Transcript Introduction to Robotics - Department of Mathematics

Introduction to Robotics
13. Deliberative and Hybrid
Control.
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Deliberative Systems
 Purely deliberative systems are considered the classical control
architecture, since they were the first to be tried.
 In AI, classical deliberative, planner-based architectures were used for
reasoning about actions in various non-physical domains, such as
chess.
 As a result, the same architectures were applied to robotics as well.
In the 1960’s: Shakey
In the late 1960's, the state-of-the-art in machine vision was used to
process visual information on a robot called Shakey, the forerunner of
many AI-inspired robotics projects.
Shakey used a classical planner as the underlying structure to decide
what to do.
What is planning?
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Traditional Deliberative Planners
• Are often aligned with hierarchical control community
within robotics.
• Hierarchical planning systems typically share a structured
and clearly identifiable subdivision of functionality
regarding to distinct program modules that communicate
with each other in a predictable and predetermined
manner.
• At a hierarchical planner’s highest level, the most global
and least specific plan is formulated.
• At the lowest levels, rapid real-time response is required,
but the planner is concerned only with its immediate
surroundings and has lost the sight of the big picture.
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Spatial
Scope
Global
Hierarchical
Time
World Model
Planner
Strategic
Global
Planning
Tactical
Intermediate
Planning
Global
Horizon
Long - Term
Knowledge
Local
World
Model
Short-Term
Local
Planning
Intermediate
Immediate
Actuator
Control
Sensor
Interpretations
Vicinity
Actions
Sensing
Real - Time
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Planning as Search
• Planning is looking ahead, searching
 The goal is a state.
 The robot's entire state space is enumerated, and searched,
from the current state to the goal state.
 Different paths are tried until one is found that reaches the
goal.
 If the optimal path is desired, then all possible paths must
be considered in order to find the best one.
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SPA = Planner-based
 Planner-based (deliberative) architectures typically involve three
generic sequential steps or functional modules:
1) sensing (S)
2) planning (P)
3) acting (A), executing the plan
 Thus, they are called SPA architectures.
 SPA has serious drawbacks.
• It takes a very (prohibitively) long time to search in a real
Problem 1:
Time Scale
robot's state space, as that space is typically very large.
 Real robots may have collections of simple digital
sensors (e.g., switches, IRs), a few more complex ones
(e.g., cameras), or analog sensors (e.g., encoders, gauges,
etc.)
 => "too much information"
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SPA = Planner-based
Problem 2:
Space
 It takes a lot of space (memory) to represent and
manipulate the robot's state space representation.
 The representation must contain all information needed
for planning.
 => Generating a plan can be large.
 Space is not nearly as much of a problem as time, in
practice.
 The planner assumes that the representation of the state
space is accurate and up-to-date.
Problem 3:
Information
 => The representation must be constantly updated and
checked
 The more information, the better.
 => "too little information"
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SPA = Planner-based
Problem 4:
Use of Plans
The resulting plan is only useful if:
a) the environment does not change during the execution
of a plan in a way that affects the plan.
b) the representation was accurate enough to generate a
correct plan.
c) the robot's effectors are accurate enough to perfectly
execute each step of the plan in order to make the
next step possible
Deliberation in Summary
 In short, deliberative (SPA, planner-based) approaches:
 require search and planning, which are slow
 encourage open-loop plan execution, which is limiting and dangerous
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 Note that if planning were not slow (computationally expensive) then
execution would not need to be open-loop, since re-planning could be done.
Hierarchical Planners vs. BBS
Hierarchical Planners
• Rely heavily on world models,
• Can readily integrate world knowledge,
• Have a broad perspective and scope.
BB Control Systems
• afford modular development,
• Real-time robust performance within a changing world,
• Incremental growth
• are tightly coupled with arriving sensory data.
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Inventing Hybrid Control
• The basic idea is simple: we want the best of both worlds
(if possible).
• The goal is to combine closed-loop and open-loop
execution.
• That means to combine reactive and deliberative
control.
•
This implies combining the different time-scales and
representations.
• This mix is called hybrid control.
