Online Control of Simulated Humanoids Using Particle Belief Propagation.

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Presentation transcript:

Online Control of Simulated Humanoids Using Particle Belief Propagation

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

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

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 : if the character’s head touches the environment

Previous work Reference Motion Simulation Fall down

Previous work Reference Motion Change reference motion

Previous work Reference Motion Simulation

Result Control Simulation Initial Pose

Result Control Simulation Initial Pose Pick best sample

Result Control Simulation

Result Control Simulation

Result Control Simulation

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

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

Sampling

Resampling Backwards local refinement Using previous trajectories as a prior

Probability Model

Control as Markov Random Field

Belief Propagation

Particle Belief Propagation

Result Control Simulation

Resampling

Local Refinement

Operation Over Multiple Frames

Current Step Previous Step

Sampling

Operation Over Multiple Frames

Current Step Previous Step

Total Algorithm