“Designing chain reaction contraptions from causal graphs” by Roussel, Cani, Léon and Mitra

  • ©Robin Roussel, Marie-Paule Cani, Jean-Claude Léon, and Niloy J. Mitra

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Title:

    Designing chain reaction contraptions from causal graphs

Session/Category Title:   Capture Control


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Abstract:


    Chain reaction contraptions, commonly referred to as Rube Goldberg machines, achieve simple tasks in an intentionally complex fashion via a cascading sequence of events. They are fun, engaging and satisfying to watch. Physically realizing them, however, involves hours or even days of manual trial-and-error effort. The main difficulties lie in predicting failure factors over long chains of events and robustly enforcing an expected causality between parallel chains, especially under perturbations of the layout. We present a computational framework to help design the layout of such contraptions by optimizing their robustness to possible assembly errors. Inspired by the active learning paradigm in machine learning, we propose a generic sampling-based method to progressively approximate the success probability distribution of a given scenario over the design space of possible scene layouts. The success or failure of any given simulation is determined from a user-specified causal graph enforcing a time ordering between expected events. Our method scales to complex causal graphs and high dimensional design spaces by dividing the graph and scene into simpler sub-scenarios. The aggregated success probability distribution is subsequently used to optimize the entire layout. We demonstrate the use of our framework through a range of real world examples of increasing complexity, and report significant improvements over alternative approaches. Code and fabrication diagrams are available on the project page.

References:


