Introduction to Robots and Multi-Robot Systems

Agents in Physical and Virtual Environments

Intro to AI, lesson 10

Based on slides by Gal A. Kaminkaand by Robin Murphy

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Some examples of robots

www.irobot.com

courtesy of MIT AI Lab

courtesy of Honda

www.sony.com

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Less Famous Cousins at WTC

Inuktun microTracks

½ iRobot PackBot

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Give me a few examples.

Is a rock a robot?

What is a robot?

Robot

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What is a robot?

A toy spring car can move and act.

a robot can sense.

Actuators(Effectors)

Robot

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What is a robot?

A sorting algorithm senses and acts.

a robot is persistent.

Actuators(Effectors)

Robot

Sensors

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What about a remote alarm?

a robot is situated in an environment.

What is a robot?

Actuators(Effectors)

Sensors

Robot

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We’re missing something here.

a robot is responsive.

What is a robot?

Actuators(Effectors)

Sensors

Environment

Robot

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We’re missing something here.

a robot is responsive.

What is a robot?

Actuators(Effectors)

Sensors

Environment

Robot

Process

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Robots: Are persistent with respect to their environment Sense and act Sense/act within the same environment (situated) Respond to senses using action

Here’s what we have so far

Environment Robot

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Here’s what we have so far

Robots: Are persistent with respect to their environment Sense and act Sense/act within the same environment (situated) Respond to senses using action

These characteristic are true for agents, not just robots

Environment Robot

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Another definition

Mechanical creature which can function autonomously Capek 1921: R.U.R

Mechanical= built, constructed

Creature= think of it as an entity with its own motivation, decision making processes

Function autonomously= can sense, act, maybe even reason; doesn’t just do the same thing over and over like automation

Physically situated, but now software agents or bots

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Why investigate robots? (1)

Because we want to understand how to build them.

So that they do things for us.

So that we can do other things instead.

In other words,

We are studying robotics because we are lazy.

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Dirty, Dangerous, and Dull Tasks

Better Than Bio Robots at WTC… voids smaller than person could enter voids on fire or oxygen depleted

Principles from robotics influenced AI community Combines programming, networks, operating systems,

algorithms, … everything about CS into a system (the ultimate software engineering project)

Why investigate robots? (2)

Void:1’x2.5’x60’

Void on fire

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Minimally Invasive SurgerySpinal Fusion with Mazor’s SpineAssist

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Mazor’s SpineAssistSurgical Robot

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The Agent/Environment/Task Framework

We want the robot to do tasks for us (or for itself) Therefore, it must take a task into account

Environment Robot

Task

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Agents are embodied Part of the environment is their own body

Sensing and acting with uncertainty Slippery grips, sensing is inaccurate

Environment is dynamic, changes even without robot ….

We will talk more about environments later, but first….

Problems with physical environments

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A Taxonomy of Environments

There are a number of characteristic dimensions: Dynamic vs. static Accessible vs. inaccessible

transparent vs. translucent Deterministic vs. non-deterministic Discrete vs. continuous …..

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Dynamic vs. Static Dynamic:

Environment changes even if agent takes no action Static:

Environment does not change until agent takes action Key question:

Is the agent only cause of change in the environment?

Physical environment is dynamic Wind, other agents, continuous mechanical forces

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Accessible vs. Inaccessible

Accessible (transparent): Agent can sense everything and anything. Nothing is hidden.

Inaccessible (translucent): Agent can only sense part of the environment. Some features of the environment are hidden.

Key question:

What can the agent sense about the environment?

Physical environments typically inaccessible: Cannot see behind you, nor over long distances, nor inside people.

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Determinism

Deterministic: An action results in a completely predictable change

Non-deterministic: An action can result in one of a range of possible changes Uncertainty in the result

Key question:

If agent takes action, is it sure of the outcome?

Physical environment is non-deterministic: Slippery grasp, coin-flips, gambling

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Discrete or continuous?

Discrete: Actions or senses are clearly separated, limited number

Continuous: Infinite possible values within a range

Note: Different from discrete/continuous senses and actions

Physical environments are continuous

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A Taxonomy of Environments

There are a number of characteristic dimensions: Dynamic vs. static Accessible vs. non-accessible

transparent vs. translucent Deterministic vs. non-deterministic Discrete vs. continuous

Open question: Quantifying the above

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The Agent/Environment/Task Framework

Given environment and task,

how do we build a robot that carries out the task?

