2 citations · 2 across the 7 of their papers we have counts for
8 papers
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen +4
Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One es…
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Lingyun Chen, Xinrui Zhao, Marcos P. S. Campanha +4
This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (…
Time-Optimized Trajectory Planning for Non-Prehensile Object Transportation in 3D
Lingyun Chen, Haoyu Yu, Abdeldjallil Naceri +2
Non-prehensile object transportation offers a way to enhance robotic performance in object manipulation tasks, especially with unstable objects. Effective trajectory planning requi…
Identification and validation of the dynamic model of a tendon-driven anthropomorphic finger
Junnan Li, Lingyun Chen, Johannes Ringwald +3
This study addresses the absence of an identification framework to quantify a comprehensive dynamic model of human and anthropomorphic tendon-driven fingers, which is necessary to…
OPENGRASP-LITE Version 1.0: A Tactile Artificial Hand with a Compliant Linkage Mechanism
Sonja Groß, Michael Ratzel, Edgar Welte +6
Recent research has seen notable progress in the development of linkage-based artificial hands. While previous designs have focused on adaptive grasping, dexterity and biomimetic a…
Autonomous and Teleoperation Control of a Drawing Robot Avatar
Lingyun Chen, Abdeldjallil Naceri, Abdalla Swikir +2
A drawing robot avatar is a robotic system that allows for telepresence-based drawing, enabling users to remotely control a robotic arm and create drawings in real-time from a remo…
Learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots
Martin Schonger, Hugo T. M. Kussaba, Lingyun Chen +4
Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations dur…
Towards Task-Specific Modular Gripper Fingers: Automatic Production of Fingertip Mechanics
Johannes Ringwald, Samuel Schneider, Lingyun Chen +4
The number of sequential tasks a single gripper can perform is significantly limited by its design. In many cases, changing the gripper fingers is required to successfully conduct…