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