CH3: Biomechanics and Neural Mechanisms of Robots

Adaptive Locomotion Control: From Animals to Robots

CH3: Biomechanics and Neural Mechanisms of Robots

Poramate Manoonpong ([email protected])www.manoonpong.com/ALCLecture/CH3

mailto:[email protected]

http://www.manoonpong.com/ALCLecture/CH3

• Poramate Manoonpong (POMA, [email protected] )

• Shao Donghao ([email protected] )

• Zhang Yanbin ([email protected] )

• Sun Tao ([email protected] )

• Dong Yi ([email protected] )

• Potiwat Ngamkajornwiwat ([email protected] )

• Worasuchad Haomachai ([email protected] )

• Kobe Kang ([email protected] )

• 3 credits

• Autumn 2020 (4 hours / Block )→ One Block/ week (8:00 to 12:00)

• Lectures, exercises, a project for your report

Lecture (Theory: up to 2-3 hours of each block):

Exercises & project (Practice: Robot simulation):

Link: https://mooc1.chaoxing.com/course/215088272.html?edit=true&articleId=2274940122

About the course









https://mooc1.chaoxing.com/course/215088272.html?edit=true&articleId=227494012

3

• Evaluation: 20% (exercise, attendance, Q&A) + 80% Final exam

• Final Exam: individual written report based on project and evaluated with an external censor. Your report must not be identical to your colleague!!

• Assessment: report (as a paper) presents “locomotion control mechanisms and result”(deadline by January 5th, 2021) , the report must be submitted as a pdf file via email to [email protected]

→ Please wrtie your email subject as ”Submit ALC report 2020”

• The report (11-page MAX length) with the given format must contain:

1) Abstract, 2) Introduction, 3) Materials and Methods, 3) Experiments and Results (link to your results, e.g., on web), 4) Discussion & Conclusions, 5) Acknowledgements, 6) References

>> You can get feedback from us!!! before the deadline

The template of the report can be downloaded from www.manoonpong.com/ALCLecture/Report_Template.docx

www.manoonpong.com/ALCLecture/Report_Template.pdf

We will not evaluate the report that does not follow the given format !!

IF there are similar reports, both will fail!

About the course


http://www.manoonpong.com/ALCLecture/Report_Template.docx

http://www.manoonpong.com/ALCLecture/Report_Template.pdf

Recommended books:

1) How the Body Shapes the Way We Think: A New View of Intelligence, by Rolf Pfeifer , Josh Bongard

2) Neural Preprocessing and Control of Reactive Walking Machines: Towards Versatile Artificial Perception-Action Systems (Cognitive Technologies), by Poramate Manoonpong

3) Adaptive Neural Control of Walking Robots by M.J. Randall

4) Principles of Insect Locomotion by H. Cruse, V. Dürr, M. Schilling, J. Schmitz

5) Neuronal Control of Locomotion: From Mollusc to Man (Oxford Neuroscience S) by G. N. Orlovsky, T. G. Deliagina and S. Grillner

About the course

4

Book:

Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology. Front. Neurorobot.

5

What have you learned?



9

Contents

1: Embodied AI: Design Principles of Intelligence (17.10.2020, 8:00-12:00)

2: Locomotion Principles in Animals (24.10.2020, 8:00-12:00)

3: Biomechanics and Neural Mechanisms for Adaptive Locomotion of Robots (31.10.2020, 8:00-12:00)

4: Neural Locomotion Control I (07.11.2020, 8:00-10:00) + CPG implementation on robot simulation for locomotion control (Practice, 10:00-12:00)

5: Neural Locomotion Control II (14.11.2020, 8:00-10:00) + Premotor neuron (VRN) implementation on robot simulation for directional control (Practice, 10:00-12:00)

6: Neural Sensory Preprocessing (21.11.2020, 8:00-10:00) + Neural preprocessing implementation on robot simulation for autonomous obstacle avoidance (Practice, 10:00-12:00)

7: Learning and Adaptation for Adaptive Locomotion I (28.11.2020, 8:00-10:00) + Robot learning implementation on simulation for adaptive behavior (Practice, 10:00-12:00)

8: Learning and Adaptation for Adaptive Locomotion II (05.12.2020, 8:00-10:00) + Robot learning for autonomous behavior generation (Practice, 10:00-12:00)

From Dec 6th onward, project and report

Week1: Embodied AI: Design Principles of Intelligence

→Thinking and body cannot be separated!

