103 research outputs found
SCSC: Spatial Cross-scale Convolution Module to Strengthen both CNNs and Transformers
This paper presents a module, Spatial Cross-scale Convolution (SCSC), which
is verified to be effective in improving both CNNs and Transformers. Nowadays,
CNNs and Transformers have been successful in a variety of tasks. Especially
for Transformers, increasing works achieve state-of-the-art performance in the
computer vision community. Therefore, researchers start to explore the
mechanism of those architectures. Large receptive fields, sparse connections,
weight sharing, and dynamic weight have been considered keys to designing
effective base models. However, there are still some issues to be addressed:
large dense kernels and self-attention are inefficient, and large receptive
fields make it hard to capture local features. Inspired by the above analyses
and to solve the mentioned problems, in this paper, we design a general module
taking in these design keys to enhance both CNNs and Transformers. SCSC
introduces an efficient spatial cross-scale encoder and spatial embed module to
capture assorted features in one layer. On the face recognition task,
FaceResNet with SCSC can improve 2.7% with 68% fewer FLOPs and 79% fewer
parameters. On the ImageNet classification task, Swin Transformer with SCSC can
achieve even better performance with 22% fewer FLOPs, and ResNet with CSCS can
improve 5.3% with similar complexity. Furthermore, a traditional network (e.g.,
ResNet) embedded with SCSC can match Swin Transformer's performance.Comment: ICCV2023 Workshop (New Ideas in Vision Transformers
Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study
Robotic skill learning has been increasingly studied but the demonstration
collections are more challenging compared to collecting images/videos in
computer vision and texts in natural language processing. This paper presents a
skill learning paradigm by using intuitive teleoperation devices to generate
high-quality human demonstrations efficiently for robotic skill learning in a
data-driven manner. By using a reliable teleoperation interface, the da Vinci
Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration
(dS4D) is proposed in this paper. Various manipulation tasks show the system's
effectiveness and advantages in efficiency compared to other interfaces. Using
the collected data for policy learning has been investigated, which verifies
the initial feasibility. We believe the proposed paradigm can facilitate robot
learning driven by high-quality demonstrations and efficiency while generating
them.Comment: 10 pages, 4 figures, accepted by ISER202
Towards Safe Landing of Falling Quadruped Robots Using a 3-DoF Morphable Inertial Tail
Falling cat problem is well-known where cats show their super aerial
reorientation capability and can land safely. For their robotic counterparts, a
similar falling quadruped robot problem, has not been fully addressed, although
achieving safe landing as the cats has been increasingly investigated. Unlike
imposing the burden on landing control, we approach to safe landing of falling
quadruped robots by effective flight phase control. Different from existing
work like swinging legs and attaching reaction wheels or simple tails, we
propose to deploy a 3-DoF morphable inertial tail on a medium-size quadruped
robot. In the flight phase, the tail with its maximum length can self-right the
body orientation in 3D effectively; before touch-down, the tail length can be
retracted to about 1/4 of its maximum for impressing the tail's side-effect on
landing. To enable aerial reorientation for safe landing in the quadruped
robots, we design a control architecture, which has been verified in a
high-fidelity physics simulation environment with different initial conditions.
Experimental results on a customized flight-phase test platform with comparable
inertial properties are provided and show the tail's effectiveness on 3D body
reorientation and its fast retractability before touch-down. An initial falling
quadruped robot experiment is shown, where the robot Unitree A1 with the 3-DoF
tail can land safely subject to non-negligible initial body angles.Comment: 7 pages, 8 figures, submit to ICRA202
Discovering Physical Interaction Vulnerabilities in IoT Deployments
Internet of Things (IoT) applications drive the behavior of IoT deployments
according to installed sensors and actuators. It has recently been shown that
IoT deployments are vulnerable to physical interactions, caused by design flaws
or malicious intent, that can have severe physical consequences. Yet, extant
approaches to securing IoT do not translate the app source code into its
physical behavior to evaluate physical interactions. Thus, IoT consumers and
markets do not possess the capability to assess the safety and security risks
these interactions present. In this paper, we introduce the IoTSeer security
service for IoT deployments, which uncovers undesired states caused by physical
interactions. IoTSeer operates in four phases (1) translation of each actuation
command and sensor event in an app source code into a hybrid I/O automaton that
defines an app's physical behavior, (2) combining apps in a novel composite
automaton that represents the joint physical behavior of interacting apps, (3)
applying grid-based testing and falsification to validate whether an IoT
deployment conforms to desired physical interaction policies, and (4)
identification of the root cause of policy violations and proposing patches
that guide users to prevent them. We use IoTSeer in an actual house with 13
actuators and six sensors with 37 apps and demonstrate its effectiveness and
performance
Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study
