yoonsang lee sungeun kim jehee lee seoul national university

Yoonsang LeeSungeun Kim

Jehee Lee

Seoul National University

Data-Driven Biped Control

Biped Control

?Human Biped character

Biped Control is Difficult

• Balance, Robustness, Looking natural• Various stylistic gaits

PETMANBoston Dynamics

ASIMOHonda

HUBOKAIST

Issues in Biped Control

Naturalness

Robustness

Richness

Interactivity

human-like natural result

maintaining balance

variety of motor skills

interactive control via user interface

Goal

As realistic as motion capture data

Robust under various conditions

Equipped with a variety of motor skills

Controlled interactively

Naturalness

Robustness

Richness

Interactivity

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]

Our Approach

• Control methods have been main focus– Machine learning, optimization, LQR/NQR

• We focus on reference data– Tracking control while modulating reference data

Our Approach

• Modulation of reference data

• Balancing behavior of human

• Importance of ground contact timings

Advantages

• Do not require– Non-linear optimization solver– Derivatives of equations of motion – Optimal control– Precomputation

Easy to implement & Computationally efficient

Advantages

• Reference trajectory generated on-the-fly can be used

Any existing data-driven techniques can be used to actuate physically simulated bipeds

Overview

forward dynamics simulation

animation engine

user interaction

data-driven control tracking control

Overview

forward dynamics simulation

user interaction

data-driven control tracking control

animation engine

• High-level control through user interfaces• Generate a stream of movement patterns

Animation Engine

motion fragments

query

motion DBpattern generator

user interaction

stream of movement patterns

• Collection of half-cycle motion fragments

• Maintain fragments in a directed graph

Motion Database

motion capture data motion fragments

Overview

forward dynamics simulation

user interaction

tracking control

animation engine

data-driven control

Data-Driven Control

• Continuous modulation of reference motion

• Spatial deviation– SIMBICON-style feedback balance control

• Temporal deviation– Synchronization reference to simulation

Balancing

...reference motion

simulation

frame n frame n+1 frame n+2

...

...

frame n frame n+1 frame n+2

Balancing

target pose

...reference motion

simulation

...

...

frame n frame n+1 frame n+2

Balancing

tracking

...reference motion

simulation

...

...

frame n+1 frame n+2frame n

Balancing

tracking

...reference motion

simulation

...

...

Balance Feedback

• Near-passive knees in human walking

• Three-step feedback– stance hip– swing hip & stance ankle– swing foot height

Balance Feedback

• Biped is leaning backward

?

reference motionat current frame

reference motionat next frame

simulation

• Stance Hip

Balance Feedback

target poseat next frame

reference frame simulation

• Swing Hip & Stance Ankle

Balance Feedback

target poseat next frame

reference frame simulation

Balance Feedback

• Swing Foot Height

target poseat next frame

reference frame simulation

Feedback Equations

Stance hip

Swing hip

Stance ankle

Swing foot height

reference frametarget pose

Feedback Equations

desired states current states

Stance hip

Swing hip

Stance ankle

Swing foot height

Feedback Equations

parameterstransition function

Stance hip

Swing hip

Stance ankle

Swing foot height

Synchronization

referencemotion

swing foot contacts the ground

Synchronization

current time

referencemotion

simulation

Early Landing

referencemotion

contact occurs!simulation

Early Landing

referencemotion

simulation

dequed

Early Landing

referencemotion

simulation

Early Landing

referencemotion

simulation

warped

Early Landing

referencemotion

simulation

Delayed Landing

referencemotion

not contact yet!simulation

Delayed Landing

referencemotion

simulation

expand byintegration

Delayed Landing

referencemotion

simulation contact occurs!

expand byintegration

Delayed Landing

referencemotion

simulation

warped

Delayed Landing

referencemotion

simulation

Overview

forward dynamics simulation

user interaction

animation engine

data-driven control tracking control

• Compute torques that attempts to follow reference trajectory (ex. PD control)

• We use floating-base hybrid inverse dynamics

Tracking Control

inverse dynamicsdesiredjoint accelerations joint torques

external forces

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

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

Acknowledgements

• Thank– All the members of SNU Movement Research

Laboratory– Anonymous reviewers

• Support– MKE & MCST of Korea

Data-Driven Biped ControlYoonsang Lee, Sungeun Kim, Jehee Lee