 Hybrid robotic architectures believe that a union of deliberative and
behavior-based approaches can potentially yield the best of both worlds.
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Organizing Hybrid Systems
Planning and reaction can be tied:
A: hierarchical integration planning and reaction are involved
with different activities, time scales
B: Planning to guide reaction configure and set parameters for the
reactive control system.
C: coupled - concurrent activities
More Deliberative
Level N
Planner
Deliberation Projection
Level 2
Behavioral Advice
Configurations
Level 1
Parameters
Level 0
Reactor
More Reactive
A
Planner
B
Reactor
C
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Organizing Hybrid Systems
 It was observed that the emerging architectural design of choice
is:
– multi-layered hybrid comprising of
* a top-down planning system and
* a lower-level reactive system.
– the interface (middle layer between the two components) design is
a central issue in differentiating different hybrid architectures.
 In summary, a modern hybrid system typically consists of three components:
 a reactive layer
 a planner
 a layer that puts the two together.
 => Hybrid architectures are often called three-layer architectures.
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The Magic Middle
 The middle layer has a hard job:
1) compensate for the limitations of both the planner and the reactive
system
2) reconcile their different time-scales.
3) deal with their different representations.
4) reconcile any contradictory commands between the two.
 This is the challenge of hybrid systems
=> achieving the right compromise between the two ends.
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The middle layer services.
Reusing Plans
 Some frequently useful planned decisions may need to be reused,
so to avoid planning, an intermediate layer may cache and look those
up. These can be:
 intermediate-level actions (ILAs): stored in contingency tables.
 macro operators: plans compiled into more general operators for future use.
Dynamic Re-planning
 Reaction can influence planning.
 Any "important" changes discovered by the low-level controller are
passed back to the planner in a way that the planner can use to re-plan.
 The planner is interrupted when even a partial answer is needed in realtime.
 The reactive controller (and thus the robot) is stopped if it must wait for
the planner to tell it where to go.
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The middle layer services.
Planner - Driven Reaction
• Planning can also influence reaction.
• Any "important" optimizations the planner discovers are passed
down to the reactive controller.
• The planner’s suggestions are used if they are possible and safe.
=> Who has priority, planner or reactor? It depends, as we will see...
Types of “Reaction  Planning”
Interaction
 Selection: Planning is viewed as configuration.
 Advising: Planning is viewed as advice giving.
 Adaptation: Planning is viewed as adaptation of controller.
 Postponing: Planning is viewed as a least commitment process.
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Universal Plans
 Suppose for a given problem, all possible plans are generated for all
possible situations in advance, and stored.
 If for each situation a robot has a pre-existing optimal plan, it can react
optimally, be reactive and optimal.
 It has a universal plan (These are complete reactive mappings).
Viability of Universal Plans
 A system with a universal plan is reactive; the planning is done at
compile-time, not at run-time.
 Universal plans are not viable in most domains, because:
 the world must be deterministic.
 the world must not change.
 the goals must not change.
 the world is too complex (state space is too large).
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Situated Automata
 A formal notion of finite state machines
 whose inputs are connected to sensors and
 whose outputs are connected to effectors
are called situated automata.
State
Register
Sensor
Action
Input
The basic structure of Rosenschein’s situated
automata design.
 Situated means existing in and interacting with a complex world, and automata is
the formal name for FSMs (formally: finite state automata).
 Situated automata are used to create reactive principled control systems.
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 Developed by Kaelbling & Rosenschein ‘91. We already mentioned situated automata in BBS.
Control with Situated Automata
Situated automata can be constructed in two basic ways:
o By hand (i.e., the designer puts FSMs together), as in the
Subsumption Architecture).
o By pre-compiling a complete plan (similar to Universal Plans,
but reduced down to circuits of FSMs).
 Rosenschein was able to design a compiler that
generates finite state machines whose
 internal states can be proved to correspond to certain logical
propositions about the environment,
 provided that the initial state and the correct laws of
“physics” are given to the compiler.
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Situated Automata
• Rosenschein’s basic design relies on a theorem to the effect that any finite state
machine can be implemented as a state register together with a feedforward
circuit that updates the state based on the sensory inputs and the current state, and
another circuit that calculates the output given the state register.