    1. Moritz Bächer, Stelian Coros, and Bernhard Thomaszewski. 2015. LinkEdit: Interactive Linkage Editing Using Symbolic Kinematics. ACM Trans. Graph. 34, 4, Article 99 (July 2015), 8 pages. Google ScholarDigital Library
    2. Moritz Bächer, Emily Whiting, Bernd Bickel, and Olga Sorkine-Hornung. 2014. Spin-it: Optimizing Moment of Inertia for Spinnable Objects. ACM Trans. Graph. 33, 4 (2014), 1–96. Google ScholarDigital Library
    3. Amit H. Bermano, Thomas Funkhouser, and Szymon Rusinkiewicz. 2017. State of the Art in Methods and Representations for Fabrication-Aware Design. Computer Graphics Forum 36, 2 (May 2017), 509–535. Google ScholarDigital Library
    4. K. Brewer, L. Carraway, and D. Ingram. 2010. Forward Selection as a Candidate for Constructing Nonregular Robust Parameter Designs. Technical Report. Arkansas State University.Google Scholar
    5. Duygu Ceylan, Wilmot Li, Niloy J. Mitra, Maneesh Agrawala, and Mark Pauly. 2013. Designing and Fabricating Mechanical Automata from Mocap Sequences. ACM SIGGRAPH Asia 32, 6 (2013), 11. Google ScholarDigital Library
    6. Desai Chen, David I. W. Levin, Wojciech Matusik, and Danny M. Kaufman. 2017. Dynamics-aware Numerical Coarsening for Fabrication Design. ACM Trans. Graph. 36, 4, Article 84 (July 2017), 15 pages. Google ScholarDigital Library
    7. Stelian Coros, Bernhard Thomaszewski, Gioacchino Noris, Shinjiro Sueda, Moira Forberg, Robert W. Sumner, Wojciech Matusik, and Bernd Bickel. 2013. Computational Design of Mechanical Characters. ACM Trans. Graph. 32, 4 (2013), 1–83. Google ScholarDigital Library
    8. Erwan Coumans. 2018. Bullet Physics SDK. https://github.com/bulletphysics/bullet3. Accessed: 2018-01-01.Google Scholar
    9. Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, New York, NY, USA. Google ScholarDigital Library
    10. Peter Fischli and David Weiss. 1987. The Way Things Go. Retrieved from https://www.youtube.com/watch?v=GXrRC3pfLnE. Accessed: 2018-01-01.Google Scholar
    11. Yohsuke Furuta, Jun Mitani, Takeo Igarashi, and Yukio Fukui. 2010. Kinetic Art Design System Comprising Rigid Body Simulation. Computer-Aided Design and Applications 7, 4 (2010), 533–546.Google ScholarCross Ref
    12. Akash Garg, Alec Jacobson, and Eitan Grinspun. 2016. Computational design of reconfigurables. ACM Trans. Graph. 35, 4 (2016), 1–14. Google ScholarDigital Library
    13. Honda. 2003. Cog. Retrieved from https://www.youtube.com/watch?v=Z57kGB-mI54. Accessed: 2018-01-01.Google Scholar
    14. Devendra Kalra and Alan H Barr. 1992. Modeling with Time and Events in Computer Animation. Computer Graphics Forum 11, 3 (may 1992), 45–58.Google ScholarCross Ref
    15. Yilip Kim and Namje Park. 2012. Development and Application of STEAM Teaching Model Based on the Rube Goldberg’s Invention. In Computer Science and its Applications, Sang-Soo Yeo, Yi Pan, Yang Sun Lee, and Hang Bae Chang (Eds.). Springer Netherlands, Dordrecht, 693–698.Google Scholar
    16. Dieter Kraft. 1988. A Software Package for Sequential Quadratic Programming. Technical Report. Institut fuer Dynamik der Flugsysteme, Oberpfaffenhofen.Google Scholar
    17. Mandy Lange, Dietlind Zühlke, Olaf Holz, and Thomas Villmann. 2014. Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. In ESANN. Bruges, 271–276.Google Scholar
    18. Steffen L Lauritzen. 2001. Causal inference from graphical models. Complex stochastic systems (2001), 63–107.Google Scholar
    19. Honghua Li, Ruizhen Hu, Ibraheem Alhashim, and Hao Zhang. 2015. Foldabilizing Furniture. ACM Trans. Graph. 34, 4, Article 90 (July 2015), 12 pages. Google ScholarDigital Library
    20. M. Lin, T. Shao, Y. Zheng, N. J. Mitra, and K. Zhou. 2018. Recovering Functional Mechanical Assemblies from Raw Scans. IEEE Transactions on Visualization and Computer Graphics 24, 3 (March 2018), 1354–1367.Google ScholarCross Ref
    21. Li-Ke Ma, Yizhonc Zhang, Yang Liu, Kun Zhou, and Xin Tong. 2017. Computational Design and Fabrication of Soft Pneumatic Objects with Desired Deformations. ACM Trans. Graph. 36, 6, Article 239 (Nov. 2017), 12 pages. Google ScholarDigital Library
    22. Niloy J. Mitra, Yong-Liang Yang, Dong-Ming Yan, Wilmot Li, and Maneesh Agrawala. 2010. Illustrating How Mechanical Assemblies Work. ACM Trans. Graph. 29, 4 (2010), 58:1–11. Google ScholarDigital Library
    23. John C. Platt. 1999. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In Advances in Large Margin Classifiers. MIT Press, 61–74.Google Scholar
    24. Steve Price. 2017. Top 10 Chain Reaction Tips | Rube Goldberg HowTo. Retrieved from https://www.youtube.com/watch?v=p8Wwq_B5S7I. Accessed: 2018-01-01.Google Scholar
    25. Ravella Sreenivas Rao, C. Ganesh Kumar, R. Shetty Prakasham, and Phil J. Hobbs. 2008. The Taguchi methodology as a statistical tool for biotechnological applications: A critical appraisal. Biotechnology Journal 3, 4 (4 2008), 510–523.Google ScholarCross Ref
    26. M. O. Riedl and R. M. Young. 2006. From linear story generation to branching story graphs. IEEE Computer Graphics and Applications 26, 3 (May 2006), 23–31. Google ScholarDigital Library
    27. Adriana Schulz, Harrison Wang, Eitan Crinspun, Justin Solomon, and Wojciech Matusik. 2018. Interactive Exploration of Design Trade-offs. ACM Trans. Graph. 37, 4, Article 131 (July 2018), 14 pages. Google ScholarDigital Library
    28. Adriana Schulz, Jie Xu, Bo Zhu, Changxi Zheng, Eitan Grinspun, and Wojciech Matusik. 2017. Interactive Design Space Exploration and Optimization for CAD Models. ACM Trans. Graph. 36, 4, Article 157 (July 2017), 14 pages. Google ScholarDigital Library
    29. Daniel L. Schwartz and Mary Hegarty. 1996. Coordinating multiple representations for reasoning about mechanical devices. In Proceedings of the AAAI Spring Symposium on Cognitive and Computational Models of Spatial Representation. AAAI Press, Menlo Park, CA, 9.Google Scholar
    30. Burr Settles. 2012. Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 6, 1 (2012), 1–114. Google ScholarDigital Library
    31. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. 2016. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104, 1 (Jan 2016), 148–175.Google ScholarCross Ref
    32. Maria Shugrina, Ariel Shamir, and Wojciech Matusik. 2015. Fab Forms: Customizable Objects for Fabrication with Validity and Geometry Caching. ACM Trans. Graph. 34, 4, Article 100 (July 2015), 12 pages. Google ScholarDigital Library
    33. I.M Sobol’. 1967. On the distribution of points in a cube and the approximate evaluation of integrals. U. S. S. R. Comput. Math. and Math. Phys. 7, 4 (1967), 86–112.Google ScholarCross Ref
    34. Peng Song, Xiaofei Wang, Xiao Tang, Chi-Wing Fu, Hongfei Xu, Ligang Liu, and Niloy J. Mitra. 2017. Computational Design of Wind-up Toys. ACM Trans. Graph. 36, 6, Article 238 (Nov. 2017), 13 pages. Google ScholarDigital Library
    35. Bernhard Thomaszewski, Stelian Coros, Damien Gauge, Vittorio Megaro, Eitan Grinspun, and Markus Gross. 2014. Computational Design of Linkage-based Characters. ACM Trans. Graph. 33, 4, Article 64 (July 2014), 9 pages. Google ScholarDigital Library
    36. Nobuyuki Umetani and Bernd Bickel. 2018. Learning Three-dimensional Flow for Interactive Aerodynamic Design. ACM Trans. Graph. 37, 4, Article 89 (July 2018), 10 pages. Google ScholarDigital Library
    37. Nobuyuki Umetani, Yuki Koyama, Ryan Schmidt, and Takeo Igarashi. 2014. Pteromys: Interactive Design and Optimization of Free-formed Free-flight Model Airplanes. ACM Trans. Graph. 33, 4 (2014), 1–10. Google ScholarDigital Library
    38. Lifeng Zhu, Weiwei Xu, John Snyder, Yang Liu, Guoping Wang, and Baining Guo. 2012. Motion-guided Mechanical Toy Modeling. ACM Trans. Graph. 31, 6, Article 127 (Nov. 2012), 10 pages. Google ScholarDigital Library


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