Post on 25-Feb-2016
description
Rising from Various Lying Postures
Wen-Chieh Lin and Yi-Jheng HuangDepartment of Computer ScienceNational Chiao Tung University, Taiwan
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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
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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
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Basic Idea
• Small database for typical rising motions
• Motion planning for large variations
• Dynamics filtering for small variations
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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
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Motion planning for large variations
• Connects an arbitrary lying pose to database– avoids collisions
– satisfies constraints
rising motion databasevarious lying postures
. . . . ..
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Dynamics filtering for small variations
• Ensures physical plausibility
• Adapts to environments and characters
Dynamics Controller torques output
motionplanned motion
external forces
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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
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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.
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Related Work: Biomechanics
• Address analysis rather than generation of rising motions– McCoy and VanSant, Physical Therapy, 1993
– Ford-Smith and VanSant, Physical Therapy, 1993
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Motion Planning Problem
Goal
Initial
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Rapidly-exploring random tree (RRT)
Steve LaValle http://msl.cs.uiuc.edu/rrt/gallery.html
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RRT-connect [Kuffner et al. 2000]
initx
goalx
bT aT
nearx
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RRT-connect [Kuffner et al. 2000]
initx
goalx
bT aT
1. Ta executes EXTEND function
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RRT-connect [Kuffner et al. 2000]
initx
goalx
randx
bT aT
2. Generate a random node xrand as a reference node
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RRT-connect [Kuffner et al. 2000]
initx
goalx
randx
bT aT
nearx
3. Find xnear on Ta (nearest to xrand)
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RRT-connect [Kuffner et al. 2000]
initx
goalx
randx
bT aT
nearx
4. Grow xnew toward xrand (within distance ε)
newx
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RRT-connect [Kuffner et al. 2000]
initx
goalx
randx
bT aT
nearxnewx
5. Tb executes EXTEND function
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RRT-blossom [Kalisiak & van de Panne, 2006]
• Blossom– add multiple samples
– explore space more quickly
Sub-goal Sub-goal
RRT-BlossomRRT
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RRT-blossom
• Regression– avoids searching spanning nodes
– merge nearby nodes
Regression!
Regression
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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
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Connecting Posture Selection
• Posture
• Posture difference
• Accelerating search by clustering the motion database
},,{ VqpP
),(),(),( 212121 VVdistVqqdistQwPPdist q
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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
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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
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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
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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
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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
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Rising from random initial poses
20 prone positions 20 lateral positions 20 supine positions
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Rising from a sitting pose
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Rising with given initial and key poses
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Rising from prone with a key pose
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Rising from lateral with a key pose
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Rising from sitting with a key pose
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Rising from different environments
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Arm motion adapts to environments
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Rising up under a table
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Rising up on different ground
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Motion Retargeting
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Quality evaluation by human subjects
• score range from 10 (best) to 1 (worst)
• 27 males and 13 females aged 19 to 60
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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
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