Nan Liang — accepted AI-conference papers

2022

A Dataset and Benchmark for Learning the Kinematics of Concentric Tube Continuum Robots

Reinhard M. Grassmann, Ryan Zeyuan Chen, Nan Liang, Jessica Burgner-Kahrs

IROS 2022poster

Establishing a physics-based model capturing the kinetostatic behavior of concentric tube continuum robots is challenging as elastic interactions between the flexible tubes constituting the robot result in a highly non-linear problem. The Goldstandard physics-based model using the Cosserat theory of…

Cited by 12 Source ↗code ↗Scholar ↗

2021

Learning-based Inverse Kinematics from Shape as Input for Concentric Tube Continuum Robots

Nan Liang, Reinhard M. Grassmann, Sven Lilge, Jessica Burgner-Kahrs

ICRA 2021poster

We introduce a methodology to compute the inverse kinematics for concentric tube continuum robots from a desired shape as input. We demonstrate that it is possible to accurately learn joint parameters using neural networks for a discrete point-wise shape representation with different discretization.…

Cited by 21 Source ↗Scholar ↗