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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

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.

Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment (2022)
Presentation / Conference Contribution

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

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

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 novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use (2022)
Presentation / Conference Contribution

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

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.

Multifingered robot hand compliant manipulation based on vision-based demonstration and adaptive force control (2022)
Journal Article

Multifingered hand dexterous manipulation is quite challenging in the domain of robotics. One remaining issue is how to achieve compliant behaviors. In this work, we propose a human-in-the-loop learning-control approach for acquiring compliant graspi... Read More about Multifingered robot hand compliant manipulation based on vision-based demonstration and adaptive force control.