367 research outputs found
Approximate Piecewise Constant Curvature Equivalent Model and Their Application to Continuum Robot Configuration Estimation
The continuum robot has attracted more attention for its flexibility.
Continuum robot kinematics models are the basis for further perception,
planning, and control. The design and research of continuum robots are usually
based on the assumption of piecewise constant curvature (PCC). However, due to
the influence of friction, etc., the actual motion of the continuum robot is
approximate piecewise constant curvature (APCC). To address this, we present a
kinematic equivalent model for continuum robots, i.e. APCC 2L-5R. Using
classical rigid linkages to replace the original model in kinematic, the APCC
2L-5R model effectively reduces complexity and improves numerical stability.
Furthermore, based on the model, the configuration self-estimation of the
continuum robot is realized by monocular cameras installed at the end of each
approximate constant curvature segment. The potential of APCC 2L-5R in
perception, planning, and control of continuum robots remains to be explored
Differentiable Frank-Wolfe Optimization Layer
Differentiable optimization has received a significant amount of attention
due to its foundational role in the domain of machine learning based on neural
networks. The existing methods leverages the optimality conditions and implicit
function theorem to obtain the Jacobian matrix of the output, which increases
the computational cost and limits the application of differentiable
optimization. In addition, some non-differentiable constraints lead to more
challenges when using prior differentiable optimization layers. This paper
proposes a differentiable layer, named Differentiable Frank-Wolfe Layer
(DFWLayer), by rolling out the Frank-Wolfe method, a well-known optimization
algorithm which can solve constrained optimization problems without projections
and Hessian matrix computations, thus leading to a efficient way of dealing
with large-scale problems. Theoretically, we establish a bound on the
suboptimality gap of the DFWLayer in the context of l1-norm constraints.
Experimental assessments demonstrate that the DFWLayer not only attains
competitive accuracy in solutions and gradients but also consistently adheres
to constraints. Moreover, it surpasses the baselines in both forward and
backward computational speeds
Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics
Multi-task learning of deformable object manipulation is a challenging
problem in robot manipulation. Most previous works address this problem in a
goal-conditioned way and adapt goal images to specify different tasks, which
limits the multi-task learning performance and can not generalize to new tasks.
Thus, we adapt language instruction to specify deformable object manipulation
tasks and propose a learning framework. We first design a unified
Transformer-based architecture to understand multi-modal data and output
picking and placing action. Besides, we have introduced the visible
connectivity graph to tackle nonlinear dynamics and complex configuration of
the deformable object. Both simulated and real experiments have demonstrated
that the proposed method is effective and can generalize to unseen instructions
and tasks. Compared with the state-of-the-art method, our method achieves
higher success rates (87.2% on average) and has a 75.6% shorter inference time.
We also demonstrate that our method performs well in real-world experiments.Comment: submitted to ICRA 202
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
In this paper, we consider the problem of change detection (CD) with two
heterogeneous remote sensing (RS) images. For this problem, an unsupervised
change detection method has been proposed recently based on the image
translation technique of Cycle-Consistent Adversarial Networks (CycleGANs),
where one image is translated from its original modality to the modality of the
other image so that the difference map can be obtained by performing
arithmetical subtraction. However, the difference map derived from subtraction
is susceptible to image translation errors, in which case the changed area and
the unchanged area are less distinguishable. To overcome the above shortcoming,
we propose a new unsupervised copula mixture and CycleGAN-based CD method
(COMIC), which combines the advantages of copula mixtures on statistical
modeling and the advantages of CycleGANs on data mining. In COMIC, the
pre-event image is first translated from its original modality to the
post-event image modality. After that, by constructing a copula mixture, the
joint distribution of the features from the heterogeneous images can be learnt
according to quantitive analysis of the dependence structure based on the
translated image and the original pre-event image, which are of the same
modality and contain totally the same objects. Then, we model the CD problem as
a binary hypothesis testing problem and derive its test statistics based on the
constructed copula mixture. Finally, the difference map can be obtained from
the test statistics and the binary change map (BCM) is generated by K-means
clustering. We perform experiments on real RS datasets, which demonstrate the
superiority of COMIC over the state-of-the-art methods
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
For the task of change detection (CD) in remote sensing images, deep
convolution neural networks (CNNs)-based methods have recently aggregated
transformer modules to improve the capability of global feature extraction.
