All Outputs (120)

Review on human-like robot manipulation using dexterous hands (2023)
Journal Article
Kadalagere Sampath, S., Wang, N., Wu, H., & Yang, C. (2023). Review on human-like robot manipulation using dexterous hands. Cognitive Computation and Systems, 5(1), 14-29. https://doi.org/10.1049/ccs2.12073

In recent years, human hand-based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close t... Read More about Review on human-like robot manipulation using dexterous hands.

A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery (2023)
Journal Article
Ali, Z. A., Yang, C., Israr, A., & Zhu, Q. (2023). A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery. Drones, 7(2), 97. https://doi.org/10.3390/drones7020097

Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our p... Read More about A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery.

A gripper-like exoskeleton design for robot grasping demonstration (2023)
Journal Article
Dai, H., Lu, Z., He, M., & Yang, C. (2023). A gripper-like exoskeleton design for robot grasping demonstration. Actuators, 12(1), 39. https://doi.org/10.3390/act12010039

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and t... Read More about A gripper-like exoskeleton design for robot grasping demonstration.

Deformation-aware contact-rich manipulation skills learning and compliant control (2023)
Conference Proceeding
Si, W., Guo, C., Dong, J., Lu, Z., & Yang, C. (2023). Deformation-aware contact-rich manipulation skills learning and compliant control. In P. Borja, C. D. Santina, L. Peternel, & E. Torta (Eds.), Human-Friendly Robotics 2022 HFR: 15th International Workshop on Human-Friendly Robotics (90-104). https://doi.org/10.1007/978-3-031-22731-8_7

In this paper, we study a vision-based reactive adaptation method for contact-rich manipulation tasks, based on the compliant control and learning from demonstration. For contact-rich tasks, the compliant control method is essential, especially when... Read More about Deformation-aware contact-rich manipulation skills learning and compliant control.

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.

Multi-fingered tactile servoing for grasping adjustment under partial observation (2022)
Conference Proceeding
Liu, H., Huang, B., Li, Q., Zheng, Y., Ling, Y., Lee, W., …Yang, C. (2022). Multi-fingered tactile servoing for grasping adjustment under partial observation. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (7781-7788). https://doi.org/10.1109/IROS47612.2022.9981464

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Ou... Read More about Multi-fingered tactile servoing for grasping adjustment under partial observation.

One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring (2022)
Journal Article
Zhang, D., Fan, W., Lloyd, J., Yang, C., & Lepora, N. F. (2024). One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring. IEEE Transactions on Automation Science and Engineering, 21(1), 541 - 554. https://doi.org/10.1109/TASE.2022.3220728

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unifi... Read More about One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring.

A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control (2022)
Journal Article
Lu, Z., Wang, N., Li, Q., & Yang, C. (2023). A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control. Neurocomputing, 521, 146-159. https://doi.org/10.1016/j.neucom.2022.11.076

Due to changes in the environment and errors that occurred during skill initialization, the robot's operational skills should be modified to adapt to new tasks. As such, skills learned by the methods with fixed features, such as the classical Dynamic... Read More about A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control.

A neural network based framework for variable impedance skills learning from demonstrations (2022)
Journal Article
Zhang, Y., Cheng, L., Cao, R., Li, H., & Yang, C. (2023). A neural network based framework for variable impedance skills learning from demonstrations. Robotics and Autonomous Systems, 160, 104312. https://doi.org/10.1016/j.robot.2022.104312

Robots are becoming standard collaborators not only in factories, hospitals, and offices, but also in people's homes, where they can play an important role in situations where a human cannot complete a task alone or needs the help of another person (... Read More about A neural network based framework for variable impedance skills learning from demonstrations.

A modified LSTM model for Chinese sign language recognition using leap motion (2022)
Conference Proceeding
Wu, B., Lu, Z., & Yang, C. (2022). A modified LSTM model for Chinese sign language recognition using leap motion. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (1612-1617). https://doi.org/10.1109/SMC53654.2022.9945287

At present, there are about 70 million deaf people using sign language in the world, but for most normal people, it is difficult to understand the meaning of the sign language expression. Therefore, it is of great importance to explore the ways of re... Read More about A modified LSTM model for Chinese sign language recognition using leap motion.

A novel robot skill learning framework based on bilateral teleoperation (2022)
Conference Proceeding
Si, W., Yue, T., Guan, Y., Wang, N., & Yang, C. (2022). A novel robot skill learning framework based on bilateral teleoperation. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). https://doi.org/10.1109/case49997.2022.9926526

In this paper, a bilateral teleoperation-based robot skill learning framework is developed to transfer multi-step and contact manipulation skills from humans to robots. Robot skill acquisition via bilateral teleoperation provides a solution for human... Read More about A novel robot skill learning framework based on bilateral teleoperation.

Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment (2022)
Conference Proceeding
Kong, H., Li, G., & Yang, C. (2022). Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment. In 2022 27th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/ICAC55051.2022.9911101

In this paper, a robust fixed-time controller is de-signed for manipulators with unknown dynamics while interacting with environment. To realize compliance of the manipulator to the environment, an admittance model is adopted in the system. In the co... Read More about Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment.

Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance (2022)
Conference Proceeding
Huang, H., Lu, Z., Wang, N., & Yang, C. (2022). Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance. In 2022 27th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/ICAC55051.2022.9911082

A fixed-time adaptive neural network control scheme is designed for an unknown model manipulator system with input saturation and external environment disturbance, so that the system convergence time can be parameterized and not affected by the initi... Read More about Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance.

Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration (2022)
Conference Proceeding
Dai, H., Lu, Z., He, M., & Yang, C. (2022). Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration. In 2022 27th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/ICAC55051.2022.9911096

Learning from demonstration (LfD) has been developed and proved to be a promising method for transferring skill knowledge from human to robot. It is desired to have a demonstration device that can effectively map demonstrations to the robot's motion... Read More about Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration.

A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective (2022)
Journal Article
Zhang, J., Jin, L., Wang, Y., & Yang, C. (2023). A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective. IEEE/ASME Transactions on Mechatronics, 28(1), 128-139. https://doi.org/10.1109/TMECH.2022.3193136

In some task-oriented multimanipulator applications, the system not only needs to complete the main assigned tasks, but also should optimize some subobjectives. In order to tap the redundancy potential of individual manipulators and improve the perfo... Read More about A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective.

A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use (2022)
Conference Proceeding
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use. . https://doi.org/10.1109/ICCA54724.2022.9831826

Dynamic Movement Primitives (DMPs) is a general method for learning skills from demonstrations. Most previous research on DMP has focused on point to point skill learning and training, and the skills learned are usually generalized based on the same... Read More about A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use.

Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems (2022)
Journal Article
Ding, F., Huang, J., Xu, W., Yang, C., Sun, C., & Ai, Y. (2022). Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems. International Journal of Robust and Nonlinear Control, 32(14), https://doi.org/10.1002/rnc.6275

Underactuated systems are extensively utilized in practice while attracting a huge deal of attention in theoretical studies. There are few robust control strategies for general underactuated systems because of the variety of their dynamic models. A d... Read More about Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems.

A review on the techniques used in prostate brachytherapy (2022)
Journal Article
Li, Y., Yang, C., Bahl, A., Persad, R., & Melhuish, C. (2022). A review on the techniques used in prostate brachytherapy. Cognitive Computation and Systems, 4(4), 317-328. https://doi.org/10.1049/ccs2.12067

Prostate brachytherapy is a validated treatment for prostate cancer. During the procedure, the accuracy of needle placement is critical to the treatment’s effectiveness. However, the inserted needle could deflect from the preset trajectory because of... Read More about A review on the techniques used in prostate brachytherapy.

From teleoperation to autonomous robot-assisted microsurgery: A survey (2022)
Journal Article
Zhang, D., Si, W., Fan, W., Guan, Y., & Yang, C. (2022). From teleoperation to autonomous robot-assisted microsurgery: A survey. Machine Intelligence Research, 19, 288–306. https://doi.org/10.1007/s11633-022-1332-5

Robot-assisted microsurgery (RAMS) has many benefits compared to traditional microsurgery. Microsurgical platforms with advanced control strategies, high-quality micro-imaging modalities and micro-sensing systems are worth developing to further enhan... Read More about From teleoperation to autonomous robot-assisted microsurgery: A survey.

E2EK: End-to-end regression network based on keypoint for 6d pose estimation (2022)
Journal Article
Lin, S., Wang, Z., Ling, Y., Tao, Y., & Yang, C. (2022). E2EK: End-to-end regression network based on keypoint for 6d pose estimation. IEEE Robotics and Automation Letters, 7(3), 6526-6533. https://doi.org/10.1109/LRA.2022.3174261

The methods based on deep learning are the mainstream of 6D object pose estimation, which mainly include direct regression and two-stage pipelines. The former are keen by many scholars at first due to their simplicity and differentiability to poses,... Read More about E2EK: End-to-end regression network based on keypoint for 6d pose estimation.