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

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

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

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

Interpolation and refinement Controller Learning Data Rectification Interpolation and refinement

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]

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?

Transitions Rectify motion graph built on the enriched database

Result Balance

Result Transition between various motions

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