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Welcome to the 2nd Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

Integrating high-level and low-level cognitive capabilities is essential for developing robotic systems that can adaptively act in our daily environment in active collaboration with humans.

The main objective of this workshop is to share knowledge about the state-of-the-art machine learning methods that contribute to modeling high-level cognitive capabilities in robotics and to exchange views among cutting-edge robotics researchers who are interested in adaptive high-level cognitive capabilities in robotics.

This workshop will bring together researchers from cognitive robotics, speech processing, artificial intelligence, machine learning, computer vision, and natural language processing to discuss the current challenges in machine learning methods for high-level cognitive capabilities in robotics.

Last year, we had the WS ML-HLCR 2016 in IROS2016 very successfully. The WS attracted more than 20 posters and 100 participants. Based on the huge success we are proposing the second WS about this challenging topic.

We hope this workshop bring us further understanding of human intelligence and creating cutting-edge robotics technologies.

The workshop proposal has been accepted by IROS 2017. I hope to see you at IROS 2017 in Vancouver.


    • (24th Sept 2017) (almost) The final program has been decided.

    • (22nd June 2017) Special Issue in Advanced Robotics has been determined.

    • (21st April 2017) The proposal was accepted by IROS 2017.

    • (15th March 2017) The proposal was submitted to the IROS2017. The website is open.

Important Dates

    • Deadline for submission of poster paper: August 19th, August 12th, 2017

      • The poster paper should be 2 pages at most.

    • Notification of acceptance, around 24th August, 2017

  • Deadline for submission of Camera-ready paper around September 1st, 2017

      • Camera-ready paper can be extended up to 4 pages.

    • The presentation date: September 28th, 2017.


    • Tetsunari Inamura, National Institute of Informatics, Japan

    • Hiroki Yokoyama, University of Tamagawa, Japan

    • Emre Ugur, Bogazici University, Turkey

    • Xavier Hinaut, INRIA, France

    • Michael Beetz, University of Bremen, Germany

    • Tadahiro Taniguchi, Ritsumeikan University, Japan


ML-HLCR 2017 is endorsed by following committees.