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

2. Lin & Huang, Rising from Various Lying Postures 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

3. 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

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

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

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

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

8. Lin & Huang, Rising from Various Lying Postures 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

9. 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.

10. 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

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

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

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

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

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

16. Lin & Huang, Rising from Various Lying Postures 16 RRT-connect [Kuffner et al. 2000] 3. Find x near on T a (nearest to x rand )

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

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

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

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

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

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

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

24. 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

25. 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.] DynamicsController torques output motion planned motion external forces

26. 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

27. 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

28. Lin & Huang, Rising from Various Lying Postures 28 R ising from random initial poses 20 prone positions20 lateral positions20 supine positions

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

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

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

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

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

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

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

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

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

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

39. 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

40. 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

41. Lin & Huang, Rising from Various Lying Postures 41

42. Lin & Huang, Rising from Various Lying Postures 42 Basic Idea Limited database + dynamics simulation – 14 rising motions Motion planning – increases motion varieties – avoids collisions Dynamics filtering – increases physical plausibility – reflects influences of environments

43. Lin & Huang, Rising from Various Lying Postures 43 Rapidly-exploring random tree (RRT) Steve LaValleSteve LaValle http://msl.cs.uiuc.edu/rrt/gallery.html

44. Lin & Huang, Rising from Various Lying Postures 44 RRT-connect [Kuffner et al. 2000] 1. T a executes EXTEND function 2. Generate a random vertex x rand as a reference vertex 3. Find x near on T a (nearest to x rand ) 4. Grow x new toward x rand (within distance ε) 5. T b executes EXTEND function

45. Lin & Huang, Rising from Various Lying Postures 45 Approach Overview Stage I: connecting posture selection – Given P init, find a closest P con Stage II: motion planning – Find a collision-free sequence between P init and P con Stage III: dynamics filtering – Tracking the planned motion

46. Lin & Huang, Rising from Various Lying Postures 46 Rising from a sitting pose