Learning high-performance deep neural networks for dynamic modeling of high
Degree-Of-Freedom (DOF) robots remains challenging due to the sampling
complexity. Typical unknown system disturbance caused by unmodeled dynamics
(such as internal compliance, cables) further exacerbates the problem. In this
paper, a novel framework characterized by both high data efficiency and
disturbance-adapting capability is proposed to address the problem of modeling
gravitational dynamics using deep nets in feedforward gravity compensation
control for high-DOF master manipulators with unknown disturbance. In
particular, Feedforward Deep Neural Networks (FDNNs) are learned from both
prior knowledge of an existing analytical model and observation of the robot
system by Knowledge Distillation (KD). Through extensive experiments in
high-DOF master manipulators with significant disturbance, we show that our
method surpasses a standard Learning-from-Scratch (LfS) approach in terms of
data efficiency and disturbance adaptation. Our initial feasibility study has
demonstrated the potential of outperforming the analytical teacher model as the
training data increases
Model-Free Large-Scale Cloth Spreading With Mobile Manipulation: Initial Feasibility Study
Cloth manipulation is common in domestic and service tasks, and most studies
use fixed-base manipulators to manipulate objects whose sizes are relatively
small with respect to the manipulators' workspace, such as towels, shirts, and
rags. In contrast, manipulation of large-scale cloth, such as bed making and
tablecloth spreading, poses additional challenges of reachability and
manipulation control. To address them, this paper presents a novel framework to
spread large-scale cloth, with a single-arm mobile manipulator that can solve
the reachability issue, for an initial feasibility study. On the manipulation
control side, without modeling highly deformable cloth, a vision-based
manipulation control scheme is applied and based on an online-update Jacobian
matrix mapping from selected feature points to the end-effector motion. To
coordinate the control of the manipulator and mobile platform, Behavior Trees
(BTs) are used because of their modularity. Finally, experiments are conducted,
including validation of the model-free manipulation control for cloth spreading
in different conditions and the large-scale cloth spreading framework. The
experimental results demonstrate the large-scale cloth spreading task
feasibility with a single-arm mobile manipulator and the model-free deformation
controller.Comment: 6 pages, 6 figures, submit to CASE202
Detection of Novel Variations Related to Litter Size in BMP15 Gene of Luzhong Mutton Sheep ( Ovis aries )
SIMPLE SUMMARY: BMP15 is a critical gene in sheep reproduction. Most of its variations have been reported in European sheep. In this study, the entire open reading frame (ORF) region of BMP15 was sequenced in 154 Luzhong mutton sheep. Among 13 identified variations, six were novel. Four SNPs (ENSOART00000010201.1:c.352+342C>A, c.352+1232T>C, c.352+1165A>G and c.353-2036T>A) were significantly associated with litter size, and could be used as candidate genetic markers for improving litter size. The results also suggested possible interaction between BMP15 and FecB/GDF9. ABSTRACT: Litter size is an important economic trait in the mutton sheep industry. BMP15 is one of the key candidate genes for litter size in sheep. In this study, the entire ORF region of BMP15 was sequenced in 154 Luzhong mutton ewes, and the novel variations were determined. The association between polymorphism in BMP15 and litter size was analyzed using a general linear model. Six out of a total of thirteen variations were identified to be novel. Association analysis indicated that four (SNPs ENSOART00000010201.1:c.352+342C>A, c.352+1232T>C, c.352+1165A>G and c.353-2036T>A) were significantly associated with litter size. The joint analysis among three major genes (BMP15, BMPR1B and GDF9) exhibited significant interaction effects in three combinations (FecB and c.352+1232T>C of BMP15; FecB and c.352+1165A>G of BMP15; c.352+342C>A of BMP15 and ENSOART00000014382.1:c.994G>A of GDF9). For the SNPs c.352+1232T>C and c.352+342C>A, the global distribution of allele frequencies showed that the highest variation frequency occurs in Western Europe. In conclusion, the results demonstrated that BMP15 is a major gene for litter size in Luzhong mutton sheep and candidate SNPs associated with litter size were identified
Operational Space Control for Planar PAN–1 Underactuated Manipulators Using Orthogonal Projection and Quadratic Programming
In this paper, we propose an operational space control formulation for a planar N-link underactuated manipulator (PA N–1 ) 1 with a passive first joint subject to actuator constraints (N ⩾ 3), covering both stabilization and tracking tasks. Such underactuated manipulators have an inherent first-order nonholonomic constraint, allowing us to project their dynamics to a space consistent with the nonholonomic constraint. Based on the constrained dynamics, we can design operational space controllers with respect to tasks assuming that all joints of the manipulator are active. Due to underactuation, we design a Quadratic Programming (QP) based controller to minimize the error between the desired torque commands and available motor torques in the null space of the constraint, as well as involve the constraint of motor outputs. The proposed control framework was demonstrated by stabilization and tracking tasks in simulations with both planar PA 2 and PA 3 manipulators. Furthermore, we verified the controller experimentally using a planar PA 2 robot
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