Download Architectures for Robot Control

Architectures for Robot Control
Intelligent Robotics
2014/15
Bruno Lacerda
This Lecture
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Deliberative paradigm
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STRIPS
Reactive paradigm
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Behaviour-based architectures
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Hybrid paradigm
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The SMACH package (ROS)
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Architectures for Robot Control - Overview
Deliberative Paradigms
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Sense the world
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Update world model and generate plan
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Knowledge representation + Automated reasoning
Execute planned action
Bottleneck
Classical / Hierarchical Paradigm
Sense
Plan
Act
! 70’s
! Focus on automated reasoning and knowledge representation
! STRIPS (Stanford Research Institute Problem Solver): Perfect
Intelligent
14/15
world model, closed
worldRobotics
assumption
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Deliberative Paradigms
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Knowledge representation:
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Closed world assumption: World model contains everything
the robot needs to know
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Frame problem: How to model everything the robot needs
to know while keeping the size of the state space
manageable?
Classical / Hierarchical Paradigm
Sense
Plan
Act
! 70’s
! Focus on automated reasoning and knowledge representation
! STRIPS (Stanford Research Institute Problem Solver): Perfect
Intelligent
14/15
world model, closed
worldRobotics
assumption
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Deliberative Paradigms
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Planning:
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Exploring the possible states of the world to find a plan is
computationally expensive (e.g., STRIPS planning is NPcomplete)
Classical / Hierarchical Paradigm
Sense
Plan
Act
! 70’s
! Focus on automated reasoning and knowledge representation
! STRIPS (Stanford Research Institute Problem Solver): Perfect
Intelligent
14/15
world model, closed
worldRobotics
assumption
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Deliberative Paradigms
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Sensing and acting are disconnected:
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After sensing we always need to update the world model
and generate a plan
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No reactivity
Classical / Hierarchical Paradigm
Sense
Plan
Act
! 70’s
! Focus on automated reasoning and knowledge representation
! STRIPS (Stanford Research Institute Problem Solver): Perfect
Intelligent
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world model, closed
worldRobotics
assumption
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Shakey
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Control architecture based on deliberative paradigm
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Plan step is based on STRIPS
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Knowledge representation: First-order predicate logic
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Planning: Logic based planning
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Remember lectures on classical planning
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Shakey
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Reactive Paradigm
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No world model; no planning
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Direct mapping between sensor input and actuators
output
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Very reactive to changes in sensor readings
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(Radical) answer to shortcomings of deliberative paradigm
Reactive / Behavior-based Paradigm
Sense
Act
! No models: The world is its own, best
model
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Reactive Paradigm
“ AI researchers ... partition the problems they work on into two
components. The AI component, which they solve, and the nonAI component which they don’t solve. Typically AI ’succeeds’ by
defining the parts of the problem that are unsolved as not AI. The
principal mechanism for this partitioning is abstraction ... In AI,
abstraction is usually used to factor out all aspects of perception
and motor skills. I argue below that these are the hard problems
solved by intelligent systems, and further that the shape of solutions to these problems constrains greatly the correct solutions of
the small pieces of intelligence which remain.”
Rodney Brooks, Intelligence without representation, 1991,
Artificial Intelligence, Vol 47.
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Behaviour-Based Architectures
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Overall controller composed of two parts
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Task achieving controllers (e.g., simple state machines)
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Arbitrating controller (also task specific)
Controllers:
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Are reactive (map perception to action)
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Do not include world models, no planning
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Are only capable of performing one task each
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Should perform it very efficiently
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Behaviour-Based Architectures
Push
Go through door
Arbitration
Approach object
Follow a wall
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Behaviour-Based Architectures
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Behaviours are the basic building block for robotics
actions, with an overall emergent behaviour obtained from
their arbitration
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︎Behaviours support good software design principles due to
modularity
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However, we have some assumptions:
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We need to be able to decompose the task into appropriate
“primitive” behaviours
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Each “primitive” behaviour requires only simple perceptual
and physical talents
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Behaviour-Based Architectures
Different behaviourbased architectures
have different
approaches to
provide arbitration
Push
Go through door
Arbitration
Approach object
Follow a wall
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The Subsumption Architecture
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Behaviours are networks of sensing and acting modules
(finite state machines)
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︎ Modules are grouped into layers of competence
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Top layers can subsume lower layers
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Herbert
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Shortcomings of Behaviour-Based Architectures
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Very task oriented - no flexibility
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No world model means no memory or ability to reason
about optimal behaviours
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Arbitrating a large set of behaviours properly is a
cumbersome and error-prone task
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Complex tasks require arbitration of a large set of
behaviours
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Hybrid Paradigm
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World model, used for planning
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Closed-loop, reactive control
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Many architectures of today follow this paradigm
Hybrid Deliberative/reactive
Paradigm
Plan
Sense
Act
! Combines advantages of previous paradigms
!
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World model used for planning
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Reading
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Deliberative paradigm
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[Fikes, Nilsson (1971)] STRIPS: a new approach to the
application of theorem proving to problem solving. Artificial
Intelligence, 2:189-208.
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http://www.sri.com/work/timeline-innovation/timeline.php?
timeline=computing-digital#!&innovation=shakey-the-robot
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Reading
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Reactive paradigm
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[R. A. Brooks]
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(1986), ”A Robust Layer Control System for a Mobile Robot",
IEEE Journal of Robotics and Automation RA-2, 14-23.
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(1987), "Planning is just a way of avoiding figuring out what
to do next", Technical report, MIT Artificial Intelligence
Laboratory.
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(1991), "Intelligence Without Representation", Artificial
Intelligence 47 (1991) 139-15
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Reading
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General bibliography:
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Behavior-based Robotics [Arkin]
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Introduction to AI Robotics [Murphy]
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The SMACH package
Overview
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Python implementation of robot executive based on
hierarchical state machines
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Also provides concurrency
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See http://wiki.ros.org/smach
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