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Yoonsang Lee Sungeun Kim Jehee Lee Seoul National University

Data-Driven Biped Control. Yoonsang Lee Sungeun Kim Jehee Lee Seoul National University. Biped Control. Human. Biped character. ?. Biped Control is Difficult. Balance, Robustness, Looking natural Various stylistic gaits. ASIMO Honda. Issues in Biped Control. Naturalness.

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Yoonsang Lee Sungeun Kim Jehee Lee Seoul National University

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

  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 human-like natural result Robustness maintaining balance Richness variety of motor skills Interactivity interactive control via user interface

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

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

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

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

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

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

  17. 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)

  18. Balancing frame n frame n+1 frame n+2 reference motion ... ... ... simulation

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

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

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

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

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

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

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

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

  27. Feedback Equations Stance hip Swing hip Stance ankle Swing foot height target pose reference frame

  28. Feedback Equations Stance hip Swing hip Stance ankle Swing foot height desired states current states

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

  30. Synchronization reference motion swing foot contacts the ground

  31. Synchronization reference motion simulation current time

  32. Early Landing reference motion contact occurs! simulation

  33. Early Landing reference motion dequed simulation

  34. Early Landing reference motion simulation

  35. Early Landing reference motion warped simulation

  36. Motion Warping motion1 motion2

  37. Motion Warping motion1 motion2 d

  38. Early Landing reference motion simulation

  39. Delayed Landing reference motion not contact yet! simulation

  40. Delayed Landing reference motion expand by integration simulation

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

  42. Delayed Landing reference motion warped simulation

  43. Delayed Landing reference motion simulation

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

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

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

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