Hello, I'm Daniel Sliwowski

I am a PhD at TuWien advised by Prof Dongheui Lee , where I work on understanding human and robot task execution, for better autonomous robot performance and human-robot collaboration.

CV Scholar Github

News

Feb 2025: We are releasing the REASSEMBLE dataset.
Feb 2025: Our ConditionNET paper got accepted at Robotics and Automation Letters!
Dec 2024: Our “Multimodal Transformer Models for Human Action Classification” paper at RiTA has won the reward of the Best Intelligence Paper.
Sep 2024: Our “Multimodal Transformer Models for Human Action Classification” paper was accepted at RiTA.
Sep 2023: HOI4ABOT has been accepted to CoRL 2023.
Sep 2022: I started my Ph.D. studies under Dongheui Lee at the Autonomous Systems Lab in TU Wien!

Publications

REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

Daniel Sliwowski, Shail Jadav, Sergej Stanovcic, Johannes Heidersberger, Jedrzej Orbik, Dongheui Lee

Preprint

We release a multimodal dataset for long-horizon contact-rich assembly and disassembly tasks

Project Page PDF arXiv

Multimodal Transformer Models for Human Action Classification

Zoltan Varga, Esteve Valls Mascaro, Daniel Sliwowski, Dongheui Lee

RiTA 2024 Winner of Best Intelligence Paper at the International Conference on Robot Intelligence Technology and Applications (RiTA 2024)

We investigate how best to fuse multimodal information for the task of human action recognition.

Project Page PDF arXiv

ConditionNET: Learning Preconditions and Effects for Anomaly Detection and Recovery

Daniel Sliwowski, Dongheui Lee

IEEE Robotics and Automation Letters

Learn Preconditions and Effects of actions in a data-driven manner, and leverage the learned conditions for anomaly detection.

Project Page PDF arXiv

HOI4ABOT: Human-Object Interaction Anticipation for Assistive roBOTs

Esteve Valls Mascaro, Daniel Sliwowski, Dongheui Lee

CoRL 2023

Detect and anticipate human-object interactions for intention reading, which facilitate robots to assist humans.

Project Page PDF arXiv