Sampling-based Contact-rich
Motion Control
Libin Liu*
KangKang Yin† Michiel van de Panne‡
Tianjia Shao*
Weiwei Xu†
* Tsinghua University
† Microsoft Research Asia
‡ University of British Columbia
Motivation
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Motivation
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Motivation
Physically-plausible motion reconstruction
Input
Output
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Motivation
Retargeting to new characters
Direct tracking
Our open-loop control
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Motivation
Retargeting to new environments
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Motivation
Synthesis of difficult-to-capture motions
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Outline
• Motivation
• Related Work
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Related Work
• Motor Control and Contact Dynamics
[Kawato 99, Jordan and Wolpert 99, Brubaker et al. 09]
• Motion Planning
[Kavraki et al. 96, LaValle and Kuffner 00, Choi et al. 03,
Yamane et al. 04]
• Sampling in Animation
[Sims 94, Hodgins and Pollard 97, Twigg and James 07,
Sok et al. 07, Wang et al. 09, Wampler and Popović 09]
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Outline
• Motivation
• Related Works
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Simulation Basics
• PD servo
  k p  -   kd  
~
• Open Dynamics Engine v0.11
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Trajectory-based sampling
Sampling
[
]
t
t + Δt
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Trajectory-based sampling
Steps
position
time
t0
t1
t2
t3
t4
t5
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Trajectory-based sampling
Steps
t += Δt
Sample
Simulate
Calculate cost
Select and prune
End?
No
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Outline
• Motivation
• Related Works
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Practical Implementations
Sample generation
• Sampling window size
– Noise level
– Joint activeness
– Stability of motion dynamics
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Practical Implementations
Feedforward torques
[ [] ]
t
t + Δt
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Practical Implementations
Sampling time step
• Small vs. Large
• Uniform vs. Semantic
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Practical Implementations
Simulations in Parallel
• Master-worker model
Master
Worker
sample selection
&maintenance
Worker
sample generation & simulation & cost evaluation
Worker
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Trajectory-based sampling
Sample Cost Evaluation
E  wp E p  wr Er  we Ee  wb Eb
• Pose Control :
• Root Control:
• End-effector Control:
• Balance Control:
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Practical Implementations
Sample Pruning
greedy
diversified
cost
cost
f
t
t
x
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Outline
• Motivation
• Related Works
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Idling
Trajectory-free Sampling
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Idling
Dynamic RRT: DoF pruning
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Idling
Dynamic RRT: Space Pruning
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Outline
• Motivation
• Related Works
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Results
Performance
Trial
walk
run
sideways roll
forward roll
backward roll
get-up
kip-up
Duration (s) Reconstruction time (s)
5.2
143
2
51
3
78
3
78
2.1
57
3.5
93
6.6
184
1400 samples for each iteration with success rate > 80%
~25 times slower than real time on cluster of 80 cores
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Results
Control Reconstruction
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Results
Motion Transformation
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Results:
Motion Retargeting
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Results
Trajectory-free Control Construction
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Results
Motion Composition
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Outline
• Motivation
• Related Works
• Control Construction
– Trajectory-based sampling
– Practical implementations
– Trajectory-free sampling
• Results
• Discussion
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Advantages of Sampling
• Derivative-free
• General
• Robust wrt. local minima
• Easy to parallelize
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Limitations
• Robustness of Reconstruction:
<100%
• Robustness of Control: open-loop
• Generalization: trajectory-tracking nature
• Smoothness: can be jerky
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Conclusions
• Sampling-based reconstruction method
general, robust, parallelizable
• Contact-rich tasks
get-up, rolling, idling
• Unified framework
inverse dynamics, motion transformation and retargeting
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Acknowledgement
• Our mocap subjects
• Organizations and authors who generously shared
their mocap data
• Xiao Liang, Teng Gao, Kevin Loken
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Thank you