FOR MANY-MUSCLE HUMANOIDS Yoonsang Lee 1,2 Moon Seok Park 3 Taesoo - - PowerPoint PPT Presentation

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FOR MANY-MUSCLE HUMANOIDS Yoonsang Lee 1,2 Moon Seok Park 3 Taesoo - - PowerPoint PPT Presentation

May 12, 2023 337 likes •955 views

LOCOMOTION CONTROL FOR MANY-MUSCLE HUMANOIDS Yoonsang Lee 1,2 Moon Seok Park 3 Taesoo Kwon 4 Jehee Lee 1 1 Seoul National University 2 Samsung Electronics Co., Ltd. 3 Seoul National University Bundang Hospital 4 Hanyang University Human Movements

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LOCOMOTION CONTROL FOR MANY-MUSCLE HUMANOIDS

Yoonsang Lee1,2 Moon Seok Park3 Taesoo Kwon4 Jehee Lee1

1 Seoul National University 2 Samsung Electronics Co., Ltd. 3 Seoul National University Bundang Hospital 4 Hanyang University

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Human Movements

  • Complex musculoskeletal system
  • Coordination of muscle activation
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SLIDE 3

Why Many-Muscles?

  • Enough for complex movements?

Lee et al. 2010 Wang et. al. 2012 Geijtenbeek et. al. 2013

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Goal

  • Controlling locomotion with

complex musculoskeletal system

  • Arbitrarily many (100+) muscles
  • Predicting new gait patterns

under varied conditions

  • Pathologic gait patterns
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SLIDE 5

Yin et al. 2007 Coros et al. 2010 Wang et al. 2009 Kwon et al. 2010 Mordatch et al. 2010 Sok et al. 2007 Lee et al. 2010

Related Work - Biped Control

Lasa et al. 2010 Wu et al. 2010 Muico et al. 2009 Liu et al. 2012 Brown et al. 2013 Al Borno et al. 2013

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SLIDE 6

Yin et al. 2007 Coros et al. 2010 Wang et al. 2009 Kwon et al. 2010 Mordatch et al. 2010

FSM / Simple Models

Related Work - Biped Control

Sok et al. 2007 Lee et al. 2010 Lasa et al. 2010 Wu et al. 2010 Muico et al. 2009 Liu et al. 2012 Al Borno et al. 2013 Brown et al. 2013

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SLIDE 7

Yin et al. 2007 Coros et al. 2010 Wang et al. 2009 Kwon et al. 2010 Mordatch et al. 2010 Sok et al. 2007 Lee et al. 2010

Motion Capture Data FSM / Simple Models

Related Work - Biped Control

Lasa et al. 2010 Wu et al. 2010 Muico et al. 2009 Liu et al. 2012 Al Borno et al. 2013 Brown et al. 2013

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SLIDE 8

Yin et al. 2007 Lasa et al. 2010 Wu et al. 2010 Coros et al. 2010 Wang et al. 2009 Kwon et al. 2010 Mordatch et al. 2010 Sok et al. 2007 Muico et al. 2009 Lee et al. 2010

Motion Capture Data FSM / Simple Models Optimization

Related Work - Biped Control

Liu et al. 2012 Al Borno et al. 2013 Brown et al. 2013

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SLIDE 9

Wang et. al. 2012 Geijtenbeek et. al. 2013 Mordatch et. al. 2013 Lee et. al. 2009 Zordan et. al. 2004 Sueda et. al. 2008 Lee & Terzopoulos 2006 Anderson & Pandy 1999 Thelen et. al. 2003 Thelen et. al. 2006 Nakamura et. al. 2004

Related Work – Musculoskeletal Analysis & Simulation

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Wang et. al. 2012 Geijtenbeek et. al. 2013 Mordatch et. al. 2013 Lee et. al. 2009 Zordan et. al. 2004 Sueda et. al. 2008 Lee & Terzopoulos 2006 Anderson & Pandy 1999 Thelen et. al. 2003 Thelen et. al. 2006 Nakamura et. al. 2004

Related Work – Musculoskeletal Analysis & Simulation

Specific Body Parts

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SLIDE 11

Wang et. al. 2012 Geijtenbeek et. al. 2013 Mordatch et. al. 2013 Lee et. al. 2009 Zordan et. al. 2004 Sueda et. al. 2008 Lee & Terzopoulos 2006 Anderson & Pandy 1999 Thelen et. al. 2003 Thelen et. al. 2006 Nakamura et. al. 2004

Related Work – Musculoskeletal Analysis & Simulation

Musculoskeletal Analysis Specific Body Parts

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SLIDE 12

Wang et. al. 2012 Geijtenbeek et. al. 2013 Mordatch et. al. 2013 Lee et. al. 2009 Zordan et. al. 2004 Sueda et. al. 2008 Lee & Terzopoulos 2006 Anderson & Pandy 1999 Thelen et. al. 2003 Thelen et. al. 2006 Nakamura et. al. 2004

Related Work – Musculoskeletal Analysis & Simulation

Musculoskeletal Analysis Locomotion Control & Synthesis Specific Body Parts

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  • Underdetermined system (muscle redundancy)
  • What is best motion for a given situation? (adaptability)
  • Complexity of muscle contraction dynamics

