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Inconsistencies in Models ofAdaptive Service Robots
Ivan Ruchkin *PhD Student in Software Engineering
Institute for Software ResearchCarnegie Mellon University
* with images from Jonathan Aldrich, Javier Cámara, and Bradley Schmerl
Software Research Seminar (SSSG)April 10, 2017
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Service robot missions
● Autonomy in the face of uncertainty● Timeliness, power, safety
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Centralized online adaptation
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Centralized online adaptation
Does it work?
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How to check if it works
Run the system
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How to check if it works
Run the system
Run the simulation
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How to check if it works
Run the system
Run the simulation
Analyze the models
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How to check if it works
Run the system
Run the simulation
Analyze the models
Analyze the code
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How to check if it works
Run the system
Run the simulation
Analyze the models
Analyze the code
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Plan for today
● Inconsistency 1: power● Inconsistency 2: turns● What is common? ● Tentative approach: detection
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Inconsistency 1: power in simulation vs hardware
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Inconsistency 1: power in simulation vs hardware
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Inconsistency 1: power in simulation vs hardware
Model source: hardware experiments
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Inconsistency 1: power in simulation vs hardware
Model source: hardware experiments
Simulation: fixed increments
Simulation
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Inconsistency 1: power in simulation vs hardware
Model source: hardware experiments
Simulation: fixed increments
Simulation
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Inconsistency 1: models
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Inconsistency 1: models
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Inconsistency 1: summary
● Hardware power predictions do not match the simulation– Initially optimistic, later pessimistic
● Obscured by mixing power dynamics– Mode switching
– CPU and sensors
● Definition: – What deviations are acceptable, over what time?
● Assurance: – Simulation, corner case tests
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Inconsistency 2: turning in instruction graphs vs planning
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Inconsistency 2: turning in instruction graphs vs planning
A
B
C
D
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Inconsistency 2: turning in instruction graphs vs planning
A
B
C
D
Instructiongraph A B C D
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Inconsistency 2: turning in instruction graphs vs planning
A
B
C
D
A Planning + cost(C)
B D
Instructiongraph A B C D
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Inconsistency 2: turning in instruction graphs vs planning
A
B
C
D
A Planning + cost(C)
B D
Instructiongraph A B C D
State estimation = ?
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Inconsistency 2: models
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Inconsistency 2: models
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● Planning and execution represent turning differently ● State estimation is uncertain during a turn
– If optimistic, can run out of power
– If pessimistic, can waste time on charging
● Definition: – Bound of utility loss from state estimation uncertainty
● Assurance: – Quantitative model checking
Inconsistency 2: summary
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What is common?
A
B D
A B C D
Power in simulation/hardware Turns in planning/execution
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Models: big picture
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Models: big picture
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Models: big picture
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What is common?
A
B D
A B C D
Power in simulation/hardware Turns in planning/execution
● Several models with differing assumptions● Definition: bounded mismatch● Checking requires multiple semantics
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Detecting Inconsistencies
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Detecting Inconsistencies
1. Relate models ● Manual
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Relating models
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Relating models
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Detecting Inconsistencies
1. Relate models ● Manual
2. Specify consistency properties● Manual
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Specifying consistency
“Predicted and simulated power always stay within a fixed error bound”
□ ∀p, t · p = PowPred(t) → □ ( time = t → | PowSim – p | < ε)
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Detecting Inconsistencies
1. Relate models ● Manual
2. Specify consistency properties● Manual
3. Find potential inconsistencies● Automated
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Finding potential inconsistencies
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Finding potential inconsistencies
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Detecting Inconsistencies
1. Relate models ● Manual
2. Specify consistency properties● Manual
3. Find potential inconsistencies● Automated
4. Check if the consistency properties hold ● Automated
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Checking properties
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Summary
Four-step approach: Relate – Specify – Find – Check
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