SPA Architectures (planning, deliberative). Science & Reality –“As far as the laws of...
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Transcript of SPA Architectures (planning, deliberative). Science & Reality –“As far as the laws of...
Science & Science & RealityReality
–“As far as the laws of mathematics refer to reality, they are not certain; as far as they are certain, they do not refer to reality” (Einstein)
Remember this dealing with formal models and deliberative robotics
Artificial Artificial IntelligenceIntelligence
• What is Artificial Intelligence?– “The science of making machines do things that
would require intelligence if done by [people]” (Minsky, 1968)
One more definition:
Are you sure that robot-frog should have human-like intelligence to solve her problems?
““Mind & Body” in AIMind & Body” in AI
• Descartes:– Mind is distinct from body
• Heidegger:– We function in the world by simply being a part
of it
• Clarke:– “mind, body and world act as equal partners”
Classical Artificial Classical Artificial IntelligenceIntelligence
• Physical Symbol System Hypothesis– “Formal symbol manipulation is both a
necessary and sufficient mechanism for general intelligent behaviour” (Newell & Simon, 1957)
• Computational Representational Understanding of Mind– “Thinking can best be understood in terms of
representational structures in the mind and computational procedures that operate on those structures” (Thagard, 1996)
Classical Artificial Classical Artificial IntelligenceIntelligence
• Shakey [Nilsson, 1969]:
- It failed….- “the principle drawback of the classical
view is that explicit reasoning about the effects of low-level actions is too expensive to generate real-time behavior” [Russell & Norvig, 1995]
a typical robota typical robot
SensorsActuators
Processor
SensorsSensors
The Old School of AI (and Robotics)The Old School of AI (and Robotics)
sense - plan - act (SPA)sense - plan - act (SPA)• Consists of 3 linear, repeated steps:
– Sense your environment
– Plan what to do next by building a world model through sensor fusion, and taking all goals into account -- both short term and long term
– Execute the plan through the actuators
• The predominant robot control mechanism through 1985
Called also sense - think - act (STA) or deliberative or planning
robots have many goalsrobots have many goals
A train is aboutto hit me
I wantto take a nap
I need to inspectthese railroad
spikes
I am about tofall over
A goal’s priority naturally will change based on context
I just wantto be loved
Tradition approach to slicing the Tradition approach to slicing the problem: SPAproblem: SPA
• decomposition by function - classical AI
Sensors Actuators
Motor control
Task execution
Planning
Modeling
Perception
All goals are known at each stage, and affect the computation
The Control Cycle: SPAThe Control Cycle: SPA• A fundamental methodology
• Derived in the early days of robotics from engineering principles
• Sense-plan-act cycle:– the principle is to continuously attempt to minimise the error between the actual
state and the desired state• based on control theory
sensecompute
(plan)act
think
modular horizontal SPA architecturemodular horizontal SPA architecture
In case of soccer robot this architecture looks as this:
• Agent design can be for instance like this:– Sequential flow– Percepts are obtained from sensors in world
(somehow)– Get a logic-based or formal description of percepts
• E.g., wumpus world percepts
– We apply search operators or logical inference or planning operators• General (replaceable) formal goal
– Arrive at some operator or operator sequence– Apply that operator sequence to world (somehow)
The Control Cycle: SPAThe Control Cycle: SPA
Path GenerationPath Generation• k = DOF of robot
• C configuration space of robot(set of points)
• O configuration space of obstacle
• F = C - O free space, the set of configurations in which the robot can move safely
Path Generation for mobile and stationary robotsPath Generation for mobile and stationary robots
A workspace with a rotary two-link arm. The goal is to move from configuration c1 to configuration c2
The corresponding configuration space, showing the free space and a path that achieves the goal
2
2
c1
c2
c1 c2
NAVIGATION AND MOTION PLANNINGNAVIGATION AND MOTION PLANNING
• Given analysis of robotics problems as motion in configuration spaces, we will begin with algorithms that handle C language directly (no parallel instructions)
• These algorithms usually assume that an exact description of the space is available, – so they cannot be used where there is significant sensor error
and motion error
• We can identify five major classes of algorithms, and arrange them roughly in order of amount of information required at planning time and execution time
• 1. Cell decomposition methods break continuous space into a finite number of cells, yielding a discrete search problem
• 2. Skeletonization methods compute a one-dimensional “skeleton” of the configuration space, yielding an equivalent graph search problem
• 3. Bounded-error planning methods assume bounds on sensors and actuator uncertainty
NAVIGATION AND MOTION NAVIGATION AND MOTION PLANNING: Classes of algorithmsPLANNING: Classes of algorithms
1. Cell decomposition method
NAVIGATION AND MOTION PLANNINGNAVIGATION AND MOTION PLANNING
A vertical strip cell decomposition of the configuration space for a two-link robot. The obstacles are dark blobs, the cells are rectangles and the solution is contained within grey rectangles.
