180 research outputs found
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
Effects of transgenic Cry1Ac + CpTI cotton on non-target mealybug pest Ferrisia virgata and its predator Cryptolaemus montrouzieri
Recently, several invasive mealybugs (Hemiptera: Pseudococcidae) have rapidly spread to Asia and have become a serious threat to the production of cotton including transgenic cotton. Thus far, studies have mainly focused on the effects of mealybugs on non-transgenic cotton, without fully considering their effects on transgenic cotton and trophic interactions. Therefore, investigating the potential effects of mealybugs on transgenic cotton and their key natural enemies is vitally important. A first study on the effects of transgenic cotton on a non-target mealybug, Ferrisia virgata (Cockerell) (Hemiptera: Pseudococcidae) was performed by comparing its development, survival and body weight on transgenic cotton leaves expressing Cry1Ac (Bt toxin) + CpTI (Cowpea Trypsin Inhibitor) with those on its near-isogenic non-transgenic line. Furthermore, the development, survival, body weight, fecundity, adult longevity and feeding preference of the mealybug predator Cryptolaemus montrouzieri Mulsant (Coleoptera: Coccinellidae) was assessed when fed F. virgata maintained on transgenic cotton. In order to investigate potential transfer of Cry1Ac and CpTI proteins via the food chain, protein levels in cotton leaves, mealybugs and ladybirds were quantified. Experimental results showed that F. virgata could infest this bivalent transgenic cotton. No significant differences were observed in the physiological parameters of the predator C. montrouzieri offered F. virgata reared on transgenic cotton or its near-isogenic line. Cry1Ac and CpTI proteins were detected in transgenic cotton leaves, but no detectable levels of both proteins were present in the mealybug or its predator when reared on transgenic cotton leaves. Our bioassays indicated that transgenic cotton poses a negligible risk to the predatory coccinellid C. montrouzieri via its prey, the mealybug F.virgata
Scene Graph for Embodied Exploration in Cluttered Scenario
The ability to handle objects in cluttered environment has been long
anticipated by robotic community. However, most of works merely focus on
manipulation instead of rendering hidden semantic information in cluttered
objects. In this work, we introduce the scene graph for embodied exploration in
cluttered scenarios to solve this problem. To validate our method in cluttered
scenario, we adopt the Manipulation Question Answering (MQA) tasks as our test
benchmark, which requires an embodied robot to have the active exploration
ability and semantic understanding ability of vision and language.As a general
solution framework to the task, we propose an imitation learning method to
generate manipulations for exploration. Meanwhile, a VQA model based on dynamic
scene graph is adopted to comprehend a series of RGB frames from wrist camera
of manipulator along with every step of manipulation is conducted to answer
questions in our framework.The experiments on of MQA dataset with different
interaction requirements demonstrate that our proposed framework is effective
for MQA task a representative of tasks in cluttered scenario
MQA: Answering the Question via Robotic Manipulation
In this paper, we propose a novel task -- Manipulation Question Answering
(MQA), where the robot is required to find the answer to the question by
actively exploring the environment via manipulation. A framework consisting of
a QA model and a manipulation model is proposed to solve this problem. For the
QA model, we adopt the method of Visual Question Answering (VQA). For the
manipulation model, a Deep Q Network (DQN) model is proposed to generate
manipulations. By manipulating objects, the robot can continuously explore the
bin until the answer to the question is found. Besides, a novel dataset for
simulation that contains a variety of object models, complicated scenarios and
corresponding question-answer pairs is established. Extensive experiments have
been conducted to validate the effectiveness of the proposed framework
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Spiking neural networks (SNNs) are emerging as an energy-efficient
alternative to traditional artificial neural networks (ANNs) due to their
unique spike-based event-driven nature. Coding is crucial in SNNs as it
converts external input stimuli into spatio-temporal feature sequences.
However, most existing deep SNNs rely on direct coding that generates powerless
spike representation and lacks the temporal dynamics inherent in human vision.
Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that
leverages the multi-dimensional gated attention unit to efficiently encode
inputs into powerful representations before feeding them into the SNN
architecture. GAC functions as a preprocessing layer that does not disrupt the
spike-driven nature of the SNN, making it amenable to efficient neuromorphic
hardware implementation with minimal modifications. Through an observer model
theoretical analysis, we demonstrate GAC's attention mechanism improves
temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and
ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with
remarkable efficiency. Notably, we improve top-1 accuracy by 3.10\% on CIFAR100
with only 6-time steps and 1.07\% on ImageNet while reducing energy usage to
66.9\% of the previous works. To our best knowledge, it is the first time to
explore the attention-based dynamic coding scheme in deep SNNs, with
exceptional effectiveness and efficiency on large-scale datasets.Comment: 12 pages, 7 figure
Vectorial structure of a hard-edged-diffracted four-petal Gaussian beam in the far field
Based on the vector angular spectrum method and the stationary phase method
and the fact that a circular aperture function can be expanded into a finite
sum of complex Gaussian functions, the analytical vectorial structure of a
four-petal Gaussian beam (FPGB) diffracted by a circular aperture is derived in
the far field. The energy flux distributions and the diffraction effect
introduced by the aperture are studied and illustrated graphically. Moreover,
the influence of the f-parameter and the truncation parameter on the
nonparaxiality is demonstrated in detail. In addition, the analytical formulas
obtained in this paper can degenerate into un-apertured case when the
truncation parameter tends to infinity. This work is beneficial to strengthen
the understanding of vectorial properties of the FPGB diffracted by a circular
aperture
Analytical vectorial structure of non-paraxial four-petal Gaussian beams in the far field
The analytical vectorial structure of non-paraxial four-petal Gaussian
beams(FPGBs) in the far field has been studied based on vector angular spectrum
method and stationary phase method. In terms of analytical electromagnetic
representations of the TE and TM terms, the energy flux distributions of the TE
term, the TM term, and the whole beam are derived in the far field,
respectively. According to our investigation, the FPGBs can evolve into a
number of small petals in the far field. The number of the petals is determined
by the order of input beam. The physical pictures of the FPGBs are well
illustrated from the vectorial structure, which is beneficial to strengthen the
understanding of vectorial properties of the FPGBs
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