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Charlie Yang's Outputs (14)

MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning (2023)
Presentation / Conference Contribution

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlook... Read More about MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning.

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.

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.

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.

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.