from novel/base categories, whose detection heavily relies
on contextual information. To address this, we propose a novel approach, namely
Simple Image-level Classification for Context-Aware Detection Scoring
(SIC-CADS), to leverage the superior global knowledge yielded from CLIP for
complementing the current OVOD models from a global perspective. The core of
SIC-CADS is a multi-modal multi-label recognition (MLR) module that learns the
object co-occurrence-based contextual information from CLIP to recognize all
possible object categories in the scene. These image-level MLR scores can then
be utilized to refine the instance-level detection scores of the current OVOD
models in detecting those hard objects. This is verified by extensive empirical
results on two popular benchmarks, OV-LVIS and OV-COCO, which show that
SIC-CADS achieves significant and consistent improvement when combined with
different types of OVOD models. Further, SIC-CADS also improves the
cross-dataset generalization ability on Objects365 and OpenImages. The code is
available at https://github.com/mala-lab/SIC-CADS.Comment: Accepted at AAAI 202
KernelGPA: A Globally Optimal Solution to Deformable SLAM in Closed-form
We study the generalized Procrustes analysis (GPA), as a minimal formulation
to the simultaneous localization and mapping (SLAM) problem. We propose
KernelGPA, a novel global registration technique to solve SLAM in the
deformable environment. We propose the concept of deformable transformation
which encodes the entangled pose and deformation. We define deformable
transformations using a kernel method, and show that both the deformable
transformations and the environment map can be solved globally in closed-form,
up to global scale ambiguities. We solve the scale ambiguities by an
optimization formulation that maximizes rigidity. We demonstrate KernelGPA
using the Gaussian kernel, and validate the superiority of KernelGPA with
various datasets. Code and data are available at
\url{https://bitbucket.org/FangBai/deformableprocrustes}.Comment: This paper has been accepted for publication in the International
Journal of Robotics Research, 2023. https://doi.org/10.1177/0278364923119538
Shelter: Smartphone Bridged Socialized Body Networks for Epidemic Control
We propose using information, computing and networking innovations to tackle epidemic control challenges
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