Challenges of Many-Muscle Control

=

  • Multiple sets of

muscle forces Same joint torque

  • # muscles > # total DOFs

Integrated controller design

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Our Approach

  • Find optimal muscle actuation

considering nonlinear muscle dynamics

  • Seamlessly integrating muscle

dynamics into QP formulation

  • Muscle optimization
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Our Approach

  • Gait adaptation under various conditions
  • Finding best motion for given

condition by offline optimization

  • Trajectory optimization
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L

Left Ankle Plantar Flexor Weakness

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Musculoskeletal Models

Delp et al. 1990; Anderson and Pandy 1999 Steele and Hamner 2013

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L

Gait2562 (25 DOFs, 62 muscles) Gait2592 (25 DOFs, 92 muscles) Fullbody (39 DOFs, 120 muscles)

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Muscle Activation

activation=1 activation=0

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Hill-Type Muscle Model

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Hill-Type Muscle Model

SE : serial element CE : contractile element PE : passive element α: pennation angle

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Hill-Type Muscle Model

SE : serial element CE : contractile element PE : passive element α: pennation angle

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Hill-Type Muscle Model

SE : serial element CE : contractile element PE : passive element α: pennation angle

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Muscle Force Generation

fmt fmt

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Muscle Force Generation

fmt fmt l

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SLIDE 27

fmt fmt

Contraction Dynamics

l

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Many-Muscle Control

  • Muscle optimization
  • Optimal muscle activation under

physics laws & muscle dynamics

  • Trajectory optimization
  • Modulates reference motion for

robustness & adaptability

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Many-Muscle Control

  • Muscle optimization
  • Optimal muscle activation under

physics laws & muscle dynamics

  • Per-frame tracking simulation
  • Trajectory optimization
  • Modulates reference motion for

robustness & adaptability

  • Offline modulation
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Muscle

  • ptimization

Integration

Simulation

Reference motion

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Simulation Muscle

  • ptimization

Integration Reference motion

Trajectory

  • ptimization

Original reference motion Optimized reference motion

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SLIDE 32

Trajectory

  • ptimization

Original reference motion Optimized reference motion

Simulation

Online Simulation Offline Modulation

Muscle

  • ptimization

Integration Optimized reference motion

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Muscle Optimization

  • Finds best (muscle activation, acceleration, contact

force) to follow reference motion.

  • Muscle activation - resolving muscle redundancy.
  • Acceleration & contact force - optimal results under

physics laws.

  • Reference motion is adjusted by balance strategy by

[Kwon & Hodgins 2010].

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SLIDE 34
  • Objective

Effort Contact force Tracking End-Effectors

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  • Objective
  • Inequality Constraints

v1 v2 v3 v4 f 𝒈 = 𝜇1𝒘𝟐 + 𝜇2𝒘𝟑 + 𝜇3𝒘𝟒 + 𝜇4𝒘𝟓

Effort Contact force Tracking End-Effectors Friction cone Non-penetration Muscle activation

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Equality Constraint - Equation of Motion

(muscle force) + (contact force)

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(muscle force) + (contact force)

Equality Constraint - Equation of Motion

. . .

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Quadratic Programming

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Trajectory Optimization

  • Modulates reference motion to
  • Reproduce original reference motion more

accurately and robustly

  • Adapt to new conditions and requirements
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Trajectory Optimization

  • Optimize foot trajectories only
  • Most essential components of fullbody gaits
  • Step locations is a key factor for balance
  • Represented by

× 3 key frames

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Trajectory Optimization

  • Objective
  • Pose difference
  • Falling down
  • Efficiency (consumed energy / move distance)
  • Contact force
  • Muscle force
  • Covariance Matrix Adaptation
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SLIDE 42
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Unilateral Painful Ankle Plantar Flexor

  • People tend to reduce the use
  • f the ankle plantar flexor.
  • Minimizing muscle force of

left ankle plantar flexor

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Painful Joints on Unilateral Limb

  • People tend to reduce contact

force of the limb.

  • Minimizing contact force of

left limb

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Painful Left Ankle Plantar Flexor Painful Joints on Left Leg

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Bilateral Gluteus Medius & Minimus Weakness

  • Waddling gait is observed for

these people.

  • Scaling maximum isometric

force by 0.4

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SLIDE 49
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Unilateral Gluteus Medius & Minimus Weakness

  • Trendelenburg gait is observed

for these people.

  • Scaling maximum isometric

force by 0.2

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SLIDE 51
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Hamstrings, PsoaiTightness & Ankle Plantar Flexors Weakness

  • Most common reason for

Crouch gait

  • Scaling tendon slack length &

maximum isometric force

  • by 0.8 (tightness) & by 0.2

(weakness), respectively

psoai hamstrings

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SLIDE 53
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Unilateral Dislocation of Hip

  • Trendelenburg gait is observed

for these people.

  • Moving left hip joint 3 cm in

the lateral direction

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Comparison with EMG data

* *Reported by Demircan et al. [2009]

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Discussion

  • First locomotion controller for “many-

muscle” humanoids developed for clinical purpose.

  • Shows details of humanoids to reproduce

various pathologic gait patterns

  • Virtual surgical planning
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Acknowledgements

  • Thanks to anonymous reviewers
  • Funding
  • National Research Foundation of Korea (NRF)

No.2011-0018340 , No. 2007-0056094.

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SLIDE 60

Locomotion Control for Many-Muscle Humanoids

Yoonsang Lee Moon Seok Park Taesoo Kwon Jehee Lee