1. Yoonsang Lee Sungeun Kim Jehee Lee Seoul National University Data-Driven Biped Control

2. Biped Control ? Human Biped character

3. Biped Control is Difficult Balance, Robustness, Looking natural Various stylistic gaits ASIMO Honda

4. Issues in Biped Control Naturalness Robustness Richness Interactivity human-like natural result maintaining balance variety of motor skills interactive control via user interface

5. Goal As realistic as motion capture data Robust under various conditions Equipped with a variety of motor skills Controlled interactively Naturalness Robustness Richness Interactivity

6. Related Work Manually designed controller –[Hodgins et al. 1995] [Yin et al. 2007] Non-linear optimization –[Sok 2007] [da Silva 2008] [Yin 2008] [Muico 2009] [Wang 2009] [Lasa 2010] [Wang 2010] [Wu 2010] Advanced control methodologies –[da Silva 2008] [Muico 2009] [Ye 2010] [Coros 2010] [Mordatch 2010] Data-driven approach –[Sok 2007] [da Silva 2008] [Muico 2009] [Tsai 2010] [Ye 2010] [Liu 2010]

7. Our Approach Control methods have been main focus –Machine learning, optimization, LQR/NQR We focus on reference data –Tracking control while modulating reference data

8. Our Approach Modulation of reference data –Balancing behavior of human –Importance of ground contact timings

9. Importance of Ground Contact Timings

10. Advantages Do not require –Non-linear optimization solver –Derivatives of equations of motion –Optimal control –Precomputation Easy to implement & Computationally efficient

11. Advantages Reference trajectory generated on-the-fly can be used Any existing data-driven techniques can be used to actuate physically simulated bipeds

13. Overview forward dynamics simulation animation engine user interaction data-driven control tracking control

15. Overview forward dynamics simulation user interaction animation engine data-driven control tracking control

16. Compute joint torques directly PD (Proportional Derivative) Control desired pose current pose generated torque

17. Compute desired tracking acceleration Forward Dynamics : force -> acceleration Inverse Dynamics : acceleration -> force Hybrid Dynamics –floating root joint : force -> acceleration –internal joints : acceleration -> force Hybrid Dynamics Tracking Control hybrid dynamics desired joint accelerations joint torques external forces

20. Overview forward dynamics simulation user interaction tracking control animation engine data-driven control

21. Data-Driven Control Continuous modulation of reference motion Spatial deviation –Simple feedback balance control (Balancing behavior) Temporal deviation –Synchronization reference to simulation (Importance of ground contact timings)

22. Balancing...reference motion simulation frame nframe n+1frame n+2...

23. frame nframe n+1frame n+2 Balancing target pose...reference motion simulation...

24. frame nframe n+1frame n+2 Balancing tracking...reference motion simulation...

25. frame n+1frame n+2frame n Balancing tracking...reference motion simulation...

26. Balance Feedback Near-passive knees in human walking Three-step feedback –stance hip –swing hip & stance ankle –swing foot height

27. Balance Feedback Biped is leaning backward ? reference motion at current frame reference motion at next frame simulation

28. Stance Hip Balance Feedback target pose at next frame reference frame simulation

29. Swing Hip & Stance Ankle Balance Feedback target pose at next frame reference frame simulation

30. Balance Feedback Swing Foot Height target pose at next frame reference frame simulation

31. Feedback Equations Stance hip Swing hip Stance ankle Swing foot height reference frametarget pose

32. Feedback Equations desired statescurrent states Stance hip Swing hip Stance ankle Swing foot height

33. Feedback Equations parameters transition function Stance hip Swing hip Stance ankle Swing foot height

34. Synchronization reference motion swing foot contacts the ground

35. Synchronization current time reference motion simulation

36. Early Landing reference motion contact occurs! simulation

37. Early Landing reference motion simulation dequed

38. Early Landing reference motion simulation

39. Early Landing reference motion simulation warped

40. Motion Warping motion1 motion2

41. Motion Warping d motion1 motion2

42. Early Landing reference motion simulation

43. Delayed Landing reference motion not contact yet! simulation

44. Delayed Landing reference motion simulation expand by integration

45. Delayed Landing reference motion simulation contact occurs! expand by integration

46. Delayed Landing reference motion simulation warped

47. Delayed Landing reference motion simulation

48. Overview forward dynamics simulation user interaction data-driven control tracking control animation engine

49. High-level control through user interfaces Generate a stream of movement patterns Animation Engine motion fragments query motion DB pattern generator user interaction stream of movement patterns

50. Collection of half-cycle motion fragments Maintain fragments in a directed graph Motion Database motion capture datamotion fragments

55. Why does this simple approach work? Human locomotion is inherently robust Mimicking human behavior –Distinctive gait serves as a reference trajectory –We do modulate the reference trajectory

56. Discussion We do not need optimization, optimal control, machine learning, or any precomputation Physically feasible reference motion data

57. Acknowledgements Thank –All the members of SNU Movement Research Laboratory –Anonymous reviewers Support –MKE & MCST of Korea

58. Data-Driven Biped Control Yoonsang Lee, Sungeun Kim, Jehee Lee