• 4. Landmark-based navigation methods assume that there are some regions in which the robots location can be pinpointed using landmarks, whereas outside those regions it may have only orientation information
• 5. Online algorithms assume that the environment is completely unknown initially, although most assume some form of accurate position sensor– Instead, one can try to produce a conditional plan or
policy that will make decisions at run time
NAVIGATION AND MOTION NAVIGATION AND MOTION PLANNING ALGORITHMS CONT.PLANNING ALGORITHMS CONT.
NAVIGATION AND MOTION PLANNINGNAVIGATION AND MOTION PLANNING
A two-dimensional environment, robot and goal
• The problem of moving a complex-shaped object( i.e., the robot and anything it is carrying) through a space with complex-shaped obstacles is a difficult one.
• The mathematical notation of configuration space provides a framework for analysis.
• Cell decomposition and skeletonization methods can be used to navigate through the configuration space.
• Both reduce a high dimensional, continuous space to a discrete graph-search problem.
• Some aspects of the world, such as the exact location of a bolt in the robot’s hand, will always be unknown.
• Fine-motion planning deals with this uncertainty by creating a sensor-based plan that will work regardless of exact initial conditions.
SUMMARY ON NAVIGATIONSUMMARY ON NAVIGATION
• Uncertainty applies to sensors at the large scale as well.
• In the landmark model, a robot uses certain well-known landmarks in the environment to determine where it is, even in the face of uncertainty.
• If a map of the environment is not available, then the robot will have to plan its navigation as it goes.
• Online algorithms do this.
• They do not always choose the shortest route, but we can analyze how far off they will be.we can analyze how far off they will be.
SUMMARY ON NAVIGATIONSUMMARY ON NAVIGATION
Problems with SPAProblems with SPA(sense-plan-act)(sense-plan-act)
• Its monolithic design makes it slow
– At each step, we have to do:
• sensor fusion,
• world modeling,
• and planning for all goals
• Slow means we almost never can plan at the rate the environment is changing
• We end up doing “open-loop plan execution”
– inadequate in the fact of uncertainty and unpredictability
Model Based ArchitecturesModel Based Architectures• A symbolic internal ‘world-model’ is maintained:
– the sub-tasks are decomposed into functional layers– similar to ‘classical’ artificial intelligence approach
sense perception
modelling
planning
task execution
motor control actMany levels assess the model
Problems with ModelsProblems with Models• An adequate, accurate and up-to-date model must be
maintained at all times– this is very difficult in practice!– suppose, for example, the sensors detect an object that we
have not got a symbol for (a novel object)
• A model-based system is extremely brittle– if one of the functional layers fails (e.g. hardware
problems, software bugs), then the whole system fails
• Significant processing power is required– maintaining the model takes time, so slow responses!?
• Despite much effort, little progress was made!
Problems with traditional Problems with traditional approachesapproaches
• Can’t account for large aspects of Intelligence, • Reliant on representation• Rapidly changing boundary conditions• Hard to map sensor values to physical quantities• Not robust• Relatively slow response• Hard to extend• Hard to test
SourcesSources• Rodney Brooks
• Maja Mataric
• Nilsson’s book
• Jeremy Elson
• Norvig’s book, chapter 2. Good. Stimulus-Response Agents
• English PH.D thesis, recent
• Jon Garibaldi
• Prof. Bruce Donald, Changxun Wu, Dartmouth College
• Leo Ilkko
• Prof. Manuela Veloso, Dr. Tucker Balch, and Dr. Brett BrowningCarnegie Mellon University
• Rabih Neouchi , Donald C. Onyango and Stacy F. President
• Axel Roth
• Ramon Brena Pinero ITESM
• Rhee, Taik-heon, Computer Science Department, KAIST
• Brian R. Duffy, Gina Joue
• Lucy Moffatt, Univ of Sheffield
• Yorick Wilks, Computer Science Department, University of Sheffield