225 research outputs found
TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors
Tactile perception stands as a critical sensory modality for human
interaction with the environment. Among various tactile sensor techniques,
optical sensor-based approaches have gained traction, notably for producing
high-resolution tactile images. This work explores gel elastomer deformation
simulation through a physics-based approach. While previous works in this
direction usually adopt the explicit material point method (MPM), which has
certain limitations in force simulation and rendering, we adopt the finite
element method (FEM) and address the challenges in penetration and mesh
distortion with incremental potential contact (IPC) method. As a result, we
present a simulator named TacIPC, which can ensure numerically stable
simulations while accommodating direct rendering and friction modeling. To
evaluate TacIPC, we conduct three tasks: pseudo-image quality assessment,
deformed geometry estimation, and marker displacement prediction. These tasks
show its superior efficacy in reducing the sim-to-real gap. Our method can also
seamlessly integrate with existing simulators. More experiments and videos can
be found in the supplementary materials and on the website:
https://sites.google.com/view/tac-ipc
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at detecting the primary
objects in a given video sequence without any human interposing. Most existing
methods rely on two-stream architectures that separately encode the appearance
and motion information before fusing them to identify the target and generate
object masks. However, this pipeline is computationally expensive and can lead
to suboptimal performance due to the difficulty of fusing the two modalities
properly. In this paper, we propose a novel UVOS model called SimulFlow that
simultaneously performs feature extraction and target identification, enabling
efficient and effective unsupervised video object segmentation. Concretely, we
design a novel SimulFlow Attention mechanism to bridege the image and motion by
utilizing the flexibility of attention operation, where coarse masks predicted
from fused feature at each stage are used to constrain the attention operation
within the mask area and exclude the impact of noise. Because of the
bidirectional information flow between visual and optical flow features in
SimulFlow Attention, no extra hand-designed fusing module is required and we
only adopt a light decoder to obtain the final prediction. We evaluate our
method on several benchmark datasets and achieve state-of-the-art results. Our
proposed approach not only outperforms existing methods but also addresses the
computational complexity and fusion difficulties caused by two-stream
architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest
speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow
also obtains competitive results on video salient object detection datasets.Comment: Accepted to ACM MM 202
Intersection-free Robot Manipulation with Soft-Rigid Coupled Incremental Potential Contact
This paper presents a novel simulation platform, ZeMa, designed for robotic
manipulation tasks concerning soft objects. Such simulation ideally requires
three properties: two-way soft-rigid coupling, intersection-free guarantees,
and frictional contact modeling, with acceptable runtime suitable for deep and
reinforcement learning tasks. Current simulators often satisfy only a subset of
these needs, primarily focusing on distinct rigid-rigid or soft-soft
interactions. The proposed ZeMa prioritizes physical accuracy and integrates
the incremental potential contact method, offering unified dynamics simulation
for both soft and rigid objects. It efficiently manages soft-rigid contact,
operating 75x faster than baseline tools with similar methodologies like
IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp
generation, penetrated grasp repair, and reinforcement learning for grasping,
successfully transferring the trained RL policy to real-world scenarios
UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding
This paper explores the development of UniFolding, a sample-efficient,
scalable, and generalizable robotic system for unfolding and folding various
garments. UniFolding employs the proposed UFONet neural network to integrate
unfolding and folding decisions into a single policy model that is adaptable to
different garment types and states. The design of UniFolding is based on a
garment's partial point cloud, which aids in generalization and reduces
sensitivity to variations in texture and shape. The training pipeline
prioritizes low-cost, sample-efficient data collection. Training data is
collected via a human-centric process with offline and online stages. The
offline stage involves human unfolding and folding actions via Virtual Reality,
while the online stage utilizes human-in-the-loop learning to fine-tune the
model in a real-world setting. The system is tested on two garment types:
long-sleeve and short-sleeve shirts. Performance is evaluated on 20 shirts with
significant variations in textures, shapes, and materials. More experiments and
videos can be found in the supplementary materials and on the website:
https://unifolding.robotflow.aiComment: CoRL 202
Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods
As is well known, differential algebraic equations (DAEs), which are able to
describe dynamic changes and underlying constraints, have been widely applied
in engineering fields such as fluid dynamics, multi-body dynamics, mechanical
systems and control theory. In practical physical modeling within these
domains, the systems often generate high-index DAEs. Classical implicit
numerical methods typically result in varying order reduction of numerical
accuracy when solving high-index systems.~Recently, the physics-informed neural
network (PINN) has gained attention for solving DAE systems. However, it faces
challenges like the inability to directly solve high-index systems, lower
predictive accuracy, and weaker generalization capabilities. In this paper, we
propose a PINN computational framework, combined Radau IIA numerical method
with a neural network structure via the attention mechanisms, to directly solve
high-index DAEs. Furthermore, we employ a domain decomposition strategy to
enhance solution accuracy. We conduct numerical experiments with two classical
high-index systems as illustrative examples, investigating how different orders
of the Radau IIA method affect the accuracy of neural network solutions. The
experimental results demonstrate that the PINN based on a 5th-order Radau IIA
method achieves the highest level of system accuracy. Specifically, the
absolute errors for all differential variables remains as low as , and
the absolute errors for algebraic variables is maintained at ,
surpassing the results found in existing literature. Therefore, our method
exhibits excellent computational accuracy and strong generalization
capabilities, providing a feasible approach for the high-precision solution of
larger-scale DAEs with higher indices or challenging high-dimensional partial
differential algebraic equation systems
Evaluation of cloned cells, animal model, and ATRA sensitivity of human testicular yolk sac tumor
The testicular yolk sac tumor (TYST) is the most common neoplasm originated from germ cells differentiated abnormally, a major part of pediatric malignant testicular tumors. The present study aimed at developing and validating the in vitro and vivo models of TYST and evaluating the sensitivity of TYST to treatments, by cloning human TYST cells and investigating the histology, ultra-structure, growth kinetics and expression of specific proteins of cloned cells. We found biological characteristics of cloned TYST cells were similar to the yolk sac tumor and differentiated from the columnar to glandular-like or goblet cells-like cells. Chromosomes for tumor identification in each passage met nature of the primary tumor. TYST cells were more sensitive to all-trans-retinoic acid which had significantly inhibitory effects on cell proliferation. Cisplatin induced apoptosis of TYST cells through the activation of p53 expression and down-regulation of Bcl- expression. Thus, we believe that cloned TYST cells and the animal model developed here are useful to understand the molecular mechanism of TYST cells and develop potential therapies for human TYST
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