may 14, 2014 on the dynamics of human locomotion and co-design of lower limb assistive devices 1 on...
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1May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
On the dynamics of human locomotion and co-design of lower
limb assistive devices
Jesse van den Kieboom
Biorobotics Laboratory, EPFL, Lausanne
PhD oral exam, May 14, 2014
Supervisor: prof. Auke Jan Ijspeert
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2May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
INtroduction
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Source: youtube.com
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3May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
introduction
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4May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Introduction
• Body versus mind, embodied intelligence
• Morphology determines how interaction takes place
• Strong notion in natural system
• Adaptation of morphology is a product of natural evolution
Motivation
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5May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Introduction
• EVRYON: evolving morphologies for human-robot symbiotic interaction (7th framework programme ICT-2007.8.5 Embodied intelligence)
• Incorporate morphological design prominently into the design phase
• Not always obvious, since performance is a combination of morphology and control
• Take inspiration from natural processes to co-design morphology and control using dynamical simulations
Motivation
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6May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Introduction
• Development of a methodology for the co-design of bipedal machines, with a case study in wearable lower limb devices
• Study of principles of human gait optimization and control
• Rigorous modeling of coupled dynamical systems and rigid body dynamics suitable for locomotion and co-design
Topics
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7May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Overview
1. Introduction
2. Dynamical systems
3. Human gait optimization
4. Co-design methodology for wearable devices
5. Conclusion
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8May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
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9May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
•Generally: a system which changes over time
•Fundamentally multi-domain
•Specifically interested in
•Control dynamics
•Rigid body dynamics
•Neuro-mechanical dynamics
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
• A framework for modeling and integrating multi-domain, coupled dynamical systems
• Motivations
• Free/open
• Expressive/modeling
• Performance
• Educational
codyn - coupled dynamical systems
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
Modeled after IJspeert et al. (2007)
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Dynamical systems
• Articulated rigid body dynamics essential tool in robotics research (modeling, simulation, control)
• Hard problem, a variety of simulators existing today
• Accuracy vs. performance vs. modeling effort
codyn - Rigid Body Dynamics
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Dynamical systems
Free/open
Accurate
FastMulti-
domainHard
contacts
Multi-body
contacts
Modeling
Embedded
Bullet/ode
+ - ++ - - ++ - -
OpenSim
+ + - +/- - + +/- -
Robotran
- + - + - - +/- -
Codyn + + ++ + + - ++ +
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
• Multi-domain: rigid body dynamics formulation indifferent from other dynamics, well suited for neuro-mechanical/musculo-skeletal modeling (Taga, 1995; Geyer, 2010)
• Equations of motion derived from (Featherstone, 2008), transformed into coupled dynamics formulation of codyn
• General purpose, 3D articulated rigid body dynamics
• Entirely user extensible, customized joint models, contact models
• State of the art: Availability of inverse, forward, closed chain, hard contacts, Jacobians, etc.
codyn - Rigid Body Dynamics
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Dynamical systems
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Dynamical systems
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Dynamical systems
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Dynamical systems
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dynamical systems
• Generalized coordinates for RBD provides accuracy
• When implemented naively, has very poor performance
• Automatically translate models to efficient representation
• Fast, optimized code
• Suitable for Real Time systems
• Suitable for low-resource, embedded systems (for example micro-controllers (Knüsel, 2013))
codyn - Performance
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dynamical systems
codyn - Performance
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dynamical systemshttp://www.codyn.net/
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Human gait optimization
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
1. What is the minimal, sufficient model for human gait optimization?
2. What are the objectives leading to stable, human gait?
3. What is the role of impedance modulation for human gait?
4. How prominent is the morphology for optimization of human gait?
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
• Extensively studied
• Neuro muscolu-skeletal control using oscillators and entrainment (Taga, 1995)
• Musculo-skeletal reflex models (Geyer, 2010)
• Optimal control approaches (Mombaur, 2010)
• Passive dynamic walkers (McGeer, 1990; Wisse 2005;
Kuo 2007)
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
• Motivation
• To obtain a minimal, sufficient optimization model to explain human locomotion
• Using simple, local control only, few parameters
• Main inspiration: passive dynamic walkers
Collins (2001)
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
1. Human like gaits occur implicitly using impedance control by optimizing only for mechanical energy expenditure
2. Gait quality increases with increasing modulation of impedance
3. Energy expenditure decreases with increasing modulation of impedance
Hypotheses
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Level of abstraction
• 2D, planar walking
• Segmented legs (upper leg, lower leg, foot)
• Perfect torque actuators at the joints
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human gait optimization
Control• Joint level variable impedance control
• Optimize reference trajectory, variable stiffness and variable damping
• Various levels of variable impedance: const, step, ppoly
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
Objective Until1 time max time
2 speed match 95%
3 std speed 0.1
4 assist time 0
5non contact
time0
6 torque -
human gait optimization
Optimization
• Particle Swarm Optimization (Kennedy, 1995)
• Codyn for modeling of control and rigid body dynamics
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human gait optimization
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human gait optimization
Single step (stance to stance)
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human gait optimization
Single step (stance to stance)
Hip, knee, ankle
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Human like gaits occur implicitly using impedance control by optimizing only for mechanical energy expenditure
Gait quality increases with increasing modulation of impedance
Energy expenditure decreases with increasing modulation of impedance
Hypotheses
✓
✓
✓
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Transfer of methodology to a human-like platform
1. Effect of morphological differences to bipedal gait obtained from optimization
2. Role of variable impedance under perturbation
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human gait optimization
Source: IIT website
Transfer of methodology to a human-like platform
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Transfer of methodology to a human-like platform
• Improved objectives due to hard-contact modeling in codyn (remove std speed and non-
contact-time)
• Optimize for (mechanical) cost of transport instead of desired speed
• Adaptation of foot length to size of the robot
• Limit maximum torques to platform limits (30N)
Same optimization methodology except:
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human gait optimization
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
• Foot length important for obtaining robust gaits
• Mass distribution influences stability, making it harder to optimize
• Resulting gait slow, maximum torque limits
Observations
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human gait optimization
Transfer of methodology to a human-like platform
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Transfer of methodology to a human-like platform
• Apply force perturbations during window in swing phase
• N(80, 5) Newton
• N(150, 50) Milliseconds
• Same optimization methodology
Role of variable impedance under perturbation
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human gait optimization
Transfer of methodology to a human-like platform
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human gait optimizationHip, knee, ankle
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
human gait optimization
Transfer of methodology to a human-like platform
• Successfully optimized human-like gait to robot morphology
• Foot length is very important for obtaining gait
• Mass distribution influences stability
• Variable impedance modulation significant under perturbation
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Morphology/control co-design
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co-design
• Many successful (some commercial) wearable lower limb devices
• Impressive results: allows for paraplegics to walk again
• Design is always anthropomorphic
• Potential for exploitation of morphology
Motivation
Ekso ReWalk Rex
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co-design
• Development of a co-design methodology, using evolutionary inspired processes
• EVRYON: use case for a non-anthropomorphic, lower limb wearable device for locomotion
• A framework for the iterative co-design of wearable devices, focussing on open-ended exploration of solutions
Problem domain
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co-design
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co-design
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co-design
• Same human model as in previous studies (2D, planar)
• Looking at mechanical dynamics only
• Augmentation of the hip and knee only (2 dof parallel structures)
• Parallel structures involving 3 human body attachment joints, 3 free wearable robot joints and 4 wearable robot segments
Level of abstraction
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
co-design
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May 14, 2014 On the dynamics of human locomotion and co-design of lower limb assistive devices
co-design
• Wearable robot attachment placement
• Active joint placement