This requires the use of a special programming language that implements the
right semantics and compiles down into FSM circuitry, as Rex and Gapps.
Flakey, the robot capable in 1984 to navigate the halls of SRI was
based on situated automata.
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Pre - compiled Systems
Advantage: Domain Knowledge
 A key advantage of pre-compiled systems is that domain knowledge,
i.e., information that the designer has about the environment, the robot,
and the task, can be embedded into the system in a principled way.
 Then, the system is compiled into a reactive circuit, so the knowledge
does not have to be reasoned about (or planned with) explicitly, in
real-time.
Disadvantages
 A key disadvantage of pre-compiled systems is that it quickly becomes
prohibitively large to enumerate the state space of a real robot, and thus
pre-compiling generally does not scale up to complex systems.
 Another disadvantage is common to compiled or hard-wired systems:
the result is not flexible in the presence of changing environments, tasks
or goals.
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Summary: Hybrid Control Relative Strength.
• Deliberative planners:
 Rely heavily on world models.
 Can readily integrate world knowledge.
 Have broader perspective and scope.
 Reactive & behavior-based systems:
 Afford modular development.
 Provide real-time robust performance in dynamic world.
 Provide for incremental growth.
 Tightly coupled to incoming sensory data.
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Representative Hybrid Architectures.
 Selection: Planning is viewed as configuration.
 Advising: Planning is viewed as advice giving.
 Adaptation: Planning is viewed as adaptation of controller.
 Postponing: Planning is viewed as a least commitment process.
Let’s discuss an example of each of these strategies:
* AuRA for selection,
* Atlantis for advising,
* Planner - Reactor for adaptation, and
* PRS as postponent.
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Example: AuRA
• R. Arkin (1986)
 Planning is viewed as configuration.
 Initial A* planner integrated with schema-based controller.
 Provides modularity, flexibility, and adaptability.
Learning
Plan Recognition
User Input
User Intentions
User Profile
Spatial Learning
Opportunism
On-line
Adaptation
Spatial Goals
Mission
Mission Planner
Spatial Reasoner
Plan Sequencer
Component
Reactive
Alterations
Teleautonomy
Hierarchical
RE
PRE
SEN
TA
TI
ON
Schema Controller
Motor Perceptual
Actuation
Sensing
Component
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AuRA
• Planning and execution components:
– a hierarchical system  mission planner, spatial reasoner, and
plan sequencer.
* traditional : highest level is a mission planner establishes high - level
(global) goals + constraints.
* mission planner acts as an interface to human operator.
* spatial reasoner navigator, uses knowledge stored in LTM to
construct a sequence of path legs that the robot must execute.
* plan sequencer (pilot) translates each path leg into a set of motor
behaviors for execution.
o In the original implementation it was a rule based system
o Now a finite state sequencer was implemented.
– coupled with a reactive system  the schema controller.
• The collection of behaviors (schemas), specified by the sequencer, is sent to
the reactive system for execution.
– At this point, deliberation ends/stops, and reactive execution begins.
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A* Search
• Best-first search using f as the evaluation function and an
admissible h function.
• Evaluation function f (n)= estimated cost of the cheapest
solution through node n.
f(n)= g(n)+ h(n)
– g(n) gives the path cost from start to node n
– h(n) is the estimated cost of the cheapest path from n to the goal.
•
If h never overestimates the cost is called admissible
heuristic.
– Example: estimate the distance between to cities by rhe straightline distance.
• A*
– exhibit monotonicity (along any path from the root f never decreases).
– optimally efficient (algorithm that extend search paths from the root).
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Example: Atlantis

E. Gat (1991)

Three layers: controller, sequencer, deliberator.

Asynchronous, heterogeneous: reactivity and deliberation

Planning as advice giving, not as command (not a decree).

Tested on NASA rovers.
Deliberative
Invocation
Results
Sequencing
Status
Sensors
Activation
Control
Actuators
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Atlantis
• Control Layer:
– Reactive controller charged with managing collection of primitive
activities.
