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2019 Volume 34
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RESEARCH ARTICLE   Open Access    

Dr. Eureka: a humanoid robot manipulation case study

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  • Abstract: To this day, manipulation still stands as one of the hardest challenges in robotics. In this work, we examine the board game Dr. Eureka as a benchmark to encourage further development in the field. The game consists of a race to solve a manipulation puzzle: reordering colored balls in transparent tubes, in which the solution requires planning, dexterity and agility. In this work, we present a robot (Tactical Hazardous Operations Robot 3) that can solve this problem, nicely integrating several classical and state-of-the-art techniques. We represent the puzzle states as graph and solve it as a shortest path problem, in addition to applying computer vision combined with precise motions to perform the manipulation. In this paper, we also present a customized implementation of YOLO (called YOLO-Dr. Eureka) and we implement an original neural network (NN)-based incremental solution to the inverse kinematics problem. We show that this NN outperforms the inverse of the Jacobian method for large step sizes. Albeit requiring more computation per control cycle, the larger steps allow for much larger movements per cycle. To evaluate the experiment, we perform trials against a human using the same set of initial conditions.
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  • Cite this article

    Lin Yu-Ren, Guilherme Henrique Galelli Christmann, Ricardo Bedin Grando, Rodrigo Da Silva Guerra, Jacky Baltes. 2019. Dr. Eureka: a humanoid robot manipulation case study. The Knowledge Engineering Review 34(1), doi: 10.1017/S0269888919000213
    Lin Yu-Ren, Guilherme Henrique Galelli Christmann, Ricardo Bedin Grando, Rodrigo Da Silva Guerra, Jacky Baltes. 2019. Dr. Eureka: a humanoid robot manipulation case study. The Knowledge Engineering Review 34(1), doi: 10.1017/S0269888919000213
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RESEARCH ARTICLE   Open Access    

Dr. Eureka: a humanoid robot manipulation case study

  • Received: 11 April 2019
  • Revised: 03 September 2019
  • Accepted: 14 November 2019
  • Published online: 19 December 2019
The Knowledge Engineering Review  34,  2019, 34(1)  |  Cite this article

Abstract: Abstract: To this day, manipulation still stands as one of the hardest challenges in robotics. In this work, we examine the board game Dr. Eureka as a benchmark to encourage further development in the field. The game consists of a race to solve a manipulation puzzle: reordering colored balls in transparent tubes, in which the solution requires planning, dexterity and agility. In this work, we present a robot (Tactical Hazardous Operations Robot 3) that can solve this problem, nicely integrating several classical and state-of-the-art techniques. We represent the puzzle states as graph and solve it as a shortest path problem, in addition to applying computer vision combined with precise motions to perform the manipulation. In this paper, we also present a customized implementation of YOLO (called YOLO-Dr. Eureka) and we implement an original neural network (NN)-based incremental solution to the inverse kinematics problem. We show that this NN outperforms the inverse of the Jacobian method for large step sizes. Albeit requiring more computation per control cycle, the larger steps allow for much larger movements per cycle. To evaluate the experiment, we perform trials against a human using the same set of initial conditions.

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    Acknowledgments

    • This work was financially supported by the ‘Chinese Language and Technology Center’ of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Science and Technology, Taiwan, under Grants No. MOST 108-2634-F-003-002, MOST 108-2634-F-003-003, and MOST 108-2634-F-003-004 (administered through Pervasive Artificial Intelligence Research (PAIR) Labs), as well as MOST 107-2811-E-003 -503-. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research.

    • http://www.dota2.com/

    • https://boardgamegeek.com/boardgame/181345/dr-eureka

    • http://mypen.org.za/images/dr-eureka.jpg

    • http://www.robotis.us/

    • https://www.intel.com/content/www/us/en/products/boards-kits/nuc.html

    • THORMANG3 e-Manual - http://emanual.robotis.com/docs/en/platform/thormang3/introduction/

    • A video demonstration can be accessed at https://youtu.be/XWSNiQHQsBI.

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    Lin Yu-Ren, Guilherme Henrique Galelli Christmann, Ricardo Bedin Grando, Rodrigo Da Silva Guerra, Jacky Baltes. 2019. Dr. Eureka: a humanoid robot manipulation case study. The Knowledge Engineering Review 34(1), doi: 10.1017/S0269888919000213
    Lin Yu-Ren, Guilherme Henrique Galelli Christmann, Ricardo Bedin Grando, Rodrigo Da Silva Guerra, Jacky Baltes. 2019. Dr. Eureka: a humanoid robot manipulation case study. The Knowledge Engineering Review 34(1), doi: 10.1017/S0269888919000213
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