Browse

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.

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.

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.

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.

Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage (2022)
Journal Article
Lu, Z., & Wang, N. (in press). Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage. IEEE Robotics and Automation Magazine, 2-13. https://doi.org/10.1109/MRA.2022.3188218

This article presents a novel biomimetic force and impedance adaption framework based on the broad learning system (BLS) for robot control in stable and unstable environments. Different from iterative learning control, the adaptation process is real... Read More about Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage.

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.

Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty (2022)
Journal Article
Liu, X., Chang, H., Huang, P., & Lu, Z. (in press). Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty. IEEE Transactions on Aerospace and Electronic Systems, https://doi.org/10.1109/TAES.2022.3191293

Proximity operation can significantly improve the efficiency of eddy current de-tumbling. However, the tumbling motion and non-cooperation of space debris make the chaser execute collision avoidance maneuvers and be influenced by model uncertainty. I... Read More about Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty.

An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays (2022)
Journal Article
Lu, Z., Guan, Y., & Wang, N. (2022). An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays. IEEE Robotics and Automation Letters, 7(2), 5599-5606. https://doi.org/10.1109/LRA.2022.3158540

Time delay, especially varying time delay, is always an important factor affecting the stability to the human-in-the-loop system. Previous research usually focuses on the performance of the internal signal transmission part, but rarely considers the... Read More about An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays.

Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system (2021)
Journal Article
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system. IEEE Transactions on Fuzzy Systems, 30(6), 1506-1515. https://doi.org/10.1109/tfuzz.2021.3136933

Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extract... Read More about Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system.

DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation (2021)
Journal Article
Lu, Z., Wang, N., & Shi, D. (2022). DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation. Complex and Intelligent Systems, 8, 2873–2882. https://doi.org/10.1007/s40747-021-00429-3

Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations (LfD) is a highly effective paradigm for robotic learning, where a robot learns from human actions directly... Read More about DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2022). A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks. International Journal of Systems Science, 53(1), 25-39. https://doi.org/10.1080/00207721.2021.1936275

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative paramet... Read More about A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks.

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance (2021)
Journal Article
Lu, Z., & Wang, N. (2021). Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance. Assembly Automation, 41(3), 302-311. https://doi.org/10.1108/AA-11-2020-0168

Purpose: Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills change... Read More about Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance.

A constrained DMPs framework for robot skills learning and generalization from human demonstrations (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2021). A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Transactions on Mechatronics, 26(6), 3265 - 3275. https://doi.org/10.1109/TMECH.2021.3057022

Dynamical movement primitives (DMPs) model is a useful tool for efficiently robotic learning manipulation skills from human demonstrations and then generalizing these skills to fulfill new tasks. It is improved and applied for the cases with multiple... Read More about A constrained DMPs framework for robot skills learning and generalization from human demonstrations.

Recent advances in robot-assisted echography: Combining perception, control and cognition (2020)
Journal Article
Lu, Z., Li, M., Annamalai, A., & Yang, C. (2020). Recent advances in robot-assisted echography: Combining perception, control and cognition. Cognitive Computation and Systems, 2(3), 85-92. https://doi.org/10.1049/ccs.2020.0015

Echography imaging is an important technique frequently used in medical diagnostics due to low-cost, non-ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnos... Read More about Recent advances in robot-assisted echography: Combining perception, control and cognition.