Outputs (21)

Multi-fingered tactile servoing for grasping adjustment under partial observation (2022)
Presentation / Conference Contribution
Liu, H., Huang, B., Li, Q., Zheng, Y., Ling, Y., Lee, W., Liu, Y., Tsai, Y.-Y., & Yang, C. (2022, October). Multi-fingered tactile servoing for grasping adjustment under partial observation. Presented at 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan

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)
Presentation / Conference Contribution
Wu, B., Lu, Z., & Yang, C. (2022, October). A modified LSTM model for Chinese sign language recognition using leap motion. Presented at 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic

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)
Presentation / Conference Contribution
Si, W., Yue, T., Guan, Y., Wang, N., & Yang, C. (2022, August). A novel robot skill learning framework based on bilateral teleoperation. Presented at 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico

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)
Presentation / Conference Contribution
Kong, H., Li, G., & Yang, C. (2022, September). Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, UK

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.

Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration (2022)
Presentation / Conference Contribution
Dai, H., Lu, Z., He, M., & Yang, C. (2022, September). Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, United Kingdom

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.

Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance (2022)
Presentation / Conference Contribution
Huang, H., Lu, Z., Wang, N., & Yang, C. (2022, September). Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, United Kingdom

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.

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.