4 papers
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…
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…
REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
Daniel Sliwowski, Shail Jadav, Sergej Stanovcic +3
Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly adv…
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…