1. Simulating Biped Behaviors from Human Motion Data
Kwang Won Sok Manmyung Kim Jehee. Lee

2. Motion graph v.s. robotics controller
Goal
Dynamics simulation
Learn various motor skills from human
Controllable
Motion graph v.s. robotics controller

3. Challenge Controller Hard to design Unnatural Mocap Imprecise data
Highly simplified model

4. Approach Rectify mocap data to be physically feasible
Good data v.s. good method
“Controller” -- “state-action” pair
A set of desired trajectories

5. Interpolation and refinement
Controller Learning
Data Rectification
Interpolation and refinement

6. Data Rectification Space-time Optimization
Variables: displacement over input data
Objective: simulation result be close to input data
[Constraints: forward simulation with simple PD control]

9. Interpolation and Refinement
Feature (state) vector
Joint angles and velocities
Root position, velocity and direction (spine)
Foot position and velocity
Ground contact ( ON/OFF )
Blend to a successful motion when failure happens
Enrich the existing database
Dimensionality
Is success guaranteed?

10. Transitions
Rectify motion graph built on the enriched database

11. Result
Balance

12. Result
Transition between various motions

13. Questions Your impression Interesting or dumb? What’s new
motion graph, reinforcement learning?
Why not 3D
Curse of dimensionality?
What makes it into SIGGRAPH