arXiv subjects

Shanshan Guo

Publications and source records attributed to Shanshan Guo.

9 recordsLinked to original sources

arXiv2025

ActionSink: Toward Precise Robot Manipulation with Dynamic Integration of Action Flow

Language-instructed robot manipulation has garnered significant interest due to the potential of learning from collected data. While the challenges in high-level perception and planning are continually addressed along the progress of general large pre-trained models, the low precision of low-level action estimation has emerged as the key limiting factor in manipulation performance. To this end, this paper introduces a novel robot manipulation framework, i.e., ActionSink, to pave the way toward precise action estimations in the field of learning-based robot manipulation. As the name suggests, ActionSink reformulates the actions of robots as action-caused optical flows from videos, called "action flow", in a self-supervised manner, which are then used to be retrieved and integrated to enhance the action estimation. Specifically, ActionSink incorporates two primary modules. The first module is a coarse-to-fine action flow matcher, which continuously refines the accuracy of action flow via iterative retrieval and denoising process. The second module is a dynamic action flow integrator, which employs a working memory pool that dynamically and efficiently manages the historical action flows that should be used to integrate to enhance the current action estimation. In this module, a multi-layer fusion module is proposed to integrate direct estimation and action flows from both the current and the working memory, achieving highly accurate action estimation through a series of estimation-integration processes. Our ActionSink framework outperformed prior SOTA on the LIBERO benchmark by a 7.9\% success rate, and obtained nearly an 8\% accuracy gain on the challenging long-horizon visual task LIBERO-Long.

cs.CV↗

arXiv2024

Conflict-free chromatic index of trees

A graph $G$ is conflict-free $k$-edge-colorable if there exists an assignment of $k$ colors to $E(G)$ such that for every edge $e\in E(G)$, there is a color that is assigned to exactly one edge among the closed neighborhood of $e$. The smallest $k$ such that $G$ is conflict-free $k$-edge-colorable is called the conflict-free chromatic index of $G$, denoted $\chi'_{CF}(G)$. D\c{e}bski and Przyby\a{l}o showed that $2\le\chi'_{CF}(T)\le 3$ for every tree $T$ of size at least two. In this paper, we present an algorithm to determine the conflict-free chromatic index of a tree without 2-degree vertices, in time $O(|V(T)|)$. This partially answer a question raised by Kamyczura, Meszka and Przyby\a{l}o.

cs.DM↗

arXiv2024

A Continuous sEMG-Based Prosthetic Hand Control System Without Motion or Force Sensors

Regressively-based surface electromyography (sEMG) prosthetics are widely used for their ability to continuously convert muscle activity into finger force and motion. However, they typically require additional kinematic or dynamic sensors, which increases complexity and limits practical application. To address this, this paper proposes a method based on the simplified near-linear relationship between sEMG and finger force, using the near-linear model ResDD proposed in this work. By applying the principle that a line can be determined by two points, we eliminate the need for complex sensor calibration. Specifically, by recording the sEMG during maximum finger flexion and extension, and assigning corresponding forces of 1 and -1, the ResDD model can fit the simplified relationship between sEMG signals and force, enabling continuous prediction and control of finger force and gestures. Offline experiments were conducted to evaluate the model's classification accuracy and its ability to learn sufficient information. It uses interpolation analysis to open up the internal structure of the trained model and checks whether the fitted curve of the model conforms to the nearly linear relationship between sEMG and force. Finally, online control and sine wave tracking experiments were carried out to further verify the practicality of the proposed method. The results show that the method effectively extracts meaningful information from sEMG and accurately decodes them. The near-linear model sufficiently reflects the expected relationship between sEMG and finger force. Fitting this simplified near-linear relationship is adequate to achieve continuous and smooth control of finger force and gestures, confirming the feasibility and effectiveness of the proposed approach.

cs.RO↗

arXiv2023

Short rainbow cycles in edge-colored graphs

A famous conjecture of Caccetta and Häggkvist (CHC) states that a directed graph $D$ with $n$ vertices and minimum outdegree at least $r$ has a directed cycle of length at most $\lceil \frac{n}{r}\rceil$. In 2017, Aharoni proposed the following generalization: an edge-colored graph $G$ with $n$ vertices, $n$ color classes of size at least $r$ has a rainbow cycle of length at most $\lceil \frac{n}{r}\rceil$. Since CHC can be seen as the case of Aharoni's Conjecture: color classes in the color partition are monochromatic stars centered at distinct vertices, one way to study Aharoni's Conjecture is to structure the color classes as each color class is either a star, a triangle or contains a matching of size 2. Guo improved the upper bound in Aharoni's Conjecture to $O(\log n)$ in some mixed cases when the color classes are not necessarily stars. In this paper, we extend Guo's results. Our main result is as follows: Let $G$ an edge-colored graph on $n$ vertices and $n$ color classes, if at least $αn$ color classes are either a matching of size 2 or a triangle for $α>\frac{1}{2}$, then $G$ contains a rainbow cycle of length $O(\log n)$. We also prove that the $\log n$ bound is the right order of magnitude.

math.CO↗

arXiv2023

Properly colored even cycles in edge-colored complete balanced bipartite graphs

Consider a complete balanced bipartite graph $K_{n,n}$ and let $K^c_{n,n}$ be an edge-colored version of $K_{n,n}$ that is obtained from $K_{n,n}$ by having each edge assigned a certain color. A subgraph $H$ of $K^c_{n,n}$ is called properly colored (PC) if every two adjacent edges of $H$ have distinct colors. $K_{n,n}^c$ is called properly vertex-even-pancyclic if for every vertex $u\in V(K_{n,n}^c)$ and for every even integer $k$ with $4 \leq k \leq 2n$, there exists a PC $k$-cycle containing $u$. The minimum color degree $δ^c(K^c_{n,n})$ of $K^c_{n,n}$ is the largest integer $k$ such that for every vertex $v$, there are at least $k$ distinct colors on the edges incident to $v$. In this paper we study the existence of PC even cycles in $K_{n,n}^c$. We first show that, for every integer $t\geq 3$, every $K^c_{n,n}$ with $δ^c(K^c_{n,n})\geq \frac{2n}{3}+t$ contains a PC 2-factor $H$ such that every cycle of $H$ has a length of at least $t$. By using the probabilistic method and absorbing technique, we use the above result to further show that, for every $\varepsilon>0$, there exists an integer $n_0(\varepsilon)$ such that every $K^c_{n,n}$ with $n\geq n_0(\varepsilon)$ is properly vertex-even-pancyclic, provided that $δ^c(K^c_{n,n})\geq (\frac{2}{3}+\varepsilon)n$.

math.CO↗

arXiv2023

CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation

Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots. These environments often include obstacles and pedestrians, making it essential for autonomous agents to possess the capability of self-corrected planning to adjust their actions based on feedback from the surroundings. However, the majority of existing vision-and-language navigation (VLN) methods primarily operate in less realistic simulator settings and do not incorporate environmental feedback into their decision-making processes. To address this gap, we introduce a novel zero-shot framework called CorNav, utilizing a large language model for decision-making and comprising two key components: 1) incorporating environmental feedback for refining future plans and adjusting its actions, and 2) multiple domain experts for parsing instructions, scene understanding, and refining predicted actions. In addition to the framework, we develop a 3D simulator that renders realistic scenarios using Unreal Engine 5. To evaluate the effectiveness and generalization of navigation agents in a zero-shot multi-task setting, we create a benchmark called NavBench. Extensive experiments demonstrate that CorNav consistently outperforms all baselines by a significant margin across all tasks. On average, CorNav achieves a success rate of 28.1\%, surpassing the best baseline's performance of 20.5\%.

cs.CV↗

arXiv2022

Photoacoustic Digital Skin: Generation and Simulation of Human Skin Vascular for Quantitative Image Analysis

Photoacoustic computed tomography (PACT) is a hybrid imaging modality, which combines the high optical contrast of pure optical imaging and the high penetration depth of ultrasound imaging. However, photoacoustic image dataset with good quality and large quantity is lacking. In this paper, we mainly talk about how to generate a practical photoacoustic dataset. Firstly, we extracted 389 3D vessel volumes from CT whole-lung scan database, and enhanced the blood vessel structures. Then for each 3D vessel volume, we embedded it into a three-layer cubic phantom to formulate a skin tissue model, which includes epidermis, dermis, and hypodermis. The vessel volume was placed randomly in dermis layer in 10 different ways. Thus, 3890 3D skin tissue phantoms were generated. Then we assigned optical properties for the four kinds of tissue types. Monte-Carlo optical simulations were deployed to obtain the optical fluence distribution. Then acoustic propagation simulations were deployed to obtain the photoacoustic initial pressure. Universal back-projection algorithm was used to reconstruct the photoacoustic images. This dataset could be used for deep learning-based photoacoustic image reconstruction, classification, registration, quantitative image analysis.

physics.med-ph↗

arXiv2020

Superposed Wave (s-Wave): Accelerating Photoacoustic Simulation

Photoacoustic imaging develops very fast in recent years due to its superior performance in many preclinical and clinical applications. However, it is still in a developing stage, and a lot of experiments have to be performed in a simulation setting. To simulate photoacoustic imaging in a computer, k-Wave is currently the most popular MATLAB toolbox. Lots of research groups choose k-Wave toolbox to perform the forward projection process, which also can be described as forward model. However, by solving complex partial differential equation, k-Wave suffers a lot from computation time. To accelerate photoacoustic simulation, in this paper, we propose a straightforward simulation approach based on superposed Wave (s-Wave). Specifically, we treat the initial pressure distribution as a set of single pixels. Then by pre-obtaining a standard sensor data from single pixel, we can easily use loop and multiplication operators to change phase and amplitude of sensor data for given pixels. We use three different 2D samples and two 3D samples to test the time cost. The result of our proposed s-Wave method shows much less time consumption compared with k-wave. Especially in a sparse configuration in 3D, s-Wave is more than 2000 times faster than k-Wave, whiling getting nearly same sensor data.

physics.med-ph↗

arXiv2020

Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint

Based on photoacoustic effect, photoacoustic tomography is developing very fast in recent years, and becoming an important imaging tool for both preclinical and clinical studies. With enough ultrasound transducers placed around the biological tissue, PAT can provide both deep penetration and high image contrast by hybrid usage of light and sound. However, considering space and measurement environmental limitations, transducers are always placed in a limited-angle way, which means that the other side without transducer coverage suffers severe information loss. With conventional image reconstruction algorithms, the limited-view tissue induces artifacts and information loss, which may cause doctors misdiagnosis or missed diagnosis. In order to solve limited-view PA imaging reconstruction problem, we propose to use both time domain and frequency domain reconstruction algorithms to get delay-and-sum (DAS) image inputs and k-space image inputs. These dual domain images share nearly same texture information but different artifact information, which can teach network how to distinguish these two kinds of information at input level. In this paper, we propose Dual Domain Unet (DuDoUnet) with specially designed Information Sharing Block (ISB), which can further share two domains' information and distinguish artifacts. Besides, we use mutual information (MI) with an auxiliary network, whose inputs and outputs are both ground truth, to compensate prior knowledge of limited-view PA inputs. The proposed method is verified with a public clinical database, and shows superior results with SSIM = 93.5622% and PSNR = 20.8859.

eess.IV↗