All Outputs (7)

MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning (2023)
Conference Proceeding
Lu, Z., Yue, T., Zhao, Z., Si, W., Wang, N., & Yang, C. (2023). MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning. In IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. https://doi.org/10.1109/iecon51785.2023.10311762

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlook... Read More about MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning.

VERGNet: Visual enhancement guided robotic grasp detection under low-light condition (2023)
Journal Article
Niu, M., Lu, Z., Chen, L., Yang, J., & Yang, C. (2023). VERGNet: Visual enhancement guided robotic grasp detection under low-light condition. IEEE Robotics and Automation Letters, 8(12), 8541-8548. https://doi.org/10.1109/lra.2023.3330664

Although existing grasp detection methods have achieved encouraging performance under well-light conditions, repetitive experiments have found that the detection performance would deteriorate drastically under low-light conditions. Although supplemen... Read More about VERGNet: Visual enhancement guided robotic grasp detection under low-light condition.

Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning (2023)
Journal Article
Jin, Z., Si, W., Liu, A., Zhang, W. A., Yu, L., & Yang, C. (in press). Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning. IEEE Transactions on Robotics, https://doi.org/10.1109/tro.2023.3303011

Learning a stable autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for demonstration learning. However, the stability guarantee may sacrifice the demonstration learning accuracy. This article solves the i... Read More about Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning.

Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network (2023)
Journal Article
Liu, X., Huang, C., Li, J., Wan, W., & Yang, C. (2024). Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network. IEEE Transactions on Cognitive and Developmental Systems, 16(2), 720 - 731. https://doi.org/10.1109/tcds.2023.3289987

When human beings see different objects, they can quickly make correct grasping strategies through brain decisions. However, grasp, as the first step of most manipulation tasks, is still an open issue in robotics. Although many detection methods have... Read More about Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network.

Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems (2023)
Journal Article
Yi, J., Li, J., & Yang, C. (2023). Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems. IEEE Transactions on Fuzzy Systems, 31(10), 3346-3357. https://doi.org/10.1109/tfuzz.2023.3252601

An adaptive fuzzy prescribed-time connectivity-preserving consensus protocol is designed for a class of stochastic nonstrict-feedback multiagent systems, in which periodic disturbances, switched nonlinearities, input saturation, and limited communica... Read More about Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems.

Human-in-the-loop Learning and Control for Robot Teleoperation (2023)
Book
Yang, C., Luo, J., & Wang, N. (2023). Human-in-the-loop Learning and Control for Robot Teleoperation. Elsevier. https://doi.org/10.1016/C2021-0-02620-1

Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning tech... Read More about Human-in-the-loop Learning and Control for Robot Teleoperation.

Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation (2022)
Book
Li, Q., Luo, S., Chen, Z., Zhang, J., & Yang, C. (2022). Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation. https://doi.org/10.1016/C2020-0-02663-0

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces r... Read More about Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation.