程序代写代做代考 flex Why listen to me?

Why listen to me?

CMP3103M/CMP9050M– Lecture 5

Robot Behaviour

Dr. Mike Mangan

Course Information: Delivery Team

Office: 2220 Isaac Newton Building

Surgery hours: Tues 9-11am

Contact: mmangan@lincoln.ac.uk

Course Information: Syllabus

Date Title Lecturer

23/01/2017 Intro Marc Hanheide

30/01/2017 Robot Programming Marc Hanheide

06/02/2017 Robot Sensing Marc Hanheide

13/02/2017 Motion & Control Marc Hanheide

20/02/2017 Robot Behaviour Mike Mangan

27/02/2017 Navigation Mike Mangan

06/03/2017 Navigation Mike Mangan

13/03/2017 Robot mapping – SLAM Mike Mangan

20/03/2017 Control Architectures Paul Baxter

27/03/2017 HRI 1 Paul Baxter

03/04/2017 HRI 2 Paul Baxter

24/04/2017 Applications Paul Baxter

01/05/2017 Revision / Robot Systems Marc Hanheide

This week…

1. Robot behaviours

2. Braitenberg’s vehicles

3. Combining robot behaviours

4. Learning robot behaviours

What do we mean by behaviour?

Definition
noun
The observable response or reaction of an organism, an individual, or a system to a situation

Supplement
Behaviour pertains to the (aggregate of) acts or reactions that an organism, an individual or a system
produces in response to a particular circumstance. It may be induced by stimuli or inputs from the
environment whether internal or external, conscious or subconscious, overt or covert, and voluntary or
involuntary.1

It may be innate. It means it may be based on the instinct or natural action (or inaction), originating
from inside an organism or a cell. Or, it may be acquired (or learned) based on previous experiences or
exposure.

http://www.biology-online.org

http://www.biology-online.org/dictionary/Organism

What do we mean by robot behaviour?

Robot Behaviour:
• [from psychology] reaction to a stimulus
• Basic building block for robot actions

Behaviour

stimulus

response

Robot behaviours – line-following

behaviour

stimulus

response

Application Areas?

Robot behaviours – exploration/search

behaviour

stimulus

response

Application Areas?

Common robot behaviours

Example Category

road following path following

wandering exploration/directional

goal following goal-oriented appetitive

obstacle avoidance aversive/protective

balance postural behaviours

flocking social/cooperative

visual search perceptual

Braitenberg’s Vehicles

Building seemingly complex behaviours from simple
interactions.

Valentino Braitenberg, “Vehicles: Experiments in Synthetic
Psychology”, MIT Press, 1984.

This lecture covers Vehicles 1 to 3
(but the whole book is highly recommended…)

Vehicle 1 – Getting Around

Input
(sensor)

Output
(motor)

Vehicle 1 – Getting Around

Input
(sensor)

Output
(motor)

Vehicle 1 – Getting Around

Input
(sensor)

Output
(motor)

Transfer function

Vehicle 1 – Getting Around

Under perfect conditions the vehicle stops on dark spots

But if more realistic physics are added e.g. non-symmetric
drive or friction interesting things emerge such as:

• looping continuously around sources
• Brownian motion on the group level

http://stevebattle.github.io/braitenberg/vehicle1.html

Vehicle 2 – What are their behaviours?

+ ++ +

Vehicle 2 – What are their behaviours?

Fearful

Vehicle 2 – What are their behaviours?

Fearful Aggressive

+ ++ +

Vehicle 2 – What are their behaviours?

– — –

Input
(sensor)

Output
(motor)

Inhibitory connections

New transfer function

Vehicle 3 – Love

Permanent
love

Exploring
Love

Multisensory Version

• Increasingly complex behaviours can be created by
adding:

• New sensors with changing profiles
• (non-linear / digital)

• New Transfer functions
• (Non-linear / weighting)

• Varying excitatory and inhibitory connectivity

• E.g. goal seeking vs obstacle avoidance

Real World Example – Cricket Phonotaxis

Work of Prof Barbara Webb, University of Edinburgh

Female has to locate male of her
species (i.e. ignore other species)

https://www.youtube.com/watch?v=aMeKvWU1CnQ (16:55)

Robot behaviours – recap

• Robot Behaviour:
• [from psychology] reaction to a stimulus
• Basic building block for robot actions

• Often reactive behaviours:
• no internal data interpretation
• fast reactions!

• Reactive behaviours perform closed loop control
• Remember lecture 4

• Different behaviours can be combined into complex
ones

Behaviour

stimulus

response

Questions?

This week…

1. Robot behaviours

2. Braitenberg’s vehicles

3. Combining robot behaviours

4. Learning robot behaviours

Why behaviour-based robots?

http://www.mind.ilstu.edu/curriculum/medical_robotics/behavior_pioneers.php

Complexity emerges through time

• Each robot programmed with
simple rules:

Drive

upon_impact

if (have_frisbee)

drop_Frisbee

else

grab_Frisbee

end

Complexity emerges through time

Multiple Behaviours

behaviour 1stimulus 1

behaviour 2stimulus 2

behaviour 3stimulus 3

behaviour 4stimulus 4

behaviour 5stimulus 5
co

o
rd

in
a

to
r

action

p
e

rc
e

p
ti

o
n

• Concurrent behaviours – How to coordinate behaviours at the same time?
• Sequencing – Not covered in this lecture

Subsumption

Organises behaviours and sub-behaviours into a hierarchy. Higher levels inhibit
(subsume) the actions lower levels.

Subsumption

Organises behaviours and sub-behaviours into a hierarchy. Higher levels inhibit
(subsume) the actions lower levels.

Subsumption

Organises behaviours and sub-behaviours into a hierarchy. Higher levels inhibit
(subsume) the actions lower levels.

https://www.youtube.com/watch?v=gN3tMnB22Uc

Subsumption

Key concepts:

Situatedness: a robot should be able to react to its environment within a human-like
time-frame.

Embodiment: “The real world is its own best model”. “Every simulation is doomed to
succeed”.

Intelligence: Brooks argues that developing perceptual and mobility skills are a
necessary foundation for human-like intelligence.

Emergence: Conventionally, individual modules are not considered intelligent by
themselves. It is the interaction of such modules, evaluated by observing the agent
and its environment, that is usually deemed intelligent (or not).

• “Machina Speculatrix”, 1948

– experiments in reflex behaviour

– built of electronic valves and photo-cells

– approach or escape a light source

– https://www.youtube.com/watch?v=lLULRlmXkKo

Complexity emerges through interaction

Notes on Behaviour-based robotics

Emergence offers fantastic opportunities but is hard to design.

Long term planning needs newer methods e.g. Q learning

Learning Robot Behaviours

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

McCulloch & Pitt neuron (1943)

𝑦 = 0 𝑖𝑓 𝑠𝑢𝑚 < 𝑇 𝑦 = 1 𝑖𝑓 𝑠𝑢𝑚 > 𝑇

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

Left Whisker Right Whisker Left Motor Right Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

Task: Obstacle Avoidance Using Whiskers

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

W_RWRMW_LWLM

We have no idea what the weights or threshold should be so
we initially set them to neutral values:

ꙍ = 0 (all weights set to zero)
Θ = -0.01 (threshold for motors activating)
µ = 0.3 (learning rate)

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

We have no idea what the weights or threshold should be so
we initially set them to neutral values:

ꙍ = 0 (all weights set to zero)
Θ = -0.01 (threshold for motors activating)
µ = 0.3 (learning rate)

Use the following learning rule to optimise weights to get
desired behaviour:

𝜔𝑘 𝑡 + 1 = 𝜔𝑘+ 𝜇 𝑡 𝜏𝑘 − 𝑜𝑘 𝑖𝑘

New
weight

Old
weight

Learning rate input

Target
output

Obtained
output

-0.01 -0.01

Neural Network learning of behaviours

LW RW

LM RM

Initial set-up.
All weights = 0
µ = 0.3
Θ = -0.01

𝜔𝐿𝑊𝐿𝑀= 0 + 0.3 ∗ 1 − 1 0=0

Left
Whisker

Right
Whisker

Left
Motor

Right
Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

𝜔𝑘 𝑡 + 1 = 𝜔𝑘 + 𝜇 𝑡 𝜏𝑘 − 𝑜𝑘 𝑖𝑘

-0.01 -0.01

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

Initial set-up.
All weights = 0
µ = 0.3
Θ = -0.01

𝜔𝐿𝑊𝐿𝑀= 0 + 0.3 ∗ 1 − 1 0=0
𝜔𝐿𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 0=0
𝜔𝑅𝑊𝐿𝑀= 0 + 0.3 ∗ (1 − 1)0=0
𝜔𝑅𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 0=0

Left
Whisker

Right
Whisker

Left
Motor

Right
Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

𝜔𝑘 𝑡 + 1 = 𝜔𝑘 + 𝜇 𝑡 𝜏𝑘 − 𝑜𝑘 𝑖𝑘

Now its your turn.

-0.01 -0.01

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

Initial set-up.
All weights = 0
µ = 0.3
Θ = -0.01

𝜔𝐿𝑊𝐿𝑀= 0 + 0.3 ∗ −1 − 1 0=0
𝜔𝐿𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 0=0
𝜔𝑅𝑊𝐿𝑀= 0 + 0.3 ∗ −1 − 1 1=-0.6
𝜔𝑅𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 1=0

Left
Whisker

Right
Whisker

Left
Motor

Right
Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

Initial set-up.
All weights = 0
µ = 0.3
Θ = -0.01

𝜔𝐿𝑊𝐿𝑀= 0 + 0.3 ∗ −1 − 1 1=0
𝜔𝐿𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 1=-0.6
𝜔𝑅𝑊𝐿𝑀= −0.6 + 0.3 ∗ −1 − 1 0=-0.6
𝜔𝑅𝑊𝑅𝑀= 0 + 0.3 ∗ 1 − 1 0=0

Left
Whisker

Right
Whisker

Left
Motor

Right
Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

-0.01 -0.01

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

Left
Whisker

Right
Whisker

Left
Motor

Right
Motor

0 0 1 1

0 1 -1 1

1 0 1 -1

1 1 X X

-0.01 -0.01

Does it work?

Neural Network learning of behaviours

See Mobile Robotics: A Practical Introduction (p56)

LW RW

LM RM

– –

-0.01 -0.01

Robot Behaviour Learning

Learned behaviours included:
• Obstacle avoidance
• Wall following
• Box pushing

Robot Behaviour Learning

Summary

1. Robot behaviours

2. Braitenberg’s vehicles

3. Combining robot behaviours

4. Learning robot behaviours

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