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Rising from Various Lying Postures

Explore the challenges and solutions for rising motions from different lying poses, focusing on motion planning, dynamics filtering, and physical plausibility in computer animation. Implementing RRT and RRT-blossom for effective motion control strategies. Developed at National Chiao Tung University, Taiwan.

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Rising from Various Lying Postures

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  1. Rising from Various Lying Postures Wen-Chieh Lin and Yi-Jheng Huang Department of Computer Science National Chiao Tung University, Taiwan

  2. Motivation • Rising up is a very common and important motion • Human / robot / avatar could fall and need stand up • reflects physical capability and style variation • Rarely addressed in computer animation • focus on motion control of general types of motions • Not address motion varieties Lin & Huang, Rising from Various Lying Postures

  3. Why is rising up hard? • Rich variations • various lying postures • various environments • different characters (style, physical capability) • Complex motor skills • collision avoidance • balance maintenance • adaptation Lin & Huang, Rising from Various Lying Postures

  4. Basic Idea Lin & Huang, Rising from Various Lying Postures Small database for typical rising motions Motion planning for large variations Dynamics filtering for small variations

  5. Small database for typical rising motions rising motion database Lin & Huang, Rising from Various Lying Postures • Most varieties appear at lying-to-squatting • 14 rising motions from prone, supine, and lateral positions on flat ground

  6. Motion planning for large variations . . . various lying postures rising motion database Lin & Huang, Rising from Various Lying Postures • Connects an arbitrary lying pose to database • avoids collisions • satisfies constraints

  7. Dynamics filtering for small variations external forces Controller torques Dynamics planned motion output motion Lin & Huang, Rising from Various Lying Postures Ensures physical plausibility Adapts to environments and characters

  8. Related Work: Computer Animation • Composable controllers • Faloutsos et al., SIGGRAPH 2001 • Contact-rich motion control • Liu et al., SIGGRAPH 2010 • Both focus on motion control of various types of motions • Not address the motion varieties • crucial for rising up motions Lin & Huang, Rising from Various Lying Postures

  9. Related Work: Robotics • Hot topic in humanoid research • Morimoto and Doya, IROS’98 • Fujiewara et al. IROS’03 • Hirukawa et al., IJRR’05 • Kanehiro et al., ICRA’07 • Focus on robustness instead of varieties and flexibilities Hirukawa et al. Lin & Huang, Rising from Various Lying Postures

  10. Related Work: Biomechanics • Address analysis rather than generation of rising motions • McCoy and VanSant, Physical Therapy, 1993 • Ford-Smith and VanSant, Physical Therapy, 1993 Lin & Huang, Rising from Various Lying Postures

  11. Motion Planning Problem Initial Goal Lin & Huang, Rising from Various Lying Postures

  12. Rapidly-exploring random tree (RRT) Steve LaValle http://msl.cs.uiuc.edu/rrt/gallery.html Lin & Huang, Rising from Various Lying Postures

  13. RRT-connect [Kuffner et al. 2000] Lin & Huang, Rising from Various Lying Postures

  14. RRT-connect [Kuffner et al. 2000] 1. Ta executes EXTEND function Lin & Huang, Rising from Various Lying Postures

  15. RRT-connect [Kuffner et al. 2000] 2. Generate a random node xrandas a reference node Lin & Huang, Rising from Various Lying Postures

  16. RRT-connect [Kuffner et al. 2000] 3. Find xnear on Ta (nearest to xrand) Lin & Huang, Rising from Various Lying Postures

  17. RRT-connect [Kuffner et al. 2000] 4. Grow xnew toward xrand (within distance ε) Lin & Huang, Rising from Various Lying Postures

  18. RRT-connect [Kuffner et al. 2000] 5. Tb executes EXTEND function Lin & Huang, Rising from Various Lying Postures

  19. RRT-blossom [Kalisiak & van de Panne, 2006] • Blossom • add multiple samples • explore space more quickly RRT RRT-Blossom Sub-goal Sub-goal Lin & Huang, Rising from Various Lying Postures

  20. RRT-blossom • Regression • avoids searching spanning nodes • merge nearby nodes Regression Regression! Lin & Huang, Rising from Various Lying Postures

  21. Environment Initial posture Key posture Stage I Connecting posture selection Full-body RRT-blossom Cut illegal motion Adjust constraint Ground collision Yes No Partial-body RRT-blossom Stage II Cut illegal motion Adjust constraint Obstacle & Self collision Yes No Smoothing and dynamics filtering Stage III Motion Lin & Huang, Rising from Various Lying Postures

  22. Connecting Posture Selection • Posture • Posture difference • Accelerating search by clustering the motion database Lin & Huang, Rising from Various Lying Postures

  23. Motion Planning Strategies • Loose-to-strict iterative refinement • Spatiotemporally local refinement Full-body RRT-blossom Environment Cut illegal motion Adjust constraint Ground collision Yes No Partial-body RRT-blossom Stage II Cut illegal motion Adjust constraint Obstacle & Self collision Yes No Lin & Huang, Rising from Various Lying Postures

  24. RRT-blossom Modifications • RRT-blossom is originally proposed for lower-dimensional configuration space • To handle motion planning in high- dimensional posture space • plan global orientation and joint angle separately • Impose joint limit constraint and avoid collision in the blossom operation Lin & Huang, Rising from Various Lying Postures

  25. Dynamics Filtering • Track a planned motion using velocity-driven control [Tsai et al., TVCG 2010] • Balance by virtual actuator control [Pratt et al.] external forces Controller torques Dynamics planned motion output motion Lin & Huang, Rising from Various Lying Postures

  26. Dynamics Filtering (cont.) • In some cases, our controller may not be able to track from squatting to standing • connect to a nearest rising motion in the database • fine since less variations from squatting to standing Lin & Huang, Rising from Various Lying Postures

  27. Results • Our database only has14 motions of rising up on flat ground (CMU MOCAP database) • Rising up from random initial postures • Rising up with an initial and a key posture • Rising up in various environments • Motion retargeting of rising up Lin & Huang, Rising from Various Lying Postures

  28. Rising from random initial poses 20 prone positions 20 lateral positions 20 supine positions Lin & Huang, Rising from Various Lying Postures

  29. Rising from a sitting pose Lin & Huang, Rising from Various Lying Postures

  30. Rising with given initial and key poses Lin & Huang, Rising from Various Lying Postures

  31. Rising from prone with a key pose Lin & Huang, Rising from Various Lying Postures

  32. Rising from lateral with a key pose Lin & Huang, Rising from Various Lying Postures

  33. Rising from sitting with a key pose Lin & Huang, Rising from Various Lying Postures

  34. Rising from different environments Lin & Huang, Rising from Various Lying Postures

  35. Arm motion adapts to environments Lin & Huang, Rising from Various Lying Postures

  36. Rising up under a table Lin & Huang, Rising from Various Lying Postures

  37. Rising up on different ground Lin & Huang, Rising from Various Lying Postures

  38. Motion Retargeting Lin & Huang, Rising from Various Lying Postures

  39. Quality evaluation by human subjects • score range from 10 (best) to 1 (worst) • 27 males and 13 females aged 19 to 60 Lin & Huang, Rising from Various Lying Postures

  40. Conclusion • Simple and effective approach • Small database + motion planning + dynamics filtering • Generate rising up motions with varieties • various lying postures and environments • physically plausible • Efficient motion planning strategy • Loose-to-strict spatiotemporally local refinement strategy Lin & Huang, Rising from Various Lying Postures

  41. Lin & Huang, Rising from Various Lying Postures

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