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Ying Wu

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15 papers
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15

IROS Conference 2025 Conference Paper

RMCC: Rigid Multi-joint Coupled Continuum Structure for Bionic Robots

  • Zida Zhou
  • Ying Wu
  • Zujian Chen
  • Zetong Bi
  • Hui Cheng

Continuum robots, inspired by biological structures such as spines and tails, have attracted significant attention due to their flexibility and ability to perform complex tasks in confined and dynamic environments. However, traditional flexible continuum robots often encounter challenges such as non-linearity, hysteresis, and limited load-bearing capacity, which can compromise their precision and effectiveness in practical applications. To address these limitations, this paper presents a novel bionic continuum mechanism: Rigid Multi-joint Coupled Continuum Structure(RMCC), which employs a rigid mechanical transmission mode to couple all joints, achieving coordinated movement of multiple joints. Its rigid structural composition and transmission method provide it with high precision and load capacity. The coordinated motion of the joints endows it with the dexterity of a continuum mechanism, while also enabling efficient and precise control with a minimal number of motors. The modular joint design improves the system’s scalability and adaptability, enabling a wide range of configurations to suit diverse robotic applications. The feasibility and effectiveness of the proposed system are validated through a series of bio-inspired experiments, including lizardlike crawling, falling-cat movement, and adaptive grasping like birds. The experimental results confirm that the RMCC exhibits the flexibility and adaptability of animals, demonstrating its potential for diverse bionic robotics applications.

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ICRA Conference 2024 Conference Paper

Robust and Energy-Efficient Control for Multi-task Aerial Manipulation with Automatic Arm-switching

  • Ying Wu
  • Zida Zhou
  • Mingxin Wei
  • Hui Cheng

Aerial manipulation has received increasing research interest with wide applications of drones. To perform specific tasks, robotic arms with various mechanical structures will be mounted on the drone. It results in sudden disturbances to the aerial manipulator when switching the robotic arm or interacting with the environment. Hence, it is challenging to design a generic and robust control strategy adapted to various robotic arms when achieving multi-task aerial manipulation. In this paper, we present a learning-based control algorithm that allows online trajectory optimization and tracking to accomplish various aerial interaction tasks without manual adjustment. The proposed energy-saved trajectory planning approach integrates coupled dynamics model with a single rigid body to generate the energy-efficient trajectory for the aerial manipulator. Addressing the challenges of precise control when performing aerial manipulation tasks, this paper presents a controller based on deep neural networks that classifies and learns accurate forces and moments caused by different robotic arms and interactions. Moreover, the forces arising from robotic arm motions are delicately used as part of the drone’s power to save energy. Extensive real-world experiments demonstrate that the proposed method can adapt to various robotic arms and interactions when performing multi-task aerial manipulation.

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NeurIPS Conference 2023 Conference Paper

SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis

  • Yuanshao Zhu
  • Yongchao Ye
  • Ying Wu
  • Xiangyu Zhao
  • James Yu

Urban mobility analysis has been extensively studied in the past decade using a vast amount of GPS trajectory data, which reveals hidden patterns in movement and human activity within urban landscapes. Despite its significant value, the availability of such datasets often faces limitations due to privacy concerns, proprietary barriers, and quality inconsistencies. To address these challenges, this paper presents a synthetic trajectory dataset with high fidelity, offering a general solution to these data accessibility issues. Specifically, the proposed dataset adopts a diffusion model as its synthesizer, with the primary aim of accurately emulating the spatial-temporal behavior of the original trajectory data. These synthesized data can retain the geo-distribution and statistical properties characteristic of real-world datasets. Through rigorous analysis and case studies, we validate the high similarity and utility between the proposed synthetic trajectory dataset and real-world counterparts. Such validation underscores the practicality of synthetic datasets for urban mobility analysis and advocates for its wider acceptance within the research community. Finally, we publicly release the trajectory synthesizer and datasets, aiming to enhance the quality and availability of synthetic trajectory datasets and encourage continued contributions to this rapidly evolving field. The dataset is released for public online availability https: //github. com/Applied-Machine-Learning-Lab/SynMob.

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NeurIPS Conference 2023 Conference Paper

TOA: Task-oriented Active VQA

  • xiaoying xing
  • Mingfu Liang
  • Ying Wu

Knowledge-based visual question answering (VQA) requires external knowledge to answer the question about an image. Early methods explicitly retrieve knowledge from external knowledge bases, which often introduce noisy information. Recently large language models like GPT-3 have shown encouraging performance as implicit knowledge source and revealed planning abilities. However, current large language models can not effectively understand image inputs, thus it remains an open problem to extract the image information and input to large language models. Prior works have used image captioning and object descriptions to represent the image. However, they may either drop the essential visual information to answer the question correctly or involve irrelevant objects to the task-of-interest. To address this problem, we propose to let large language models make an initial hypothesis according to their knowledge, then actively collect the visual evidence required to verify the hypothesis. In this way, the model can attend to the essential visual information in a task-oriented manner. We leverage several vision modules from the perspectives of spatial attention (i. e. , Where to look) and attribute attention (i. e. , What to look), which is similar to human cognition. The experiments show that our proposed method outperforms the baselines on open-ended knowledge-based VQA datasets and presents clear reasoning procedure with better interpretability.

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JBHI Journal 2022 Journal Article

Flexible Brain Transitions Between Hierarchical Network Segregation and Integration Associated With Cognitive Performance During a Multisource Interference Task

  • Rong Wang
  • Xiaoli Su
  • Zhao Chang
  • Pan Lin
  • Ying Wu

Cognition involves locally segregated and globally integrated processing. This process is hierarchically organized and linked to evidence from hierarchical modules in brain networks. However, researchers have not clearly determined how flexible transitions between these hierarchical processes are associated with cognitive performance. Here, we designed a multisource interference task (MSIT) and introduced the nested-spectral partition (NSP) method to detect hierarchical modules in brain functional networks. By defining hierarchical segregation and integration across multiple levels, we showed that the MSIT requires higher network segregation in the whole brain and most functional systems but generates higher integration in the control system. Meanwhile, brain networks have more flexible transitions between segregated and integrated configurations in the task state. Crucially, higher functional flexibility in the resting state, less flexibility in the task state and more efficient switching of the brain from resting to task states were associated with better task performance. Our hierarchical modular analysis was more effective at detecting alterations in functional organization and the phenotype of cognitive performance than graph-based network measures at a single level.

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AAAI Conference 2016 Conference Paper

Learning FRAME Models Using CNN Filters

  • Yang Lu
  • Song-Chun Zhu
  • Ying Wu

The convolutional neural network (ConvNet or CNN) has proven to be very successful in many tasks such as those in computer vision. In this conceptual paper, we study the generative perspective of the discriminative CNN. In particular, we propose to learn the generative FRAME (Filters, Random field, And Maximum Entropy) model using the highly expressive filters pre-learned by the CNN at the convolutional layers. We show that the learning algorithm can generate realistic and rich object and texture patterns in natural scenes. We explain that each learned model corresponds to a new CNN unit at a layer above the layer of filters employed by the model. We further show that it is possible to learn a new layer of CNN units using a generative CNN model, which is a product of experts model, and the learning algorithm admits an EM interpretation with binary latent variables.

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