ROBOTICS

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• ROBOTICS
• COE 584
• Deliberative Hybrid Control

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Lecture Outline

• Deliberative control
• Hybrid control
• Types of layer organization
• selection
• advising
• adaptation
• postponement
• Examples of hybrid control
• AuRA, Atlantis
• SSS, PRS

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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

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In the 1960s 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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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
• What are they?

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Problem 1 Time

• Complex state spaces
• very slow plan generation
• Dynamic worlds
• out of date plans (latency)

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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

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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

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Problem 3 Representation

• Sensors have
• noise
• inaccuracies
• aliasing (partial observability)
• Effectors are
• unpredictable
• unreliable
• None of the assumptions are valid!

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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
• gt p(success 10 actions) 0.35

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Deliberative Summary

• In short, deliberative (SPA) approaches
• require search (slow)
• require representations (hard)
• encourage open-loop execution (dangerous)

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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?

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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

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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.

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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

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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)

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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)

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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.
• 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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Control 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.

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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

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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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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

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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

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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

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Interaction of Layers

• Hierarchical integration
• Planning guides reaction
• Coupled planning reacting

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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.

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Planner-Driven Reaction

• Planning can influence reaction
• Any "important" optimizations the planner
  discovers are passed down to the reactive
  controller
• The planners suggestions are used if they are
  possible and safe
• Who has priority, planner or reactor?

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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

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Selection Example AuRA

• R. Arkin (1986)
• Planning is viewed as configuration
• Initial A planner integrated with schema-based
  controller
• Provides modularity, flexibility, and
  adaptability

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AuRA Schematic
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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

Rocky 4
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Atlantis Schematic
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Adaptation Example Planner-Reactor

• D. Lyons (1992)
• Continuous modification of a reactive control
  system
• 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 a particular underlying mathematical model
  called a process algebra
• Tested in both assembly cell and grasp planning

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Planner-Reactor Architecture
GOALS
REACTOR
PLANNER
WORLD
ADAPTION
ACTION
REACTIONS
PERCEPTIONS
SENSING
PERCEPTION
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Postponing Example PRS

• PRS Procedural Reasoning System
• Georgeff and A. Lansky (1987)
• Least commitment via plan elaboration
  postponement
• Tested on SRI Flakey

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Flakey the robot
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PRS Schematic
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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

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SSS Implementation T J
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More Examples

• SOMASS 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)

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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

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Still More Examples

• Theoagent
• T. Mitchell (CMU, 1990)
• Reacts when it can plans when it must
• Emphasis on learning
• Generic Robot Architecture
• Noreils and Chatila (1995, France)
• 3 levels planning, control system, functional
• Dynamical Systems Approach
• Schoner and Dose (1992)
• Planning is selecting and parameterizing
  behavioral fields
• Behaviors use vector summation

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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)

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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., multi-robot systems) and more
  practical applications

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Textbook Readings

• MM 13, 15