1. Online Control of Simulated Humanoids Using Particle Belief Propagation

2. Motivation Control simulated humanoid Various movements, environment Without any pre-computation, motion capture data At real time

3. Simulation Model : State ( pose and velocity ) : Control. ( Desire joint angle ) Character model ( 15 bones, 30 DOF )

4. Objective ( for balancing ) vel : speed of COM com : horizontal distance of COM from the feet y : y position of COM relative to feet feet : distance between each foot w : angular speed of the pelvis up : difference between the pelvis up vector and global up vector fwd : head facing direction damage : 10000 if the character’s head touches the environment

5. Previous work Reference Motion Simulation Fall down

6. Previous work Reference Motion Change reference motion

7. Previous work Reference Motion Simulation

8. Result Control Simulation Initial Pose

9. Result Control Simulation Initial Pose Pick best sample

10. Result Control Simulation

11. Result Control Simulation

12. Result Control Simulation

13. Result Control Simulation N : # of samples ( = 32 ) K : Planning horizon ( = 1.2s, 36 time step )

14. Simulation Model : State ( pose and velocity ) : Control. ( Desire joint angle ) Character model ( 15 bones, 30 DOF )

15. Sampling

16. Resampling Backwards local refinement Using previous trajectories as a prior

17. Probability Model

20. Control as Markov Random Field

21. Belief Propagation

22. Particle Belief Propagation

25. Result Control Simulation

26. Resampling

27. Local Refinement

28. Operation Over Multiple Frames

29. Current Step Previous Step

30. Sampling

31. Operation Over Multiple Frames

32. Current Step Previous Step

33. Total Algorithm