We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation
from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images …

Screw-driving is an important operation in numerous applications. In many situations, hole
pose cannot be estimated very accurately. Autonomous screw-driving cannot be performed …

The accurate simulation of deformable linear object (DLO) dynamics is challenging if the
task at hand requires a human-interpretable and data-efficient model that also yields fast …

Robots are being considered for performing external heating of components in
manufacturing applications. This paper presents a physics-aware action selection policy that …

We present a dynamic position and orientation controller for a hybrid rigid-soft manipulator
arm where the soft arm is extruded from a two degrees-of-freedom rigid link. Our approach …

Accurately predicting deformable linear object (DLO) dynamics is challenging, especially
when the task requires a model that is both human-interpretable and computationally …

Controller design for soft robots is challenging due to nonlinear deformation and high
degrees of freedom of flexible material. The data-driven approach is a promising solution to …

We consider the problem of identifying material parameters of a deformable object, such as
elastic moduli, by non-destructive robotic manipulation. We assume known geometry and …

Controlling deformable linear objects requires reliable models that capture their complex
and high-dimensional dynamics. This paper aims to obtain accurate state-space models of …

The interface between machine learning and control has enabled robots to move outside the
laboratory into challenging real-world settings. Deep reinforcement learning can scale …