• Wearable robot segment lengths
• Wearable robot initial joint angles
Morphological space
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co-design
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• Solutions obtained were never self-stable, torso was constraint to be upright (mass-distribution)
• Ground contact modeling problematic, unstable
• Almost all solutions made use of structural singularities
• Main conclusion: promising, however available tools are insufficient
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• Optimization no longer only considering continuous parameters only
• Particle Swarm Optimization in itself not suitable
• Consider Genetic Algorithms (Goldberg, 1987) or Genetic Programming (Koza, 1992)
• Many parameters which significantly alter performance, PSO found to perform better for locomotion (Bourquin, 2004)
• A novel PSO for co-design
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• Meta morphic particle swarm optimization
• Extension of PSO principles to simultaneous exploration/optimization of solution structures and its parameters
• Suitable for problems where solution structures can be enumerated
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co-design• Structural parameters (MMPSO)
• Topology
• Actuator placement
• Morphological parameters
• Attachment location
• Wearable robot joint location
• Control parameters
• Joint angle pattern, stiffness pattern, damping pattern
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Topo Mass CoM (up)Segment
sizePower
283.3
(+13.3)1.07 (-0.03) 0.3 - 0.46 462
583.9
(+13.9)1.02 (-0.08) 0.2 - 0.52 748
383.5
(+13.5)1.03 (-0.07) 0.1 - 0.64 1018
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co-design
• Development of a complete framework for co-design (dynamics/control, optimization, simulation)
• Successfully optimize human like gaits with parallel structures
• Automatic and simultaneous exploration of solution structures and their parameters
• Mass distribution particularly important
• Actuator placement favors actuator on the torso
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Conclusion
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conclusion
• Open and freely available, framework for modeling of multi-domain coupled dynamics systems
• State of the art, competitive rigid body dynamics simulator
• Novel particle swarm optimization based algorithm for co-optimization of solution structures and their parameters
• Robust optimization of human gait from first principles using simple, local impedance control
• Front to end framework for the co-design of morphology and control of robotic structures
Main contributions
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conclusion
• Human locomotion
• Extension of methodology to 3D
• Transfer of control to robotic platform
• Feedback based variable impedance control
Discussion
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conclusion
• Co-design
• Methodology shown to be feasible
• Should provide input in a larger, iterative design process
• Open-ended search does not provide complete solutions
Discussion
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conclusion
• Co-design
• Methodology shown to be feasible
• Should provide input in a larger, iterative design process
• Open-ended search does not provide complete solutions
• Only looked at mechanics so far, no human-in-the-loop
• Integration of neuro/musculo skeletal models, studying human adaptation
Discussion
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conclusion
• Within constraints of EVRYON, obtained wearable robot solutions did not provide the aimed for dynamic symbiosis purely using evolutionary algorithms
• Applying the methodology to smaller subsystems may be more promising
• Use an iterative design approach (engineer-in-the-loop), rather than a front-to-end automatic approach
• Importance of human-in-the-loop cannot be ignored, but tools need to be suitable for such simulations
Lessons learned
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Questions
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Co-design
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References1. Ijspeert, A. & Crespi, A. & Ryczko, D. & Cabelguen, J.-M. (2007). From swimming to walking with
a salamander robot driven by a spinal cord model. Science,315, 1416—1420.
2. Taga, G. (1995). A model of the neuro-musculo-skeletal system for human locomotion. I. Emergence of basic gait.Biological Cybernetics,73, 97—111.
3. Geyer, H. & Herr, H. (2010). A Muscle-Reflex Model That Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities. Neural Systems and Rehabilitation Engineering, IEEE Transactions on,18, 263—273.
4. Mcgeer, T. (1990). Passive dynamic walking. the international journal of robotics research,9, 62—82.
5. Wisse, M. (2005). Three additions to passive dynamic walking: actuation, an upper body, and 3D stability.International Journal of Humanoid Robotics,2, 459—478.
6. Kuo, A. D. (2007). The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective.Human movement science,26, 617—656.
7. Mombaur, K. & Truong, A. & Laumond, J. (2010). From human to humanoid locomotion—an inverse optimal control approach. Autonomous robots,28, 369—383.
8. Knüsel, J. (2013). Modeling a diversity of salamander motor behaviors with coupled abstract oscillators and a robot. EPFL.
9. Goldberg, D. E. & others , . (1989). Genetic algorithms in search, optimization, and machine learning. Addison-wesley Reading Menlo Park,412.
10.Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT press,1.
11.Bourquin, Y. & Ijspeert, A. J. & Harvey, I. (2004). Self-organization of locomotion in modular robots. Unpublished Diploma Thesis, http://birg. epfl. ch/page53073. html.
12.Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE international conference on neural networks,4, 1942—1948 vol.4.
13.Featherstone, R. (2008). Rigid body dynamics algorithms. Springer New York,49.
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human gait optimization
Transfer of methodology to a human-like platform
Step controller kinematics
Single step (stance to stance)
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HUMAN GAIT OPTIMIZATION