terrain runner: control, parameterization, composition ...terrain runner: control, parameterization,...

Terrain Runner: Control, Parameterization, Composition,

and Planning for Highly Dynamic Motions

Libin Liu* KangKang Yin† Michiel van de Panne‡ Baining Guo§

*Tsinghua University †National University of Singapore

‡University of British Columbia §Microsoft Research Asia

Motivation

[clips are from YouTube, uploaded by 3runTube, l1consolable,

ParkourGenerations, rubenparkour, traceurelements]

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Outline

• Motivation

• Related work

• Controller synthesis pipeline + results

• Conclusion

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Related Work

• Kinematic Methods– [Kovar et al. 2002; Heck and Gleicher 2007;

Min et al. 2009; Treuille et al. 2007; Lee et al. 2009]

• Physics-based Methods– Single controllers: [Hodgins et al. 1995;

Zhao and van de Panne 2005; Muico et al. 2009; Coros et al. 2010; Lee et al. 2010; Wang et al. 2009]

– Control Composition: [Faloutsos et al. 2001, Sok et al. 2007, da Silva et al. 2009, Muicoet al. 2011, Coros et al. 2011]

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System Overview

run

drop roll

jump

vault

single

controller

parameterization

composition

control

online planning

1

2

3

4

parameterized

controllers

single example

motion clips

run

jump

vault

drop roll

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Motion Examples

run

drop roll

jump

vault

single example

motion clips

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composition

control

System Overview

run

drop roll

jump

vault

single example

motion clips

parameterization

run

drop roll

jump

vault

online planning

2

3

4

parameterized

controllers

single

controller

1

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Stage 1: Single Controller Construction

run

drop roll

jump

vault

single example

motion clips

single

controller

1

a: Open loop policy

[Liu et al. 2010]

b: Reduced-order closed-loop

policy [Ding et al. 2012]

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Stage 1a: Open-loop Policy

[Liu et al. 2010]: Sampling-based Contact-rich Motion

Control, SIGGRAPH 2010

𝑎

𝑠

reference states

reference actions

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Stage 1b: Reduced-order Closed-loop Policy

𝑠 − 𝑠 = 𝛿𝑠

𝛿𝑎 = 𝑎 − 𝑎

[Ding et al. 2012]: Learning reduced-order feedback policies

for motion skills. Tech. Rep. University of British Columbia.

𝛿𝑎 = 𝑀 𝛿𝑠 + 𝑎

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𝑠 − 𝑠 = 𝛿𝑠


change in control change in states

𝑀𝑎𝑝 ⋅ 𝑀𝑠𝑝

𝛿𝑎 = 𝑎 − 𝑎

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𝛿𝑠


change in control change in states

𝑀𝑎𝑝 ⋅ 𝑀𝑠𝑝

𝛿𝑎

𝑀𝑠𝑝𝑀𝑎𝑝

reduced-order state

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Stage 1b: Feedback Policy

Manually-selected States: s

• Running: 12 dimensions

{ , , , }

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Manually-selected Controls: a

• for all skills: 9 dimensions

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• Vaulting

phase 1: raising phase 2: dropping phase 3: landing


Multi-phase Skills

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Multi-phase Skills

• Drop-rolling

phase 1: jumping phase 2: dropping phase 3: rolling phase 4: standing-up

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• Jumping

• Vaulting

• Drop-rolling


Manually-selected States: s

}{

{ }

}{

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• Optimize 𝑀

– CMA, Covariance Matrix Adaption ([Hansen 2006])

– Running:

• Objective function

• 12 minutes on 24 cores

• more details in paper and [Ding et al. 2012]

𝛿𝑎 = 𝑀𝛿𝑠 + 𝑎

𝐸 = 𝑤𝑡 𝑁𝑑𝑇𝑐 − 𝑇𝑠 + 𝑤𝑠𝐸𝑠 + 𝑤𝑝𝐸𝑝 + 𝑤𝜏𝐸𝜏


Optimization

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composition

control

single

controller

1

System Overview

run

drop roll

jump

vault

single example

motion clips

online planning

3

4

parameterization

2

run

drop roll

jump

vault

parameterized

controllers

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Stage 2: Parameterization

𝛿𝑎 = 𝑀𝜃 𝛿𝑠 + 𝑎𝜃

𝑎𝜃

𝜃

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Running: parameter space

• 𝜃 = (v, 𝜙)

– speed, turning rate

– [2m/s, 5m/s] × [−6°, 6°]/step

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𝒓𝒍

𝑡 𝑡 𝑡 𝑡


Running: Action Set Augmentation

• 𝑎𝜃 = , , 𝛼 , 𝛽

space scaling time scaling

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Running: optimization

• 𝑀𝜃 , 𝑎𝜃

success head’s stability

desired parameters

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Continuation

• [Yin et al. 2008]: Continuation methods for adapting

simulated skills. SIGGRAPH 2008Radial Basis Functions

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Continuation

• Predictor-corrector

…

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Running Results

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Obstacle Clearing Maneuvers

• 𝜃 = ℎ

– Obstacle height

• Jumping – [0.1m, 0.7m]

• Vaulting – [0.6m, 1.0m]

• Drop-rolling – [0.9m, 2.0m]

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• Optimization

– 𝑎ℎ = { , 𝛼, 𝛽}

contact balance pose

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• Jumping

– Contact term 𝐸𝑐

ℎ ℎ𝑓

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ℎ𝑟 ℎ𝑑ℎ ℎℎ

𝑑ℎ



• Vaulting

– Contact term 𝐸𝑐

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• Drop-rolling

– Balance term 𝐸𝑏

𝑑𝑟 𝑑𝑙

𝑑

𝐿

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Obstacle Clearing Results

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single

controller

1

System Overview

run

drop roll

jump

vault

single example

motion clips

parameterization

online planning

2

4

parameterized

controllers

run

drop roll

jump

vault

3

composition

control

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Stage 3: Composition

Three-phase Composition Scheme

clear

transIn transOutrun run


𝛿𝑎 = 𝑀𝜃𝑖𝑛

′ 𝛿𝑠 + 𝑎𝜃𝑖𝑛𝛿𝑎 = 𝑆𝜃𝑜𝑢𝑡

𝛿𝑠

𝛿𝑎 = 𝑀ℎ′ 𝛿𝑠 + 𝑎ℎ

Obstacle

𝛿𝑎 = 𝑀𝜃𝑖𝑛𝛿𝑠 + 𝑎𝜃𝑖𝑛

𝛿𝑎 = 𝑀𝜃𝑜𝑢𝑡𝛿𝑠 + 𝑎𝜃𝑜𝑢𝑡

clear

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Optimization

• Parameters

– 𝜃𝑖𝑛, 𝑀𝜃𝑖𝑛

′ , 𝑀ℎ′ , 𝑆𝜃𝑜𝑢𝑡

transIn transOut

𝛿𝑎 = 𝑀𝜃𝑖𝑛

′ 𝛿𝑠 + 𝑎𝜃𝑖𝑛𝛿𝑎 = 𝑆𝜃𝑜𝑢𝑡

𝛿𝑠

𝛿𝑎 = 𝑀ℎ′ 𝛿𝑠 + 𝑎ℎ

run run

clear


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Results

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single

controller

1

System Overview

run

drop roll

jump

vault

single example

motion clips

parameterization

run

drop roll

jump

vault

composition

control

online planning

2

3

4

parameterized

controllers

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Stage 4: Online Planning

• Step-based kinematic planning

min𝑤𝑢 𝑢 − 𝑢ℎ2 + 𝑤𝑖 𝑣𝑖 − 𝑣𝑖−1

2

s. t. 𝑣𝑛 = 𝑣𝑖𝑛

𝑢, 𝑣𝑛

𝑣𝑖𝑛

𝑢ℎ

𝑢, 𝑣𝑛

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Stage 4: Planning

Results

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Results

Terrain Running

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Conclusion

• Parkour-style motions

– Running, jumping, vaulting, drop-rolling

• Complete framework, Realtime synthesis

– Control construction, parameterization, composition,

planning

• Structured optimization scheme

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Limitations

• Only partly automated

• Composition can fail

• No arbitrary transitions

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Future Work

[Parkour Memories, uploaded by 3runTube

http://www.youtube.com/watch?v=24cgnAA6x0I&hd=1]

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Thanks

terrain runner: control, parameterization, composition ...terrain runner: control, parameterization,...

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