rising from various lying postures

Rising from Various Lying Postures

Wen-Chieh Lin and Yi-Jheng HuangDepartment of Computer ScienceNational Chiao Tung University, Taiwan

Lin & Huang, Rising from Various Lying Postures 2

Motivation

• Rising up is a very common and important motion– Human / robot / avatar could fall and need stand up

– reflects physical capability and style variation

• Rarely addressed in computer animation – focus on motion control of general types of motions

– Not address motion varieties


Why is rising up hard?

• Rich variations– various lying postures

– various environments

– different characters (style, physical capability)

• Complex motor skills– collision avoidance

– balance maintenance

– adaptation


Basic Idea

• Small database for typical rising motions

• Motion planning for large variations

• Dynamics filtering for small variations


Small database for typical rising motions

• Most varieties appear at lying-to-squatting

• 14 rising motions from prone, supine, and lateral positions on flat ground

rising motion database


Motion planning for large variations

• Connects an arbitrary lying pose to database– avoids collisions

– satisfies constraints

rising motion databasevarious lying postures

. . . . ..


Dynamics filtering for small variations

• Ensures physical plausibility

• Adapts to environments and characters

Dynamics Controller torques output

motionplanned motion

external forces


Related Work: Computer Animation

• Composable controllers– Faloutsos et al., SIGGRAPH 2001

• Contact-rich motion control– Liu et al., SIGGRAPH 2010

• Both focus on motion control of various types of motions

• Not address the motion varieties– crucial for rising up motions


Related Work: Robotics

• Hot topic in humanoid research– Morimoto and Doya, IROS’98

– Fujiewara et al. IROS’03

– Hirukawa et al., IJRR’05

– Kanehiro et al., ICRA’07

• Focus on robustness instead of varieties and flexibilities

Hirukawa et al.


Related Work: Biomechanics

• Address analysis rather than generation of rising motions– McCoy and VanSant, Physical Therapy, 1993

– Ford-Smith and VanSant, Physical Therapy, 1993


Motion Planning Problem

Goal

Initial


Rapidly-exploring random tree (RRT)

Steve LaValle http://msl.cs.uiuc.edu/rrt/gallery.html

http://msl.cs.uiuc.edu/~lavalle

http://msl.cs.uiuc.edu/~lavalle


RRT-connect [Kuffner et al. 2000]

initx

goalx

bT aT

nearx



initx

goalx

bT aT

1. Ta executes EXTEND function



initx

goalx

randx

bT aT

2. Generate a random node xrand as a reference node



initx

goalx

randx

bT aT

nearx

3. Find xnear on Ta (nearest to xrand)



initx

goalx

randx

bT aT

nearx

4. Grow xnew toward xrand (within distance ε)

newx



initx

goalx

randx

bT aT

nearxnewx

5. Tb executes EXTEND function


RRT-blossom [Kalisiak & van de Panne, 2006]

• Blossom– add multiple samples

– explore space more quickly

Sub-goal Sub-goal

RRT-BlossomRRT


RRT-blossom

• Regression– avoids searching spanning nodes

– merge nearby nodes

Regression!

Regression


Initial postureInitial posture

Full-body RRT-blossomFull-body RRT-blossom

Ground Ground collisioncollision

Cut illegal motionCut illegal motionAdjust constraintAdjust constraint

Obstacle & Obstacle & Self collisionSelf collision

Smoothing and Smoothing and dynamics filteringdynamics filtering

MotionMotion

Cut illegal motion Cut illegal motion Adjust constraintAdjust constraint

Partial-body RRT-blossomPartial-body RRT-blossom

YesYes

YesYes

NoNo

NoNo

Connecting Connecting posture selectionposture selection

EnvironmentEnvironment

Stage IStage I

Stage IIStage II

Stage IIIStage III

Key postureKey posture


Connecting Posture Selection

• Posture

• Posture difference

• Accelerating search by clustering the motion database

},,{ VqpP

),(),(),( 212121 VVdistVqqdistQwPPdist q


Motion Planning Strategies

• Loose-to-strict iterative refinement

• Spatiotemporally local refinement

Full-body RRT-blossomFull-body RRT-blossom

Ground Ground collisioncollision

Cut illegal motionCut illegal motionAdjust constraintAdjust constraint

Obstacle & Obstacle & Self collisionSelf collision

Cut illegal motion Cut illegal motion Adjust constraintAdjust constraint

Partial-body RRT-blossomPartial-body RRT-blossom

YesYes

YesYes

NoNo

NoNo

Stage IIStage II

EnvironmentEnvironment


RRT-blossom Modifications

• RRT-blossom is originally proposed for lower-dimensional configuration space

• To handle motion planning in high- dimensional posture space– plan global orientation and joint angle separately

• Impose joint limit constraint and avoid collision in the blossom operation


Dynamics Filtering

• Track a planned motion using velocity-driven control [Tsai et al., TVCG 2010]

• Balance by virtual actuator control [Pratt et al.]

Dynamics Controller torques output

motionplanned motion

external forces


Dynamics Filtering (cont.)

• In some cases, our controller may not be able to track from squatting to standing– connect to a nearest rising motion in the database

– fine since less variations from squatting to standing


Results

• Our database only has14 motions of rising up on flat ground (CMU MOCAP database)

• Rising up from random initial postures

• Rising up with an initial and a key posture

• Rising up in various environments

• Motion retargeting of rising up


Rising from random initial poses

20 prone positions 20 lateral positions 20 supine positions


Rising from a sitting pose


Rising with given initial and key poses


Rising from prone with a key pose


Rising from lateral with a key pose


Rising from sitting with a key pose


Rising from different environments


Arm motion adapts to environments


Rising up under a table


Rising up on different ground


Motion Retargeting


Quality evaluation by human subjects

• score range from 10 (best) to 1 (worst)

• 27 males and 13 females aged 19 to 60


Conclusion

• Simple and effective approach– Small database + motion planning + dynamics

filtering

• Generate rising up motions with varieties– various lying postures and environments

– physically plausible

• Efficient motion planning strategy– Loose-to-strict spatiotemporally local refinement

strategy

rising from various lying postures

Documents

motion varietiescrucial

contactrich motion controlliu

physical therapy

distance rrt

arbitrary lying pose

motionsrelated work

functionrrtblossom kalisiak

small variationssmall