→ Complete Embodied Agent• Embodied→ Physical body • Autonomous→Without human control• Self-sufficient→ Sustain itself• Situated→ Sense & learn about the environment

→ Properties of Complete Embodied Agents

• Obey the laws of physics• Generate sensory stimulation• Effect the environment (interaction)• Perform morphological computation• Behave as dynamical systems (behaviors as

attractors!)

• Principle I: The Three-Constituents Principle(Structural design, Desired behaviors, Ecological niche)

• Principle II: The Complete-Agent Principle (Sensors & actuators, neural & morphological computations)

• Principle III: Cheap Design (Exploiting body to simplify control)

• Principle IV: Redundancy(Different sensor modalities)

• Principle V: Sensory-Motor Coordination (Correlation between sensory inputs & motor outputs)

• Principle VI: Ecological Balance (Balance between sensors, motors, materials, control)

Principle VII: Parallel, Loosely Coupled Processes(Distributed control with sensory feedback)

• Principle VIII: Value (Objective function for learning & adaptation)

Embodiment – Theoretical scheme (Pfeifer et al. 2007).

11

Biomechanics &

Sensors (Body)• Structures

• Muscles

• Materials

Neural

mechanisms• Control

• Memory

• Learning

Dickinson et al., 2000

contact

musclelength, force

“Motor Intelligence”

• Joint coordination• Leg coordination

Week2: Locomotion Principles in Animals

12

Artificial systems (Robots)

Biomechanics &


• Actuators / Muscles

• Materials

Neural


• Memory

• Learning



CWRU

Ant-robot

HECTOR

RHEX

LAURON

HECTOR

AMOS

13

Artificial systems

Biomechanics &


• Muscles

• Materials

How many motors do you need to build a hexapod robot?

14

Artificial systems

Biomechanics &


• Muscles

• Materials 1-2 DOFs in total

The Dynamic Autonomous Sprawled Hexapod, DASH (Birkmeyer et al., 2009)

University of California

16

Artificial systems

Biomechanics &


• Muscles

• Materials

Rhex, Saranli et al. 2001, 2004

1 DOF legs & non-segmented body(6 DOFs in total)

Additional DOF for adjustment

Galloway et al., 2011

17

Artificial systems

Biomechanics &


• Muscles

• Materials 1 DOF legs & segmented body(7 DOFs in total)

http://biorobots.case.edu/projects/whegs/whegs-ii/

Schroer et al., 2004

18

Artificial systems

Biomechanics &


• Muscles

• Materials

Robot I, CWRU

2 DOFs legs & non-segmented body(12 DOFs in total)

Fischer et al., 2005

http://www-cdr.stanford.edu/biomimetics/documents/sprawl/

Sprawlita hexapedal robot

19

Artificial systems

Biomechanics &


• Muscles

• Materials 3 DOFs legs & non-segmented body(18 DOFs in total)

LAURON V, KITBILL-Ant-p robot, CWRU

Hector robot, Bielefeld (Schneider et al., 2014)

20

Artificial systems

Biomechanics &


• Muscles

• Materials 3 DOFs legs & segmented body( >18 DOFs in total)

AMOSII robot, SDU&BCCN

Artificial systems

Biomechanics &


• Muscles

• Materials investigate

Billeschou, P., Bijma, N.N., Larsen, L.B., Gorb, S.N., Larsen, J.C., Manoonpong, P.

(2020) Framework for Developing Bio-inspired Morphologies for Walking Robots,Appl.