However, they suffer degraded CD performance on small changed areas due to the
simple single-scale integration of deep CNNs and transformer modules. To
address this issue, we propose a hybrid network based on multi-scale
CNN-transformer structure, termed MCTNet, where the multi-scale global and
local information are exploited to enhance the robustness of the CD performance
on changed areas with different sizes. Especially, we design the ConvTrans
block to adaptively aggregate global features from transformer modules and
local features from CNN layers, which provides abundant global-local features
with different scales. Experimental results demonstrate that our MCTNet
achieves better detection performance than existing state-of-the-art CD
methods
Chinese Medicine Injection Qingkailing for Treatment of Acute Ischemia Stroke: A Systematic Review of Randomized Controlled Trials
Qingkailing (QKL) injection was a famous traditional Chinese patent medicine, which was extensively used to treat the acute stages of cerebrovascular disease. The aim of this study was to assess the quantity, quality and overall strength of the evidence on QKL in the treatment of acute ischemic stroke. Methods. An extensive search was performed within MEDLINE, Cochrane, CNKI, Vip and Wan-Fang up to November 2011. Randomized controlled trails (RCTs) on QKL for treatment of acute stroke were collected, irrespective of languages. Study selection, data extraction, quality assessment, and data analyses were conducted according to the Cochrane standards, and RevMan5 was used for data analysis. Results. 7 RCTs (545 patients) were included and the methodological quality was evaluated as generally low. The pooled results showed that QKL combined with conventional treatment was more effective in effect rate, and the score of MESSS and TNF-Ī± level compared with conventional treatment alone, but there was no significant difference in mortality of two groups. Only one trial reported routine life status. There were four trials reported adverse events, and no obvious adverse event occurred in three trials while one reported adverse events described as eruption and dizziness
Capacity Expansion of High Renewable Penetrated Energy Systems Considering Concentrating Solar Power for Seasonal Energy Balance
With the increasing proportion of variable renewable energy which owns
fluctuation characteristics and the promotion of the Clean Heating policy, the
seasonal energy imbalance of the system has been more and more challenging.
There is a lack of effective means to mitigate this challenge under the
background of gradual compression of the traditional thermal unit construction.
Concentrating solar power (CSP) is a promising technology to replace thermal
units by integrating emergency boilers to cope with extreme weather, and can
meet long-time energy balance as a seasonal peak regulation source. In this
paper, we propose a long-term high-resolution expansion planning model of the
energy system under high renewable penetration which integrates CSP technology
for seasonal energy balance. With the projection to 2050, by taking the energy
system in Xinjiang province which is a typical area of the Clean Heating
project with rich irradiance as a case study, it shows that the optimal
deployment of CSP and electric boiler (EB) can reduce the cost, peak-valley
difference of net load and renewable curtailment by 8.73%, 19.72% and 58.24%
respectively at 65% renewable penetration compared to the base scenario.Comment: 17 pages, 13 figure
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
Constrained policy search (CPS) is a fundamental problem in offline
reinforcement learning, which is generally solved by advantage weighted
regression (AWR). However, previous methods may still encounter
out-of-distribution actions due to the limited expressivity of Gaussian-based
policies. On the other hand, directly applying the state-of-the-art models with
distribution expression capabilities (i.e., diffusion models) in the AWR
framework is intractable since AWR requires exact policy probability densities,
which is intractable in diffusion models. In this paper, we propose a novel
approach, (dubbed
DiffCPS), which tackles the diffusion-based constrained policy search with the
primal-dual method. The theoretical analysis reveals that strong duality holds
for diffusion-based CPS problems, and upon introducing parameter approximation,
an approximated solution can be obtained after number
of dual iterations, where denotes the representation ability of the
parametrized policy. Extensive experimental results based on the D4RL benchmark
demonstrate the efficacy of our approach. We empirically show that DiffCPS
achieves better or at least competitive performance compared to traditional
AWR-based baselines as well as recent diffusion-based offline RL methods. The
code is now available at https://github.com/felix-thu/DiffCPS.Comment: 22 pages, 9 figures, 6 tables. Submitted to ICML 2024. arXiv admin
note: text overlap with arXiv:1910.13393 by other author
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