4 citations · 6 across the 6 of their papers we have counts for
8 papers
V2-STRep: VLM-Grounded Structured Task Representations for Reusable Robot Skills Acquired from Generated Videos
Yexin Hu, Dongheui Lee
Human manipulation videos provide rich motion and interaction cues for acquiring robot skills without robot demonstrations. Video generation models synthesize such demonstrations f…
Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks
Yexin Hu, Haoyi Zheng, Johannes Heidersberger +1
Learning from demonstration (LfD) enables robots to learn manipulation skills directly from expert demonstrations but remains challenging for contact-rich tasks involving geometric…
Monocular Vision Based Control Framework for Grasping
Shail Jadav, Dongheui Lee
Grasping in unstructured environments requires handling objects with widely different mechanical properties, from soft and deformable items to rigid everyday objects. Most existing…
LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations
Johannes Heidersberger, Shail Jadav, Dongheui Lee
Learning from Demonstration (LfD) enables intuitive robot skill acquisition by allowing robots to learn directly from human task demonstrations. However, current methods often fail…
ConditionNET: Learning Preconditions and Effects for Execution Monitoring
Daniel Sliwowski, Dongheui Lee
The introduction of robots into everyday scenarios necessitates algorithms capable of monitoring the execution of tasks. In this paper, we propose ConditionNET, an approach for lea…
Shared Autonomy via Variable Impedance Control and Virtual Potential Fields for Encoding Human Demonstration
Shail Jadav, Johannes Heidersberger, Christian Ott +1
This article introduces a framework for complex human-robot collaboration tasks, such as the co-manufacturing of furniture. For these tasks, it is essential to encode tasks from hu…
Orientation Control with Variable Stiffness Dynamical Systems
Youssef Michel, Matteo Saveriano, Fares J. Abu-Dakka +1
Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional t…
Input-Output Feedback Linearization Preserving Task Priority for Multivariate Nonlinear Systems Having Singular Input Gain Matrix
Sang-ik An, Dongheui Lee, Gyunghoon Park
We propose an extension of the input-output feedback linearization for a class of multivariate systems that are not input-output linearizable in a classical manner. The key observa…