Sci. 2020, 10(19), 6986; https://doi.org/10.3390/app10196986

https://doi.org/10.3390/app10196986

Artificial systems

Biomechanics &


• Muscles


Billeschou, P., Bijma, N.N., Larsen, L.B., Gorb, S.N., Larsen, J.C., Manoonpong, P.

(2020) Framework for Developing Bio-inspired Morphologies for Walking Robots,Appl.

Sci. 2020, 10(19), 6986; https://doi.org/10.3390/app10196986

https://doi.org/10.3390/app10196986

Artificial systems

Biomechanics &


• Muscles


Artificial systems

Biomechanics &


• Muscles


Artificial systems

Biomechanics &


• Muscles


Artificial systems

Biomechanics &


• Muscles


Artificial systems

Biomechanics &


• Muscles


Artificial systems

Biomechanics &

Sensors (Body)• Structures (summary!)

• Muscles

• Materials 1 DOF legs(6 DOFs in total)

1 DOF legs & segmented body(7 DOFs in total)

2 DOFs legs(12 DOFs in total)

3 DOFs legs (18 DOFs in total)

3 DOFs legs & segmented body( >18 DOFs in total)

Walking, Climbing

Walking, Climbing,Manipulating

(1-2 DOFs in total)

Artificial systems

Biomechanics &

Sensors (Body)• Muscles

•Simple, cheap

•Revolve freely 360 degrees

•Easy to control

•H-bridge

•Using shaft encoders for feedback

•DC motor+gearing+feedbackcontrol loop circuit (position control)

•Expensive

•Position control (angle)

•Pulse Width Modulation (PWM)

•Mostly, 0-90, 0-180 degrees

•Position control

•No feedback used

•Control by impulses (switching

sequence)

•Worse weight/performance

ratio than DC, servo motors

DC motors Servo motors Stepper motors

Actuators (Rotary motion)

Artificial systems

Biomechanics &


Actuators (Linear motion)

•Linear movement•High forces without gears•Controlled by a hydraulic pump (heavy)•Noise & smoke of engine

•Linear movement•Fast response•Simple to control•Pull/push

•Linear movement•High forces•Power from compressed air (heavy)•Fast on/off

•Linear movement•Similar tostandard servo motor•PWM •Position control

Linear servo Pneumatic HydraulicSolenoids

Artificial systems

Biomechanics &


Compliant Actuators

Ham et al., 2009

Artificial systems

Biomechanics &


Compliant Actuators

”But Muscles are not just stiff but also compliant!”

ETH, Zurich

Ham et al., 2009

Artificial systems

Biomechanics &


Compliant Actuators

Artificial systems

Biomechanics &


Compliant Actuators

MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator)

Vanderborght et al., 2009

Artificial systems

Biomechanics &


Compliant Actuators

Antagonistic setup of two SEA

AMASC

Hurst et al., 2004

Artificial systems

Biomechanics &


Compliant Actuators

Antagonistic setup of two SEA

AMASC

Hurst et al., 2004

Migliore et al., 2005

Artificial systems

Biomechanics &


Compliant Actuators

Compact series elastic actuator

Tsagarakis et al., 2009

Artificial systems

Biomechanics &


Pneumatic artificial muscles

•Flexible bladder surrounded by nylon cord•Power from compressed air (heavy)•Fast response•Nonlinearity

Shape memory alloy SMA

•Copper-zinc-alu-nikel, Copper-alu-nickel, Nickel-titanium alloys•Changing shape when energy is applied toOr removed from them•Light weight•Low force output, poor fatigue properties

1968 McKibben

Compliant Actuators

Artificial systems

Biomechanics &


Compliant ActuatorsActuator

Spring

Load

”Hardware”

Artificial systems

Biomechanics &


Compliant ActuatorsActuator

Spring

Load

Actuator

Virtual Spring

LoadStiff Actuators with Software Control

”Software”

”Hardware”

Artificial systems

Biomechanics &


Stiff Actuators with Software Control

Active compliance using force/torque sensing at each joint (force control)

https://www.youtube.com/watch?v=r6mrNnIamKw

Artificial systems

Biomechanics &



Virtual muscle model using force at the foot of a leg (position control)

Standard servomotor

(position control)

without angle feedback

+ = Variable

compliancesMusclemodel

M1M2

Xiong et al., Industrial Robot, 2014

Artificial systems

Biomechanics &




Musclemodel

Virtual Agonist-Antagonist Muscle (i.e., M1 and M2).

