ROBOTICS 01PEEQW Basilio Bona DAUIN Politecnico di Torino Mobile - - PowerPoint PPT Presentation

ROBOTICS 01PEEQW

Basilio Bona DAUIN – Politecnico di Torino

Mobile & Service Robotics Supervision and control

Traditional approach

Traditional artificial intelligence considers robot “brains” as sequential processing units

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Main ideas Representation -> reasoning -> planning Model building (maps of the environment is required) Functional decomposition; hierarchical systems Symbolic/semantic manipulation

Supervision and Control

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Localization & Map Building Path planning & Reasoning Position Global map Environment Data treatment Sensors Data treatment Actuators data Perception Motion control a priori knowledge Task/mission commands commands iteration

Supervision and Control

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Localization Map Building Path planning Reasoning

Position Global map

Perception Motion control Environment

Local map World model Path

Control Strategies

Control loop requirements

World changes dynamically A compact model of the world is very difficult to define There are many sources of uncertainty, both in the world and in the robot

Two possible approaches + a combination of them

Classic AI – deliberative model

Approximate world modeling (model-based method) Based on a set of functions Vertical decomposition Top-down approach

Modern AI – reactive model

No world model is given: behavior-based control Horizontal decomposition Bottom-up approach

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Control Characteristics

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This architecture may produce a slow and delayed response sense use model plan act Vertical approach Function 1 Function 2 Function 3 Function 5 Function 4 Sense – Plan – Act Subsumption/Reactive model Horizontalapproach

Model-Based Approach

Requires the complete modeling of the world Each block is a computed function Vertical decomposition and nested-embodiment of functions

An example Perception Cognitive planning Localization - Map building Motion control

sensors actuators

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Model-Based Approach

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Low level control Planner Navigator Path monitor Pilot Controller

GOAL RECOGNITION GLOBAL PATH PLANNING SUB-GOAL FORMULATION LOCAL PATH PLANNING TARGET GENERATOR DYNAMIC PATH PLANNING TARGET LOCATION PATH CORRECTION/OBSTACLE AVOIDANCE COMMANDS SENSORS ACTUATORS

Behavior-Based Approach

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Behavior-Based Approach

Rodney Brooks, inventor of this approach, writes:

Complex behavior needs not necessarily be the product of a complex control system The world is its best model Simplicity is a virtue Intelligence is in the eye of the observer Robots should be cheap Robustness in the presence of noisy or failing sensors is a design goal Planning is just a way of avoiding figuring out what to do next All onboard computation is important Systems should be built incrementally No representation. No calibration. No complex computers. No high band communication

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Behavior-Based Approach

Various names given to this approach Subsumption architecture Reactive system Reflexive behavior Perception-action

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WORLD

Perception 1 Perception 2 Action 1 Action 2

Subsumption architecture

The subsumption architecture was originally proposed by Brooks [1986] The subsumption architecture copies the synergy between sensation and actuation in lower animals such as insects Brooks argues that instead of building complex agents in simple worlds, we should follow the evolutionary path and start building simple agents in the real, complex and unpredictable world From this argument, a number of key features of subsumption result

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Subsumption architecture

1. No explicit knowledge representation is used. Brooks often refers to this as “The world is its own best model” 2. Behavior is distributed rather than centralized 3. Response to stimuli is reflexive – the perception-action sequence is not modulated by cognitive deliberation 4. The agents are organized in a bottom-up fashion. Thus, complex behaviors are fashioned from the combination of simpler, underlying ones 5. Individual agents are inexpensive, allowing a domain to be populated by many simple agents rather than a few complex ones. These simple agents individually consume little resources (such as power) and are expendable, making the investment in each agent minimal 6. Several extensions have been proposed to pure reactive subsumption systems. These extensions are known as behavior- based architectures

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Behavior-Based Approach

Characteristics No model is necessary Horizontal decomposition Coordination + Priority = Fusion Biomimesis = observe and copy animal behavior Subsumption Embodiment

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Avoid obstacles Discover new areas Detect goal position Communicate data Recharge Follow right/left wall sensors actuators Coordination / fusion

∑

Behavior-Based Approach

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Behavior-Based Approach

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Behavior-Based Approach

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Subsumption architecture

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Subsumption architecture

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Subsumption architecture

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Control Strategies: deliberative vs reactive

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Predictive capabilities Speed of response Depends on accurate world models DELIBERATIVE Model-based REACTIVE Behavior-based Purely symbolic Reflexive

• Depends on the world

representation

• Slow response
• High level intelligence (cognition)
• Variable latency
• Representation-free
• Real-time response
• Low level intelligence (stimulus-

response)

• Fast and easy computation

Embodiment

To embody (verb) = to manifest or personify in concrete form; to incarnate; to incorporate, to unite into one body Embodiment is the way in which human (or any other animal) psychology arises from the brain & body physiology Embodiment theory was introduced into AI by Rodney Brooks in the ‘80s. Brooks have claimed that all autonomous agents need to be both embodied and situated The theory states that intelligent behavior emerges from the interplay between brain, body and world. Brain, body and world are equally important factors in the explanation of how particular intelligent behaviors originate in practice Brooks showed that robots could be more effective if they “thought” (planned or processed) as little as possible The robot's intelligence is organized only for handling the minimal amount of information necessary to make its behavior be appropriate and/or as desired by its creator

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Embodiment

Rolf Pfeifer (AILab Zurich) says that there are essentially two directions in artificial intelligence:

• ne focused on developing useful algorithms or robots;

and another direction that focuses on understanding intelligence, biological or artificial

In order to make progress on the second direction, an embodied perspective is mandatory

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Situated robot

A situated robot is one which does not deal with abstract representations of the world (which may be simulated or real), but rather reacts directly to its environment as seen through its sensors An alternative to having a situated robot would be one which builds up a representation of its world and then makes plans based on this representation Because of the limitations of our present technology, these two approaches often seem contradictory At present, each approach can be appropriate for different applications

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Situated robot

The situated approach is good for dealing with problems where planning ahead is unnecessary or takes too much time However, the representation approach is needed for solving more complicated problems where it is necessary to reason about the state of the world For dealing with complicated tasks in the real world, it will probably be necessary to fuse the two approaches Reasoning can be used to build up higher level plans and solve high level problems Lower level functions may use a more situated approach for carrying

• ut plans and dealing with problems which need immediate

attention The structure and relations that originates from the interaction of simple controllers and complex environment is called emergent behavior

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Robotics and AI

Main areas of Artificial Intelligence applied to robotics

• 1. Knowledge representation
• 2. Understanding natural languages
• 3. Learning
• 4. Planning and problem solving
• 5. Inference
• 6. Search
• 7. Vision

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Knowledge representation

Define, build and memorize the physical and virtual structures used by the robot to represent

the world the desired tasks itself

Example: a robot is looking after a human being under the wreckage of a fallen building: how it is represented?

Structural model:

Head (oval) + trunk (cylindrical) + arms (cylindrical) Bilateral symmetry

Physical model (thermal image, …) What happens if the body is only partially visible?

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Understanding natural languages

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Natural language is one of the most simple and human ways to interact But … To understand the words does not mean to understand the meaning Grammatical structure vs Semantic structure Example We gave the monkeys two bananas because they were hungry We gave the monkeys two bananas because they were over-ripe They have the same grammatical structure, but a very different semantic structure To understand the sense we must know both the monkeys and the bananas Necessity to develop ontologies

Ontology

An ontology is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those

• concepts. It is used to reason about the entities within that domain,

and may be used to describe the domain An ontology is a “formal, explicit specification of a shared conceptualization.” An ontology provides a shared vocabulary, which can be used to model a domain — that is, the type of objects and/or concepts that exist, and their properties and relations Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, etc., as a form of knowledge representation about the world or some part of it

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Learning

Learning is the capacity to memorize actions and behaviors and to repeat them to adapt to the implicit or explicit objectives In a broad sense, learning is the ability to adapt during life We know that most living organisms with a nervous system display some type of adaptation during life The ability to adapt quickly is crucial for autonomous robots that

• perate in dynamic and partially unpredictable environments, but the

learning systems developed so far have so many constraints that are hardly applicable to robots that interact with an environment without human intervention Learning requires

• A structure able to store and retrieve data
• One or more explicit objectives
• An adaptation mechanism (reward + punishment)
• An explicit or implicit teacher

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Planning and problem solving

Intelligence is associated to the ability to plan actions toward the the given task fulfillment, and to solve problems arising when plans fail

Go there Go there

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Planning and problem solving

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SLAM

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SLAM Simultaneous Localization and Mapping

Inference

Inference is a procedure that allows to generate an answer also when the available data or information are incomplete Inference is based on statistical models (bayesian networks) or semantic models

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Search

Search does not necessarily mean a true search of objects in space, but defines the ability to examine a knowledge representation data-base (search space) to find the required answer Consider a computer playing chess: the best move is found looking for a solution in the search space of all possible moves, starting from the present chessboard state

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Vision

Vision is the most important sense in human beings Psychological studies have demonstrated that the ability to solve problems is due to our brain capacity to visualize the effects of each action

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Books

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R.C. Arkin Behavior-Based Robotics MIT Press, 1998 R.R. Murphy Introduction to AI Robotics MIT Press, 2000

• G. Dudek, M. Jenkin

Computational Principles of Mobile Robotics Cambridge U.P., 2000

• R. Siegwart, I.R. Nourbakhsh

Autonomous Mobile Robots MIT Press, 2004

Books

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Autori Vari Principles of Robot Motion MIT Press, 2005

• S. Thrun, W. Burgard, D. Fox

Probabilistic Robotics MIT Press, 2005 Rolf Pfeifer, Josh C. Bongard How the Body Shapes the Way We Think A New View of Intelligence Foreword by Rodney Brooks MIT Press, 2006