Emotion-Based Control of Cooperating Heterogeneous Mobile Robots

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University of Missouri Columbia

• Emotion-Based Control of Cooperating
  Heterogeneous Mobile Robots
• Robins R. Murphy, Christine L. Lisetti, Russell
  Tardiff, Liam Irish and Aaron Gage
• Presented by
• Ashwin Mohan
• Course Instructor
• Dr. Majorie Skubic
• April 27, 2005

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What does this paper talk about?

• First investigation a formal cognitive model of
  emotions
• choice of behavior is self-regulated for each
  agent
• Investigates the capacity of agents to
  distinguish and adapt based on emotions
• Investigates ability of robot to represent and
  learn knowledge using emotions to improve
  efficiency
• Investigates how emotions enable adaptation to
  harmful conditions
• Uses communication, awareness of and reaction to
  emotional stimuli to achieve interdependency

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Why interested in INTERDEPENDENCY?

• Multi-agent control for interdependent tasks
• Execution of a tightly coupled task with a
  cyclic dependency and one that cannot be
  performed by another robot
• Interdependency leads to the possibility of
  failure hardware, planning, environment, etc
• How can one enhance performance improvements in a
  dynamic and distributed system
• What kind of control mechanisms can be
  implemented on a heterogeneous team

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Competition, Interference and Deadlock

• Robots cooperating asynchronously on a sequential
  task can enter deadlock, where one robot does not
  fulfill its obligations in a timely manner
• Interference includes conflicts like goal
  clobbering, deadlocks and oscillations
• Resource competition can be over space,
  information, and objects
• Stagnation occurs when a team of robots work on a
  task but cease to make progress

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? Motivation for using Emotions ?

• One method of controlling multi-robot systems
• Help to dynamically adapt to limitations, manage
  social behavior, and to communicate with others
• At the implementation level, emotions monitor the
  accomplishment of the goals and corresponds to a
  formal cognitive model
• Emotional intelligence lead to robots capable of
  representing and learning affective knowledge
• Each emotion calls a distinctive suite of actions
  appropriate for that emotional state

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Related work and non-cognitive solutions

• Coherent framework for implementation of emotions
  in heterogeneous robots is missing
• Based on structure of behaviors, NO awareness of
  interaction
• All tasks were available to all robots
• Work focused on functions of emotions in social
  exchanges than efficiency
• Very few AI models have computer programs
• No explicit representation and awareness of and
  reaction to emotional stimulus
• No work done on interdependence in hybrid
  architectures

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Framework for this paper

• Leventhal and Scherer the hierarchical
  multilevel process theory of emotions
• The sensory-motor level is activated by external
  stimuli and internal changes. Reactions are
  mostly of short duration and reflex-like
• The schematic level integrates sensory-motor
  processes with prototypes of emotional situations
  having concrete representations
• The conceptual level is deliberative, involves
  reasoning over the past and projecting into the
  future to avoid emotional disturbances

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Approach using BSG and ESG

• Scripts used for assemblages of robotic
  behavioral schemas to represent stereotypical set
  of behaviors
• BSG and ESG accept measures of task progress as
  inputs
• Task progress metrics come from three sources
  monitors, individual behaviors, and inter-agent
  communication
• Monitors are perceptual schemas Individual
  behaviors often act as releasers for other
  behaviors, communication from an external agent,
  either a command (e.g.,hurry) or data (e.g.,
  Im at location )

Fig A Layout of a causal chain
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Approach (2)

• The use of emotions and ESG breaks the potential
  master/slave coupling
• Example
• Robot A receives a message Hurry
• Causes shift, say from Confident to Concerned
• ESG triggers adaptation to make better progress
• Robot may reduce the sensitivity to obstacles,
  change the acceleration of its motions to produce
  the change

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Multi Agent Implementation

• Waiter Script implements only schematic link
• Refiller Script implements only the sensory-motor
  level link between ESG and behaviors
• BSG and ESG represented as a single finite state
  machine

Fig B Basic organization of the reactive layer
of Sensor fusion effects (SFX)
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Problem setup

• Waiter robot whose task it is to serve items to
  an audience
• Refiller cannot perform the Waiters task and
  vice versa
• Two robots are distributed and decentralized
• Each robot uses WaiterScript or RefillerScript

• Refiller can substantially decrease the time on
  task, i.e., serving
• The tray of refills is the resource
• Emotions are used to adapt or change the team
  behavior
• Robots use wireless to communicate either a
  command or location data