It consists of parallel and contractile elements (i.e., PE(1,2) and CE(1,2)).

PE(1,2) for compliance (passive force)

CE(1,2) for actuation (active force)

M1M2


Artificial systems

Biomechanics &




Musclemodel





Passive forceActive forceExternal force

M1M2


Artificial systems

Biomechanics &




Musclemodel






M1M2


Artificial systems

Biomechanics &




Musclemodel






M1M2

“Runge–Kutta

method”


Artificial systems

Biomechanics &




Musclemodel






M1M2

“Runge–Kutta

method”

Tuning K or D for

variable compliance!


Artificial systems

Biomechanics &




0

Force sensor

Two-jointed legs

Artificial systems

Biomechanics &




Softer joints (i.e., D = 0.001, K = 0.8)→ bouncing


Artificial systems

Biomechanics &




Stiffer joints (i.e., D = 0.1, K = 0.8)→difficult to push


Artificial systems

Biomechanics &




Intermediately soft joints (i.e., D = 0.01, K = 0.8)


Artificial systems

Biomechanics &





Artificial systems

Biomechanics &





5.72kg Load


Artificial systems

Biomechanics &

Sensors (Body)• Muscles (summary!)

Spring or damper

Spring or damper

Artificial systems

Biomechanics &

Sensors (Body)• Materials

Steel, Aluminium

Carbon-fiber-reinforced polymer

Rigid

Plastic

57

Artificial systems

Biomechanics &


Steel, Aluminium


Rigid

Plastic

Spring

Tendon

58

Artificial systems

Biomechanics &


Steel, Aluminium


Rigid Soft

Plastic

SpringRubber

SiliconeECOFLEX

Tendon

Pourable Polyurethane

59

Artificial systems

Biomechanics &


Shepherd et al., 2011

Harvard University

60

Artificial systems

Biomechanics &


Shape Deposition Manufacturing (SDM)

Sprawlita hexapedal robot (Cham et al., 2002)

October 2013 61

30o

4X

Incline walking (rough surface)

Reactive posture control (Foot & IMU sensors)

25°. Roennau et al., IEEE IROS, 2014

4X

Control:New walking behavior


30o

Morphological

computation:Structure/material

30o

4X


Morphological



Adhesion

Mechanical

interlocking

Smooth surface

Rough surface

Spenko et al., J. Field Robot., 2008

Kimet al., IEEE Trans. Robot., 2008

Stanford University

30o

4X


Morphological



Mechanical

interlockingRough surface

Spenko et al., J. Field Robot., 2008

FOOT RASP CALLUS dead

skin remover

Manoonpong et al., Scientific Reports-Nature Journal, 2016, In collaboration with Kiel University (Prof. Gorb)

4X



Frictional isotropy

(Uniform spikes)

30o

4X

Frictional anisotropy

(Shark skin, Sloped spikes)



Rough surface

30o

4XManoonpong et al., Scientific Reports-Nature Journal, 2016, In collaboration with Kiel University (Prof. Gorb)


SR = P/mgv

Locomotion efficiency of AMOS

with and without shark skin


Sharkskin shoes


Sharkskin shoesMicro scale

4X

4X17 deg

17 deg

78

Artificial systems

Biomechanics &


Not only soft materials are good but also different surfaces for variousfrictions are required!

79

Artificial systems

Biomechanics &

Sensors (Body)• Materials (summary!)

• Rigid• Soft• Anisotropic friction

80

Artificial systems

Biomechanics &

Sensors (Body)

• Structures →

• Muscles →

• Materials →

Segmented legs & body

Actuation & compliance

Rigid, soft, anisotropic friction

“Virtual muscles”

“Shark skin”

“Rubber&

spring”

“Backbone” “3 Jointed legs ”

AMOSII (19 motors)

81

Artificial systems

Biomechanics &

Sensors (Body)

• Structures →

• Muscles →

• Materials →

Segmented legs & body

Actuation & compliance

Rigid, soft, anisotropic friction

“Virtual muscles”

“Shark skin”

“Rubber&

spring”

“Backbone” “3 Jointed legs ”

AMOSII (19 motors)

October 2013 82

Biomechanics &

Sensors (Body)

Neural

mechanisms

MemoryLearningControl

StructureMusclesMaterials

Biomechanical

development

Bio-inspired

walking robotsNeural mechanisms

(modular structure)

Structure: ?Muscles: ?Material: ?

Artificial systems

Short Summary

Walking animals

(Insects)

Biomechanics &

Sensors (Body)

Neural

mechanisms



Biomechanical

development

Bio-inspired


(modular structure)


Short Summary

Walking animals

(Insects)

Artificial systems

85

Biological systems

Biomechanics &

Sensors (Body)• Sensors

Proprioceptive sensing (sensing internal environment)

Exteroceptive sensing (sening the external environment)

86

Artificial systems

Biomechanics &


•Poteniometers (Pot), shaft encoder (En)

•Determining rotational motion (joint angle)

•Variable resistor (Pot)

•Built in a servo motor (Pot)

•Analog (Pot, En) / Digital (En) output

•Passive sensors

•Accelerometer: measuring the acceleration

•Gyroscope: measuring the rotational change

•Inclenometer: measuring the absolute angle

•Ball switch: measuring the rotational change

•Balancing

•Analog, PWM, digital, output

•Passive sensors

Position sensing (leg joints) Orientation sensing (body)



87

Artificial systems

Biomechanics &




•Stain gauge, force sensing resistors (FSR)

•Determining loading being carried by the

machines, foot contact event

•Variable resistance

•Analog output

•Passive sensors

•Current sensor, voltage sensor

•Determining current, voltage

•Analog output

•Passive sensors

Load sensing (feet) Power management

88

Artificial systems

Biomechanics &




•IR sensor, sonar sensor,

laser scanner

•Measuring distance

•Analog/ digital output

•Active sensors

•Camera, most complex sensors

used

•Image processing, e.g., object

recognition

•Analog/digital output (USB)

•Passive sensor

•Photoresistor or light

dependent resistor

•Detecting light source

•Variable resistance

•Analog output

•Passive sensor

Proximity sensing Vision Light sensing

89

Artificial systems

Biomechanics &




•Microphone

•Sound localization

•Analog output

•Passive sensor

•Direct contact, bumpers,

Artificial skin, antenna

•Measuring distance, tactile

sensing

•Analog/digital output

•Passive sensor

•Microphone + whisker

•Detecting sound, wind

tactile sensing

•Analog output

•Passive sensor

Audition Touch Auditory-tactile-wind sensing

90

Artificial systems

Biomechanics &




Compass (internal, passive)Temperature sensor (internal/external, passive)

Olfaction (external, passive)

GPS (external, active)

Vibration (internal/external, passive)

Biomechanics &

Sensors (Body)

Neural

mechanisms



Biomechanical

development & sensorsBio-inspired


(modular structure)


Short Summary

Walking animals

(Insects)

Artificial systems

Biomechanics &


• Muscles

• Materials

Neural


• Memory

• Learning



Locomotion in Robots

Locomotion control of walking robots

92

The problems of legged locomotion control

Coordinating all the degrees-of-freedom of the robot: finding the right

• Frequency f=1/T• Phase ϕ,

• Amplitude A

• Signal shape

Ijspeert, EPFL Locomotion control

93

The problems of legged locomotion control

•Need to modify the gait for different speeds and directions

•Need to keep balance

•Need to take advantage of the robot’s dynamics

•Obstacle avoidance

• Adapting to perturbations or different terrains

•Optimizing the gaits (finding the fastest, most efficient,...)

• Dealing with lesions, and changes in body properties


94

Minimal ingredients for locomotion control

1. A trajectory planner:

• For producing the different trajectories that each degree of freedom (DOF) has to follow.

• Trajectory = set of joint angles over time

2. A feedback PID-controller:

• For producing the torques (in the motors) necessary to follow a specific trajectory.

Ijspeert, EPFL Locomotion control95

Minimalistic control diagram (block diagram)

(Desired joint angle)

(Actual joint angle)

(Joint angle command)


96

Locomotion control

Forward/inverse kinematics, balance

control, impedance/stiffness control

Semini et al.,

Int J Rob Res., 2015

Boston Dynamics, USA, 2018

IEEE SPECTRUM 2019,

Zhejiang University

Different approaches to legged locomotion control in current robots

I. NEED kinematic

models

Engineering control



Semini et al.,



IEEE SPECTRUM 2019,

Zhejiang University


I. NEED kinematic

models

Engineering control

(Foot tip)

Model-based control (traditional control engineering): Forward/Inverse kinematics

(Foot tip)


d1

a2 a3


BaseEnd effector

End effector

2

1

3

2

1

3

d1

a2a3


C1,2,3 = cos 1,2,3; C23 = cos(2+3); S1,2,3 = sin 1,2,3; S23 = sin(2+3)

Px

Py

Pz

d1

a2 a3

End effector

2

1

3



Px

Py

Pz

q1= 1= atan2(Py, Px);

COSZETA3=((Px*Px)+(Py*Py)+((Pz-d1)*(Pz-d1))-(a3*a3)-(a2*a2))/(2*a2*a3);

q3= 3 = atan2(sqrt(1-COSZETA3*COSZETA3), COSZETA3);

q2= 2 =-atan2((Pz-m_d1), sqrt(Px*Px+Py*Py))-atan2(m_a3*sin(q3),m_a2+m_a3*cos(q3));

d1

a2 a3

End effector

2

1

3



Px

Py

Pz

q1= 1= atan2(Py, Px);

COSZETA3=((Px*Px)+(Py*Py)+((Pz-d1)*(Pz-d1))-(a3*a3)-(a2*a2))/(2*a2*a3);

q3= 3 = atan2(sqrt(1-COSZETA3*COSZETA3), COSZETA3);

q2= 2 =-atan2((Pz-m_d1), sqrt(Px*Px+Py*Py))-atan2(m_a3*sin(q3),m_a2+m_a3*cos(q3));

Inputs (Px, Py, Pz)

d1

a2 a3

End effector

2

1

3

Trajectory generation (Cartesian space)

R3

R2

R1

L3

L2

L1

”Tripod gait”

Px = 0.0

x

Y

Z

105


R3

R2

R1

L3

L2

L1

”Tripod gait”

Px = 0.0

x

Y

Z

Manual design!!!106


R3

R2

R1

L3

L2

L1

”Tripod gait”

Px = 0.0

x

Y

Z

Recoding animal walking!!!107

Stride length -> 10 cm.

Pz

Py

Px = 0.0


Inverse kinematics

ST SW ST

2

1

3

1

2

3

108

109



Prior knowledge map & offline trial-

and-error learning with approx.

40 million iterations

Offline reinforcement learning

with approx. 2600 iterations

Semini et al.,


Hwangbo et al., Science Robotics, 2019

ETH, Switzerland


IEEE SPECTRUM 2019,

Zhejiang University


Machine learning

Cully et al., Nature, 2015

I. NEED kinematic

modelsII. NEED long learning time

(mins to hours/days in Sim)

Engineering control

Evolutionary algorithm

Approx. 250,000 internal modelsimulations

Evolving robot control for locomotion

Bongard, J., Zykov, V., Lipson, H. (2006). Resilient machines through continuous self-modeling. Science, 314: 1118-1121.









Semini et al.,



ETH, Switzerland


IEEE SPECTRUM 2019,

Zhejiang University


Machine learning


I. NEED kinematic



Engineering control

Evolutionary algorithm









Semini et al.,



ETH, Switzerland


IEEE SPECTRUM 2019,

Zhejiang University


Machine learning


I. NEED kinematic



Engineering control








Semini et al.,



ETH, Switzerland


IEEE SPECTRUM 2019,

Zhejiang University


Machine learning


I. NEED kinematic



Engineering control

CPGs-based control with

predefined coordination

Bio-inspired control

Karakasiliotis et al., J. R. Soc. Interface, 2016

EPFL, Switzerland

Schilling et al., Biol Cybern., 2013 (EU project)

Reflex-based control (Purely

sensor driven with predefined

coordination

III. CPG, Reflex,

Neural Control …






EPFL, Switzerland




coordination

III. CPG, Reflex,

Neural Control …

Central Pattern Generators (CPGs)

Ryczko D., Simon A., Ijspeert AJ. (2020) Walking with Salamanders: From Molecules to Biorobotics, Trends in Neurosciences






EPFL, Switzerland




coordination

III. CPG, Reflex,

Neural Control …

Neural mechanisms underlying locomotion




Biological neural networksArtificial neural networks


Biological neural networksArtificial neural networks

“From Number

To Behavior !”1110001010101

00100010011

110001111

0110010

11100

00010

1101

11

Biomechanical


walking robots


Summary

Biomechanical


walking robotsLocomotion control


Summary

Machine learning

Engineering control


Biomechanical


walking robotsLocomotion control


Summary

Machine learning

Engineering control


Next week

NEXT WEEK: Neural Locomotion Control ISe

nso

rs

Mo

tors

Memory guidance

Goal-directed navigation

Neural learning (Online)

Neural locomotion control

Learning & Adaptation

Control

• Central Pattern Generator (CPG)

• Frequency adaptation

• Robot simulation !!! Please prepare your notebook and install the robot simulation software before next time!!, classroom at NUAA Ming Palace Campus

Reading Materials of Today!

1) Rolf Pfeifer, Max Lungarella, Fumiya Iida (2012) The challenges ahead for bio-inspired 'soft' robotics, Communications of the ACM, Volume 55 Issue 11, November 2012 Pages 76-87

2) Ham, R.; Sugar, T.G. ; Vanderborght, B. ; Hollander, K.W. ; Lefeber, D. (2009) Compliant actuator designs, IEEE Robotics & Automation Magazine, pp. 81-94

3) Sangbae Kim, Cecilia Laschi, Barry Trimmer(2013) Soft robotics: a bioinspired evolution in robotics, Trends in Biotechnology, Volume 31, Issue 5, May 2013, Pages 287–294

4) Xiaodong Zhou and Shusheng Bi (2012) A survey of bio-inspired compliant legged robot designs, Bioinspir. Biomim. 7, 041001 (20pp)

5) Xiong, X.; Wörgötter, F.; Manoonpong, P.(2014) Virtual Agonist-antagonist Mechanisms Produce Biological Muscle-like Functions: An Application for Robot Joint Control. Industrial Robot: An International Journal, Vol. 41 Iss: 4

147

Exercises & project work

1) Robot simulation (C++, gorobots_edu)

1. Assuming you have a clean install of Ubuntu 18!!https://releases.ubuntu.com/bionic/

2. Please follow this link for installing the simulationhttp://manoonpong.com/MOROCO/lpz_guide.txt

https://releases.ubuntu.com/bionic/

http://manoonpong.com/MOROCO/lpz_guide.txt

Appendix: Muscle model

Passive force

Passive force

Active force

Active force

External forcespring damper

spring damper


spring damperNeural activation

Neural activation

M1M2

Date post:	01-Oct-2021
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CH3: Biomechanics and Neural Mechanisms of Robots

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