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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. HUBO KAIST. PETMAN Boston Dynamics.

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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 HUBO KAIST PETMAN Boston Dynamics

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

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

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

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

  13. 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

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

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

  16. Data-Driven Control • Continuous modulation of reference motion • Spatial deviation • SIMBICON-style feedback balance control • Temporal deviation • Synchronization reference to simulation

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

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

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

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

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

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

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

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

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

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

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

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

  29. Synchronization reference motion swing foot contacts the ground

  30. Synchronization reference motion simulation current time

  31. Early Landing reference motion contact occurs! simulation

  32. Early Landing reference motion dequed simulation

  33. Early Landing reference motion simulation

  34. Early Landing reference motion warped simulation

  35. Early Landing reference motion simulation

  36. Delayed Landing reference motion not contact yet! simulation

  37. Delayed Landing reference motion expand by integration simulation

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

  39. Delayed Landing reference motion warped simulation

  40. Delayed Landing reference motion simulation

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

  42. Tracking Control • Compute torques that attempts to follow reference trajectory (ex. PD control) • We use floating-base hybrid inverse dynamics external forces desired joint accelerations inverse dynamics joint torques

  43. 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

  44. Discussion • We do not need optimization, optimal control, machine learning, or any precomputation • Physically feasible reference motion data • Future work • Wider spectrum of human motions

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

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