ROBOTICS COE 584 Deliberative & Hybrid Control
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Transcript of ROBOTICS COE 584 Deliberative & Hybrid Control
ROBOTICS
COE 584
Deliberative & Hybrid Control
Lecture Outline Lecture Outline Deliberative control Hybrid control Types of layer organization
selection advising adaptation postponement
Examples of hybrid control AuRA, Atlantis SSS, PRS
Deliberative Systems 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 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?
Planning as Search 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
SPA = Planner-based 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 What are they?
Problem 1: Time Problem 1: Time Complex state spaces:
very slow plan generation
Dynamic worlds: out of date plans (latency)
Problem 2: Space Problem 2: Space Representation of state space may
be very large Search tree (intermediate plan data)
may be very large Modern machines have virtual
memory (page to disk), but swapping is very slow
Problem 3: Representation Problem 3: Representation
Representation for planning has two parts: Knowing the state of the world Predicting the outcome of actions
State representation assumed to be: complete accurate current predictable
Problem 3: Representation Problem 3: Representation
Sensors have: noise inaccuracies aliasing (partial observability)
Effectors are: unpredictable unreliable
None of the assumptions are valid!
Problem 4: Execution Problem 4: Execution Execution is assumed to be:
sequential reliable unique (one actor)
But: blind execution of long sequences of
unreliable actions will fail E.g., p(success | 1 action) = 0.90 => p(success | 10 actions) = 0.35
Deliberative Summary Deliberative Summary In short, deliberative (SPA)
approaches: require search (slow) require representations (hard) encourage open-loop execution
(dangerous)
Opposition to SPA Opposition to SPA As a consequence, much opposition
from real robot practitioners mounted against SPA architectures
In the early/mid 1980's alternatives were proposed reactive systems hybrid systems
What happened to purely deliberative systems?
Role of Pure Deliberation Role of Pure Deliberation
Pure deliberation is alive and well in other domains, like game playing (chess, go, etc.) and other static worlds with plenty of time to plan
Planners Live On in Robotics Planners Live On in Robotics
The SPA approach has not been abandoned, it has been expanded
Given the two fundamental problems with purely deliberative approaches, we can augment them: search/planning is slow, so save/cache
important and/or urgent decisions; open-loop plan execution is bad, use
closed-loop feedback, and be ready to respond or re-plan when the plan fails.
Reusing Plans 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) macro operators: plans compiled into
more general operators for future use
Universal Plans 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 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 they require that: 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)
Situated Automata 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.
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.
Control w/ Situated AutomataControl w/ Situated Automata
Situated automata can be constructed in two basic ways: By hand (i.e., the designer puts FSMs
together), as in the Subsumption Architecture). By pre-compiling a complete plan (similar to
Universal Plans, but reduced down to circuits of FSMs). This requires the use of a special programming language that implements the right semantics and compiles down into FSM circuitry, as Rex and Gapps.
Domain Knowledge 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 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
Inventing Hybrid ControlInventing 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
Organizing Hybrid Systems Organizing Hybrid Systems
A 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 (TLA)
The planner and the reactive system are both standard, as we have covered them so far
The Magic Middle 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
Interaction of Layers Interaction of Layers
Hierarchical integrationPlanning guides reaction
Coupled planning & reacting
Dynamic Re-planning 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 real-time
The reactive controller (and thus the robot) is stopped if it must wait for the planner to tell it where to go.
Planner-Driven Reaction Planner-Driven Reaction
Planning can 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?
Types of Interaction Types of 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
Selection Example: AuRA Selection Example: AuRA
R. Arkin (1986)
Planning is viewed as configuration Initial A* planner integrated with
schema-based controller Provides modularity, flexibility, and
adaptability
AuRA Schematic AuRA Schematic
Advising Example: Atlantis Advising Example: Atlantis
E. Gat (1991) (JPL) Three layers: controller, sequencer, deliberator Asynchronous, heterogeneous: reactivity and
deliberation Implemented in ALFA (A Language for Action) Planning as advice giving, not decree Notion of cognizant failure Tested on NASA rovers
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Rocky 4
Atlantis Schematic Atlantis Schematic
Adaptation Example: Planner-ReactorAdaptation Example: Planner-ReactorD. Lyons (1992)Continuous modification of a reactive control
systemPlanning is a form of reactor adaptationAdaptation is on-line rather than off-line
deliberationPlanning is used to remove performance
errors when they occurUses a particular underlying mathematical
model called a process algebraTested in both assembly cell and grasp
planning
Planner-Reactor ArchitecturePlanner-Reactor Architecture
REACTOR
PLANNER WORLDPERCEPTIONS
REACTIONS
ADAPTION ACTION
SENSINGPERCEPTION
GOALS
Postponing Example: PRSPostponing Example: PRS PRS = Procedural Reasoning System Georgeff and A. Lansky (1987)
Least commitment via plan elaboration postponement
Tested on SRI Flakey
Flakey the robot Flakey the robot
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PRS Schematic PRS Schematic
Another Example: SSS Another Example: SSS J. Connell (1992)
SSS = Servo Subsumption Symbolic 3 layers: servo, subsumption, symbolic World models are a convenience, not a
necessity Symbolic: where-to-next (discrete time) Subsumption: where-to-go-now Servo: making it go (continuous time)
Tested on TJ
SSS Implementation: T J SSS Implementation: T J
More Examples More ExamplesSOMASS hybrid assembly system
C. Malcolm and T. Smithers (Edinburgh U.)
cognitive/subcognitive components planning as configuration
Agent architecture B. Hayes-Roth (Stanford) physical and cognitive levels functional boundary blurry
Multi-valued logic Saffiotti, Konolige, Ruspini (SRI)
Even More Examples Even More Examples
Supervenience L. Spector (1992, U. of Maryland)
Multiple levels of abstraction
Teleo-reactive agent architecture Benson and N. Nilsson (1995, Stanford)
Planning yields TR operator tree
Reactive Deliberation M. Sahota (1993, U. of British Columbia)
Robosoccer
Still More Examples Still More Examples Theoagent
T. Mitchell (CMU, 1990)Reacts when it can plans when it mustEmphasis on learning
Generic Robot ArchitectureNoreils and Chatila (1995, France)3 levels: planning, control system, functional
Dynamical Systems ApproachSchoner and Dose (1992)Planning is selecting and parameterizing
behavioral fields
Behaviors use vector summation
And Still More Examples And Still More Examples
Integrated path planning and dynamic steering control Krogh and C. Thorpe (1986, CMU)
Relaxation over grid-based model with potential fields controller
Planner generated waypoints for controller
Many others (including several for UUVs)
Hybrids Everywhere? 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., multi-robot systems) and more practical applications
Textbook Readings Textbook Readings MM 13, 15