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Problem setup (2)

• Fully autonomous Nomad 200 robot bases
• Both robots use the (SFX) hybrid deliberative/
  reactive architecture
• Both robots run under RedHat Linux version 3.0.3
  and are coded in a combination languages
• Emotional state was governed by the changing
  relationship of the rate of treat consumption,
  time till empty (TTE) to the time to be refilled
  (TTR)

• The Waiter (a.k.a. Butler) has sonar rings, laser
  ranger, a thermal probe, and dual Hitachi color
  video camera and controlled by on-board
  processors (233-MHz and 133-MHz MMX)
• The Refiller (a.k.a. Leguin) has one sonar ring,
  and dual Hitachi color video cameras on a
  pan-tilt head and has 233 and 66-MHz Pentium
  MMXs
• Knowledge Query and Manipulation Language (KQML)
  agent communication language

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Action tendency and Task progressmeasures

• Robots programmed with the happy, confident,
  concerned and frustrated emotional states which
  correspond to the action
• Two modifiers were used, caution, C, and
  patience, P, acting as thresholds
• The output of the emotions was at the schematic
  level, leading to changes in the set of active
  behaviors

Fig C Action Tendency
Fig D Task Progress Measures
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Waiter and WaiterScript

• One external input data about location of
  Refiller
• Six external outputs which are communicated to
  the Refiller
• Wait refill, produced by serve
• Hurry, intercept, go home generated when an
  emotional event (state change) occurs
• script is responsible for computing (TTR)
  (TTE), task progress measures and instantiating
  or modifying the set of active behaviors

Fig E WaiterScript
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Refiller and the RefillerScript

• One source of inputs from the Waiter as commands
  (wait, refill, hurry, go home, intercept) or
  position data
• Wait is when she loiters around the serving
  station
• Refill is a move-to-goal behavior where Waiter is
  the goal
• Hurry command increases her navigational speed to
  the maximum safe speed

Fig F RefillerScript
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Combined Behavior Diagrams
Fig G Representative data run showing emotions
and changes in internal and team behavior
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Combined Behavior Diagrams (2)

• WaiterScript begins with the serve behavior Happy
  and sends a command wait to the Refiller to
  synchronize
• Serve uses the sub-behavior face-find to track
  human faces plays sound encouraging to remove
  treats
• While serving, at t150 the Waiter robot may
  communicate a refill request if (TTE) is now
  less than (TTR), Confident that she will
  receive a refill in time
• Refill changes behavior from wait to refill
• Ideally, the Refiller reaches the Waiter
  triggering the exchange behavior
• At about 220s, Waiter goes into Concerned
  triggering hurry to Refiller

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Combined Behavior Diagrams (3)

• Insufficient progress of Refiller will cause
  emotional state of Frustrated
• ESG now dictates, if Butler should abandon serve
  and move to intercept
• During intercept, emotional state moves from
  Frustrated to concerned to happy
• Under exchange, the Waiter does nothing until the
  tray-watch monitor sees the operator flash the
  empty tray in front of the cameras
• When the tray was seen, the Waiter communicates a
  go home command to the Refiller.
• When exchange terminates, the WaiterScript
  re-instantiates serve

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Discussion of Results

• Demonstrated at AAAI Mobile Robot
  Competition,00, in Austin, TX, and at Museum of
  Science and Industry (MOSI), 2000
• Trace data collected at MOSI clearly showed that
  emotions led to dynamic adaptations, and changes
  in the robots behaviors
• Robots regulated their subgoals and motivations
  according to their own current internal emotional
  states as well as external
• signals
• They socially adapted their actions to the other
  agents, both human and artificial, depending on
  the current situation
• Emotional model provides number of features
  beyond a simple state machine
• Coding of emotions is simple and can be added to
  these systems without any re-conceptualization of
  components

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Discussion of Results

• Emotional implementation is local to each robot
  and based on task progress
• Robots need not interpret or understand each
  others emotions
• Sensory-motor stimuli (e.g., seeing a tray
  full/empty) gave rise to simple reflex-like
  reactions involving the motor system only
• Emotions suited for control of distributed,
  behavior-based systems where centralized,
  deliberative methods are often too
  computationally expensive
• The implementation reported in this paper is
  appropriate for behavior-based and hybrid
  deliberative/reactive robots
• Partial translation of the multilevel process
  theory of emotions
• Results offers support that this multilevel
  theory is a useful model of emotions

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• Thank you!