Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments Lecture...

Introduction to Robots and Multi-Robot Systems

Agents in Physical and Virtual Environments

Lecture 1:Basic Concepts

Gal A. [email protected]

2 Multi-Robot Systems © 2002- Gal Kaminka

Some examples of robots


Give me a few examples.

Is a rock a robot?

What is a robot?

Robot


What is a robot?

A toy spring car can move and act.

a robot can sense.

Actuators(Effectors)

Robot


What is a robot?

A sorting algorithm senses and acts.

a robot is persistent.


Robot

Sensors


What about a remote alarm?

a robot is situated in an environment.

What is a robot?


Sensors

Robot


We’re missing something here.

a robot is responsive.

What is a robot?


Sensors

Environment

Robot


We’re missing something here.

a robot is responsive.

What is a robot?


Sensors

Environment

Robot

Process


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


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


Why investigate robots?

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.


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


In this course we focus on physical environments

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….


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 …..


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


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.


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


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


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


The Agent/Environment/Task Framework

Given environment and task,

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

Environment Robot

Task


Agents and Environments

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


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


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



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



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



The action-selection problem (our focus): 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


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.


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


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:


Simulations

Significance issues: Run many experiments, draw statistical conclusions

Simulation is very useful here Many roboticists frown at simulations (I was called “a theoretician”)

Simulation and virtual environment are not same thing


Science and Scientists

Scruffies and Neaties

The revolution of 86: Plans are not enough!


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)


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


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


?שאלות


Readings

Related readings: Empirical methods in artificial intelligence (book by Paul Cohen) AI Magazine articles by same Toby Tyrell: the action selection problem

Readings for next week: On the web page


Homework

Give 3 examples of environments that are different from the real world along at least one dimension

Characterize the “information environment” existing on the web, where agents may discover, exchange, and manipulate information.

Can non-determinism occur when an environment is static? Give an example

Extra credit: Propose a way of measuring environments quantitatively along the

4 dimensions discussed in class. Discuss: Is a human a robot? Is a cockroach? Is a cell? Is a

thermostat? Is a web-server?


The End

Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments Lecture...

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