“Dextrous manipulation from a grasping pose” by Liu

  • ©C. Karen Liu

Conference:


Type:


Title:

    Dextrous manipulation from a grasping pose

Presenter(s)/Author(s):



Abstract:


    This paper introduces an optimization-based approach to synthesizing hand manipulations from a starting grasping pose. We describe an automatic method that takes as input an initial grasping pose and partial object trajectory, and produces as output physically plausible hand animation that effects the desired manipulation. In response to different dynamic situations during manipulation, our algorithm can generate a range of possible hand manipulations including changes in joint configurations, changes in contact points, and changes in the grasping force. Formulating hand manipulation as an optimization problem is key to our algorithm’s ability to generate a large repertoire of hand motions from limited user input. We introduce an objective function that accentuates the detailed hand motion and contacts adjustment. Furthermore, we describe an optimization method that solves for hand motion and contacts efficiently while taking into account long-term planning of contact forces. Our algorithm does not require any tuning of parameters, nor does it require any prescribed hand motion sequences.

References:


    1. Albrecht, I., Haber, J., and Seidel, H.-P. 2003. Construction and animation of anatomically based human hand models. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 98–109. Google ScholarDigital Library
    2. Aydin, Y., and Nakajima, M. 1999. Database guided computer animation of human grasping using forward and inverse kinematics. Computers and Graphics 23, 1, 145–154.Google ScholarCross Ref
    3. Bicchi, A. 1992. Optimal control of robotic grasping. In Proc. American Control Con., 778–779.Google ScholarCross Ref
    4. Brost, R. C. 1988. Automatic grasp planning in the presence of uncertainty. Int. J. Robotics Research 7, 3–17. Google ScholarDigital Library
    5. Cai, C., and Roth, B. 1987. On the spatial motion of a rigid body with point contact. In Proc. IEEE Int. Con. Robotics and Automation, 686–695.Google Scholar
    6. Cheng, F. T., and Orin, D. E. 1990. Efficient algorithm for optimal force distribution-the compact dual lp method. IEEE Trans. Robot Automat. 6 (Apr.), 178–187.Google ScholarCross Ref
    7. ElKoura, G., and Singh, K. 2003. Handrix: Animating the human hand. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 110–119. Google ScholarDigital Library
    8. Fearing, R. 1986. Simlified grasping and manipulation with dexterous robot hands. IEEE Jour. on Robotics and Automation 2, 188–195.Google ScholarCross Ref
    9. Gill, P., Saunders, M., and Murray, W. 1996. Snopt: An sqp algorithm for large-scale constrained optimization. Tech. Rep. NA 96-2, University of California, San Diego.Google Scholar
    10. Huang, Z., Boulic, R., and Thalmann, D. 1995. A multisensor approach for grasping and 3-D interaction. In Computer Graphics International ’95. Google ScholarDigital Library
    11. Kerr, J., and Roth, B. 1986. Analysis of multifingered hands. Int. J. Robotics Research 4, 3–17.Google ScholarCross Ref
    12. Kim, J., Cordier, F., and Magnenat-Thalmann, N. 2000. Neural network-based violinists hand animation. In Conference on Computer Graphics International, 37–44. Google ScholarDigital Library
    13. Koga, Y., Kondo, K., Kuffner, J., and Latombe, J.-C. 1994. Planning motions with intentions. In SIGGRAPH, 395–408. Google ScholarDigital Library
    14. Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. ACM Trans. on Graphics 25, 3 (Aug.), 872–880. Google ScholarDigital Library
    15. Liu, C. K., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. on Graphics 24, 3 (July), 1071–1081. Google ScholarDigital Library
    16. Liu, C. K. 2008. Synthesis of interactive hand animation. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Google ScholarDigital Library
    17. Majkowska, A., Zordan, V., and Faloutsos, P. 2006. Automatic splicing for hand and body animation. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Google ScholarDigital Library
    18. Nguyen, V. D. 1986. Constructing force-closure grasps. In Proc. IEEE Int. Con. Robotics and Automation, 1368–1373.Google ScholarCross Ref
    19. Pollard, N. S., and Zordan, V. B. 2005. Physically based grasping control from example. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 311–318. Google ScholarDigital Library
    20. Popović, J., Seitz, S. M., Erdmann, M., Popović, Z., and Witkin, A. P. 2000. Interactive manipulation of rigid body simulations. 209–218.Google Scholar
    21. Rosenbaum, D. A., and Jorgensen, M. J. 1992. Planning macroscopic aspects of manual control. Hum. Mov. Sci. 11, 61–69.Google ScholarCross Ref
    22. Safonova, A., Hodgins, J. K., and Pollard, N. S. 2004. Synthesizing physically realistic human motion in low-dimensinal, behavior-specific spaces. ACM Trans. on Graphics 23, 3, 514–521. Google ScholarDigital Library
    23. Sueda, S., Kaufman, A., and Pai, D. K. 2008. Musculotendon simulation for hand animation. ACM Trans. on Graphics 27, 3 (Aug.). Google ScholarDigital Library
    24. Tournassoud, P., Lozano-Perez, T., and Mazer, E. 1987. Regrasping. In Proc. IEEE Int. Con. Robotics and Automation, 1924–1928.Google Scholar
    25. Tsang, W., Singh, K., and Fiume, E. 2005. Helping hand: An anatomically accurate inverse dynamics solution for unconstrained hand motion. In Eurographics/SIGGRAPH Symposium on Computer Animation, 1–10. Google ScholarDigital Library
    26. Witkin, A., and Kass, M. 1988. Spacetime constraints. In SIGGRAPH, vol. 22, 159–168. Google ScholarDigital Library


ACM Digital Library Publication:



Overview Page: