Bio-inspired control and sensors

5/7/2019

THE STONE AGES

WHAT ARE CONTROL SYSTEMS ?

N. J. Cowan, M. M. Ankarali, J. P. Dyhr, M. S. Madhav, E. Roth, S. Sefati, S. Sponberg, S. a. Stamper, E. S. Fortune, and T. L. Daniel, “Feedback control as a framework for understanding tradeoffs in biology,” Integr. Comp. Biol., vol. 54, pp. 223–237, 2014.

WHAT ARE CONTROL SYSTEMS ?

CONTROLALOGRITHM

SENSORS

BODYDYNAMICS

INPUT OUTPUT

+

-

MOTIVATION

Robotic control could be better

Better control would mean better robots

MOTIVATION

Study animals to develop better controllers

MOTIVATION

• How are animals achieving control?

• How are robots controlled?

(These questions are very broad)

BIOLOGICAL CONTROL HARDWARE

[Wikipedia]

[Santiago Ramón y Cajal (from wiki)]

Purkinje cell (Cerebellum)

Interneuron (Spine Gray Matter)

[Pearson]

Sensory Neuron

[Wikipedia]

BIOLOGICAL CONTROL HARDWARE

Biological sensory systems are slow

Mass (kg)

LOW LEVEL CONTROL: BIOLOGY

• Nerves have non-negligible transmission delays

• Speed:• 0.6 m/s (pain signals)• 100 m/s (large diameter motor axons)

• Not even counting synapse delay

Nerves are slow!3 million times slower than wire

5.8 ms for neural impulse to travel from spine to hand muscles

0.5 ms required for accuracy [Hore, Watts & Tweed ‘96]

Nerve impulse at spine

FEEDFORWARD CONTROL

Activation onsetbefore impact

[Organization of Movement, Krakauer & Ghez 2000]

HIERARCHAL CONTROL

• Some control needs to happen fast

• Some control needs lots of information

• You can’t really have both ☹

• Solution: Hierarchal Controllers

E. Todorov, W. Li, and X. Pan, “From task parameters to motor synergies: A hierarchical framework for approximately optimal control of redundant manipulators,” J. Robot. Syst., vol. 22, no. 11, pp. 691–710, 2005.

HIERARCHAL CONTROLROBOT ANIMAL

HIGH

MID

LOW

Force/Position

Pose target

GRASP TASK

DYNAMIC PLANNER

MOTOR CONTROLLER

MOTOR CONTROLLER …

????

???

GRASP TASK

CEREBELLUM(probably)

MOTOR POOLS

1000 Hz

100 Hz

25 Hz

HIERARCHAL CONTROL: BACKGROUND

• Two main approaches to control hierarchies:

• Centralized: simple, easy to modify, bandwidth limited

• Decentralized: offloads computation, can reduce delay

HIERARCHAL CONTROL MIT CHEETAH

Image courtesy of “High speed trot-running: Implementation of a hierarchical controller using proprioceptive impedance control on the MIT Cheetah” by Hyun et al.

• The MIT Cheetah uses a gait-pattern modulator to develop patterns for the leg trajectories, similar to a CPG

• The lower level controller is an impedance controller

• Many robots use a roughly similar structure but with different implementations details, e.g. trajectory arrays, set points, optimal control, PD control.

LOW-LEVEL CONTROL: MUSCLE STRETCH REFLEX• Involuntary muscle contraction to a

sudden stretch sensed by muscle spindles

• This is basically a load dependent response to input

• The purpose of this is mostly for postures and movements that are robust to disturbances

• Both excites and inhibits opposing muscle groups to incite motion

• Both this muscle reflex and the voluntary muscle movements controlled by the brain are responsible controlling motions

[Human Physiology: An integrated Approach, Silverthorn 2012]

LOW-LEVEL CONTROL: MUSCLE FLEXION REFLEX• Involuntary muscle

contraction due to a painful stimulus, such as stepping on a lego

• This muscle reflex is mostly for protection

• These reflexes can trigger reactions on the other limb to support the increased weight

• Reactions are quicker than it takes for the pain signal to reach the brain

[KOREAN MEDICAL LIBRARY ENGINE (HTTP://WWW.KMLE.CO.KR)]

LOW-LEVEL CONTROL: REFLEXES IN THE COCKROACH

• Cockroaches have a turn response for wind or sound stimulus• They tend to jump and turn away from the response, preparing to run• This behavior is still observed when the head is removed

[Hill 1989]

LOW-LEVEL CONTROL: DO ROBOTS HAVE REFLEXES?• Sort of

• Reflexes are mostly needed for speed and protection

• Robots typically don’t need to go faster, but could use a decentralized control architecture if they do

• An interrupt based protection scheme can be used here

LOW-LEVEL CONTROL: HyQ FOOT “REFLEX”

• It modifies its trajectory if it senses an obstacle

• Not really the same structure as an animal reflex

[Focci et al. CLAWAR 2013]

IMPEDANCE CONTROL: BACKGROUND

• Key idea: instead of focusing on perfectly or optimally tracking some trajectory, we can instead focus on HOW we interact with the environment

• The impedance control paradigm focuses on specifying the relationship between a flow input (position or velocity) and an effort (force or torque)

• This paradigm is useful for understanding both animal and robotic manipulation tasks

IMPEDANCE CONTROL IN HUMANS

• Hogan developed impedance control from a motivation for how humans interact with their environment

• Idea is that muscle stiffness is modulated to vary impedance

• Muscle stiffness is varied primarily through muscle co-contraction, posture selection, and stretch reflexes

• This approach generally assumes that the brain selects an impedance for the lower level controls to assume for a given task.

Image courtesy of “Impedance Control: An Approach to Manipulation” by Neville Hogan

IMPEDANCE CONTROL IN ROBOTS

• The MIT cheetah implements an impedance control algorithm for it’s legs

• The apparent impedance to the environment is varied depending on the task

• Impedance control fits into our distributed control architecture

• Downside: you have to choose an impedance. This is not always straight forward!

[Hyun et al. IJRR 2014]

IMPEDANCE CONTROL IN ROBOTS

• The MIT cheetah’s desired impedance is a coupled linear and radial spring-damper system

• The MIT cheetah receives leg trajectories from the higher level controller and

Images courtesy of “High speed trot-running: Implementation of a hierarchical controller using proprioceptive impedance control on the MIT Cheetah” by Hyun et al.

RYTHMIC CONTROL

Name as many rhythmic behaviors as you can. ! Is the behavior purposeful or involuntary?

! What enforces the periodicity? (Sensory feedback? Environmental interaction? Something else?)

! Alternatively, what causes the periodicity to break down?

RYTHMIC CONTROL

• Link behavior to a steady rhythm

• Be able to alter that rhythm if conditions vary

!Welcome to central pattern generators (CPGs)!

http://media.giphy.com/media/52LkAuJYMzjt6/giphy.gif

http://images.viralnova.com/000/074/910/blue-ribbon-eel.gif

https://media3.giphy.com/media/O1Mfoj3I5eAo0/200.gif

http://stream1.gifsoup.com/view1/1758417/chewing-cow-o.gif

http://stream1.gifsoup.com/view3/1979255/breathing-o.gif http://upload.wikimedia.org/wikipedia/

commons/6/6b/Elephant_walking.gifhttp://i.imgur.com/CAZcD03.gif

RYTHMIC CONTROL: CPG REGULATED BEHAVIORS

• Consist of distributed neural networks of multiple coupled oscillators

• Produce rhythmic behavior without the aid of:• Higher-level

sensory feedback• Higher-level control

[5] Rossignol et al, 2006

RYTHMIC CONTROL: CPG REGULATED BEHAVIORS

IB 222

RYTHMIC CONTROL: NEURONAL RYTHMICITY

Two (possibly) competing main ideas:• Pacemaker neurons:

individual oscillatory neurons

• Network pacemaker: synaptic connections in neural network generate rhythm• Half-center model: two

neurons reciprocally inhibit each other

5/11/2015

RYTHMIC CONTROL: CPGS SHAPED BY SENSORY FEEDBACK

• CPG provides rhythmic base for behavior

• Feedback keeps CPG and body in sync by:• Modulating phase &

frequency based on environmental changes

• Allowing locomotion mode to change as required/desired

http://birg2.epfl.ch/movies/SIMS/sal_s2w.htm (Ijspeert2007)

RYTHMIC CONTROL: BENEFITS OF CPGS

• Reduced time delay: signals only loop through spinal cord

• Smaller control signal dimensionality: CPG control is simpler than muscle control

• Reduced bandwidth required between higher control centers & CPG

RYTHMIC CONTROL: NEURAL BIOLOGICAL CPG MODELS

Three primary modelling paths:

• Connectionist: Use simple neuron models to explore rhythmogenesis

• Coupled oscillator: Explore impact of coupling type on dynamics of multiple oscillators

• Neuromechanical: Include model of body, allowing study of sensory feedback

[2] Ijspeert et al, 2007

RYTHMIC CONTROL: CPGS IN ROBOTICS

Do CPGs impart the same benefits to robotic control as they do to biological control?

!Generally yes, as well as some additional robotics-specific opportunities

[3] Ijspeert and Crespi, 2007

RYTHMIC CONTROL: ATTRACTIVENESS OF CPGS IN ROBOTICS

• Limit cycle behavior is very stable• Distributed implementation is good for modular robotics• Reduced number of control parameters• Easy sensory feedback integration• Ideal for exploring learning/optimization

[1] Ijspeert 2008http://biorobotics.ri.cmu.edu/projects/modsnake/gaits.html

RYTHMIC CONTROL: COMPONENTS OF CPGS

1. General architecture: # of oscillators/neurons, choice of position or torque control

2. Coupling type & topology

3. Waveforms: can be altered with filters

4. Effect of input signals

5. Effect of feedback

CPG exampleKuramoto oscillator model

d✓idt

= !i +K

N

NX

j=1

sin (✓j � ✓i) , i = 1 . . . N

CPG exampleModified Kuramoto oscillator for a legged system

✓̇i = 2⇡fi +6X

j=1

Kaij sin (✓j � ✓i � �ij)

Limb frequency

Limb coupling strength

Limb phase offset

RYTHMIC CONTROL: MULTILEGGED ROBOTS

WITHOUT FEEDBACK WITH FEEDBACK

[Tarapore2014]

[4] Kimura et al, 2007

RYTHMIC CONTROL: QUADRUPEDAL ROBOTS

RYTHMIC CONTROL: BIPEDAL ROBOTS

[https://sites.google.com/site/robodreamer/projects]

IB 222[3] Ijspeert and Crespi, 2007

RYTHMIC CONTROL: EEL ROBOTS

5/11/2015

[2] Ijspeert et al, 2007

RYTHMIC CONTROL: SALAMANDER ROBOTS

5/11/2015

IB 222[2] Ijspeert et al, 2007

RYTHMIC CONTROL: MORE SALAMANDER ROBOTS

5/11/2015