Environment Robot

Task

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Agents and Environments

Many different environments can exist Different techniques are used with different environments We focus on techniques used in physical environments

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Agent Control

In principle, our view is of an agent with three components: Effectors/actuators Sensors Think

This view is sometimes referred to as sense-think-act cycle But this can be misleading: not necessarily so sequential

Sense

Think

ActRobot

Environment

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Three components, three challenges*

The action selection problem: Given task/goals, how to select the next action(s)

The sensor planning problem: Given task/goals, how to use sensors

The pose planning problem: Given needed target body position, how to get there

Sense

Think

ActRobot

Environment

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Three components, three challenges*

The sensor planning problem: Which sensors to use? When? How to integrate their information (sensor fusion)? How to overcome uncertainty in their readings?

May depend on what think is thinking, and may need to influence what action to take

Sense

Think

ActRobot

Environment

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Three components, three challenges*

The pose planning problem: Which (combination of) actuators to use to achieve pose? What trajectory should they take? How to compensate for actuation uncertainty?

May depend on what think is thinking, and may need to depend what sense reads, and needs

Sense

Think

ActRobot

Environment

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Three components, three challenges*

The action-selection problem How to select action in real-time? How to select action that is good for task/goal? How to integrate competing needs of different subtasks?

Depends on the capabilities of sense and act

Sense

Think

ActRobot

Environment

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Three challenges

These three challenges are highly coupled Not easy to separate them out.

Many systems/techniques provide integrated solutions Multiple levels at which can be addressed:

hardware, control, software, … Example: better vision by blurring camera Example: using probabilistic inference to handle uncertainty Example: sensor placement affects foraging behavior

Robotics is a highly inter-disciplinary field.

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Empirical research

As you can see, these are complex concepts Many of problems/solutions affect each other in very subtle ways Physical environments very uncertain, unpredictable

Difficult to predict system behavior from analysis Cannot just browse at the algorithms and hardware involved

Use empirical research methods in investigations

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Empirical research

Experiment design issues: Study system with and without proposed techniques Compare performance of many systems Compare performance across different environments or tasks

Faces generality problems in drawing conclusions Tied to the actual challenges of the real world:

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Simulations

Significance issues: Run many experiments, draw statistical conclusions

Simulation is very useful here Many roboticists frown at simulations

Simulation and virtual environment are not same thing

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Science and Scientists

Scruffies and Neaties

The revolution of 86: Plans are not enough!

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The Sense-Think-Act Cycle:What's in Think (for scruffies) in late 80's?

No need to Think: If sensors read X, then do Y Reactive Camp (Brooks 1986, Schoppers 1987)

Limited thinking: Behavior-based control Behaviors may have state, memory, procedures Arkin, Firby (1986), Maes, ...

Deep thinking: integrated planning, monitoring e.g., IPEM (1988)

Hybrid architectures (e.g., Gat 1992)

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The Sense-Think-Act Cycle:What's in Think (for neaties) in late 80's?

"The Old View" Plans as sequences of actions for execution

Plans as mental attitudes (Pollack 1992) Plans as recipes: Some get executed, some just known

BDI: Belief-Desire-Intention (approximately): Belief: What the agent knows Desire: What the agents ideally wants to see happening Intention: What the agents actually acts towards

Commitments

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An Historical Perspective on Teamwork:From a Single Agent to Multiple Agents

Time Scruffiness Neatness

IntegratingPlanning, Execution,

Monitoring,Re-Planning, Architectures

Reactive-Plans,Architectures

Behavior-Based Architectures

Mental Attitudes,Belief, Desire, Intention (BDI)

Plans as Attitude

'86

'90

'96

Subjective

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Agent Teams Are Everywhere:Teamwork is Important

Nature Formations, flocking, pack hunting, software development

Robotic nature imitations, explorations, soccer Internet, Intranets

Routing, distributed applications, groupware Workflow, cooperating information agents

Virtual environments for training, simulations Human-computer interactions

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Task-Specific Teamwork: Foraging Coverage Mapping Coordinated movement Patrolling Human supervision ….

Main research problems

General Teamwork: Architecture

Flexible teamwork Allocation

Learning, adaptation Monitoring Fault diagnosis Maintenance ….