– Implemented in ALFA, a LISP based program language, similar to Rex, a
circuit based language.
• Sequencing Layer:
– Modeled after RAP (Reactive Action Packages, Firby 1989).
– RAP is a situation-driven execution reactive method, in which the current
situation provides an index into a set of actions regarding how to act in that
environment.
– Conditional sequencing occurs upon the completion of subtasks or
detection of failure.
– Notion of cognizant failure was introduced, referring to the robot’s ability
to recognize on its own when it has not or cannot complete its task: it has
knowledge about its failures.
*Monitor (task specific) routines are added to determine if things are
going as they should and then interrupt the system if cognizant failure
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occurs.
Atlantis
• Deliberative Layer.
– Deliberation occurs at the sequencing layer’ request.
– Consists of traditional LISP based AI planning algorithms, specific to the
task at hand.
– The planner’s output is viewed only as advice to the sequencer layer: it is
not necessarily followed or implemented.
• Design proceeds from bottom up:
– low-level activities capable of being executed within the reactive
controller are first constructed.
– suitable sequences of primitive behaviors are then constructed.
– Followed by deliberative methods that assist in the decision done by the
sequencer.
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Example: Planner-Reactor
• D. Lyons (1992)
 Continuous modification of a reactive control system (sub-optimal).
 Planning is a form of reactor adaptation.
 Adaptation is on-line rather than off-line deliberation.
 Planning is used to remove performance errors when they occur.
 Uses Robot Schema (RS) model.
 Tested in both assembly cell and grasp planning.
Goals
Adaptation
Planner
Perception
REACTOR
Reactions
Perceptions
Action
World
Sensing
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Planner-Reactor
• A suboptimal reactor may be present at any time.
• The planner’s goal is to improve the performance of the
reactor at all times (any-time planning).
– Any-time planners provide approximate answers in a time
critical manner:
* at any point a plan is available for execution, and
* the quality of the available plan increases over time.
– Situations provide the framework for structuring sets of reactions.
* can be defined hierarchically, as behavioral structures for use
in the reactor and not specific robotic commands.
* denote the state of the robotic agent is currently in (regarding
a task).
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Planner-Reactor
• Adaptation:
a) a reactor executes under a set of operating assumptions.
b) if any assumptions are violated, the planner modifies the reactor’s
control system to remove the violation.
– Each assumption has a monitor associated with it during run time to
ensure its validity.
Reactor
Adapted by
Planner and
Assumptions
Relaxed.
Adapt Reactor
Initial
Reactor
Construction
Start
Execution
Adapt Reactor
Restore
Initial
Reactor.
Assumptions
violation
detected.
Reactor
Performance
with
Monitoring
Violation
assumptions
Restored.
Normal
Performance
Completed
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Example: PRS
 Georgeff and A. Lansky (1987)
 PRS = Procedural Reasoning System
 Reactivity refers to postponement of the elaboration
of plans until it is necessary:
 a least commitment strategy.
 Tested on SRI Flakey
OPERATOR
INTRFACE
BELIEFS
DESIRES
PLANS
INTERPRETER
MONITOR
INTENTIONS
COMMAND
GENERATOR
SENSORS
ACTUATORS
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PRS
• Plans are the primary mode of expressing action.
– They are continuously determined in reaction to the current situation.
– Previously formulated plans undergoing execution can be interrupted
and abandoned at any time.
• Representations of the robot’s beliefs, desires, and intentions are all
used to formulate a plan.
• The plan represents the robot’s desired behaviors instead the
traditional AI planner’s output of goal states to be achieved.
• The interpreter drives system execution, handling the plan switching.
• A symbolic plan always drives the system.
– it is not reactive in the normal sense of tight sensori-motor pair
execution
– it is reactive in the sense that perceived changing environmental
conditions permit the robotic agent to alter its plans on the fly.
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Hybrids Everywhere?
 Hybrid systems are the most popular
alternative for single-robot control.
 Behavior-based systems are not used by
quite as many researchers, but have
more specialized niches (e.g., multirobot systems) and more practical
applications.
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