Skip to main content

Research Repository

Advanced Search

Outputs (23)

A bio-inspired multi-functional tendon-driven tactile sensor and application in obstacle avoidance using reinforcement learning (2023)
Journal Article
Lu, Z., Zhao, Z., Yue, T., Zhu, X., & Wang, N. (in press). A bio-inspired multi-functional tendon-driven tactile sensor and application in obstacle avoidance using reinforcement learning. IEEE Transactions on Cognitive and Developmental Systems, https://doi.org/10.1109/tcds.2023.3297361

This paper presents a new bio-inspired tactile sensor that is multi-functional and has different sensitivity contact areas. The TacTop area is sensitive and is used for object classification when there is a direct contact. On the other hand, the TacS... Read More about A bio-inspired multi-functional tendon-driven tactile sensor and application in obstacle avoidance using reinforcement learning.

Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation (2023)
Journal Article
Lu, Z., Wang, N., Si, W., & Yang, C. (in press). Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2023.3292553

In this paper, a distributed observer-based prescribed performance control method is proposed for using a multi-robot teleoperation system to manipulate a common deformable object. To achieve a stable position-tracking effect and realize the desired... Read More about Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation.

Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton (2023)
Journal Article
Lu, Z., Chen, L., Dai, H., Li, H., Zhao, Z., Zheng, B., …Yang, C. (2023). Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton. IEEE Robotics and Automation Letters, 8(9), 5384-5391. https://doi.org/10.1109/lra.2023.3295296

The soft fingers and strategic grasping skills enable the human hands to grasp objects in a stable manner. This letter is to model human grasping skills and transfer the learned skills to robots to improve grasping quality and success rate. First, we... Read More about Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton.

A constrained framework based on IBLF for robot learning with human supervision (2023)
Journal Article
Shi, D., Li, Q., Yang, C., & Lu, Z. (2023). A constrained framework based on IBLF for robot learning with human supervision. Robotica, 41(8), 2451-2463. https://doi.org/10.1017/S0263574723000462

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce th... Read More about A constrained framework based on IBLF for robot learning with human supervision.

Handheld device design for robotic teleoperation based on multi-sensor fusion (2023)
Conference Proceeding
Xie, L., Huang, D., Lu, Z., Wang, N., & Yang, C. (2023). Handheld device design for robotic teleoperation based on multi-sensor fusion. In 2023 IEEE International Conference on Mechatronics (ICM). https://doi.org/10.1109/ICM54990.2023.10102054

Precise leader-follower control is critical for teleop- eration. This paper designs and implements a low-cost leader device for unilateral teleoperation scenario. Monocular vision based on fiducial markers and MEMS Inertial Measurement Unit (IMU) are... Read More about Handheld device design for robotic teleoperation based on multi-sensor fusion.

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.

Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor (2022)
Conference Proceeding
Zhao, Z., & Lu, Z. (2022). Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2099-2104). https://doi.org/10.1109/iros47612.2022.9981477

In this paper, we create a new tendon-connected multi-functional optical tactile sensor, MechTac, for object perception in the field of view (TacTip) and location of touching points in the blind area of vision (TacSide). In a multi-point touch task,... Read More about Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor.

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 robotic learning and generalization framework for curved surface based on modified DMP (2022)
Journal Article
Xue, X., Dong, J., Lu, Z., & Wang, N. (2023). A robotic learning and generalization framework for curved surface based on modified DMP. Robotics and Autonomous Systems, 160, 104323. https://doi.org/10.1016/j.robot.2022.104323

How to reproduce and generalize the skills acquired by demonstrating is a hot topic for researchers. (1) A compliant continuous drag demonstration system based on discrete admittance model was designed to continuously and smoothly drag or demonstrate... Read More about A robotic learning and generalization framework for curved surface based on modified DMP.