What Robotics Can Teach Us

WHAT BEHAVIOUR-BASED ROBOTS
CAN TEACH US ABOUT WIRELESS
SENSOR/ACTUATOR NETWORKS
Chris Holgate ([email protected])
Zynaptic Limited Lancashire Digital Technology Centre Burnley BB10 2TP
SOME RELEVANT BACKGROUND
• 
Investigated ‘Adaptive state machines for use in unsupervised distributed learning systems’ (Postgrad research at Imperial) • 
Paid off some debts by designing chips
• 
Worked on low power wireless network implementation for pioneering IoT company (AlertMe incarnation 1.0)
• 
Now aQempting to combine distributed machine learning and IoT (Zynaptic Ltd) TWO POPULAR IOT FALLACIES
• Fallacy: All data is valuable – make money by Data Is The New Oil collecting and hoarding as much of it as possible
• Reality: Comparing crude oil to raw data – the value (Clive Humby, 2006)
must be extracted through processing & refinement IoT Leads To Skynet
(John Connor, The Future)
• Fallacy: If you connect enough devices to the Internet you will get emergent intelligence
• Reality: If you want IoT devices to behave in an intelligent way you need to design it in
A BUSINESS LESSON FROM 1998
• 
Irrational ‘fear of missing out’ based investment
• 
Even more irrational exit valuations (greater fools)
• 
Dotcoms valued by number of eyeballs or other spurious metrics
• 
Business plans wriQen by the infamous South Park Underpants Gnomes
BUT IT’S DIFFERENT THIS TIME!
Phase 1 Collect Underpants Data
Phase 2 Big Data ???
Phase 3 PROFIT!
SMART GNOME SYSTEM ARCHITECTURE
Big Data ???
Cloud Database
Internet Gateway
Wireless Network
2015 IoT Smart Gnome
Sensor Data Fusion
Sensor Devices
A STANDARD SOMEONE MADE EARLIER
2015 IoT Smart Gnome
Big Data ???
Mission Planning ???
Cloud Database
Task Decomposition
Internet Gateway
Task Sequencing
Wireless Network
Elemental Move
Sensor Data Fusion
Sensor Devices
1989 NASRAM Standard
Motion Primitives
Sensor / Servo
THE SYMBOL GROUNDING PROBLEM
• 
In Cognitive Science, the problem of connecting abstract symbols to the entities they refer to • 
For Robotics, the problem of maintaining an internal symbolic representation which accurately represents the real world • 
Breaks the assumption that GOFAI planning algorithms can easily be ‘plugged in’ to the top of the architectural stack
• 
To paraphrase: “The best laid schemes of robots and algorithms often go awry”
INTELLIGENCE WITHOUT REPRESENTATION *
• 
Inspired by original work on cybernetics from the 1950’s
• 
Simple control systems acting in complex environments
• 
Analogue ‘nerve cell’ feedback loops
• 
Typified by W. Grey Walter’s Machina Speculatrix
• 
Popularised as behaviour based robotics in the 1990’s
• 
Layered behaviours for more sophisticated interaction
• 
Subsumption architecture design (Rodney Brooks et al)
• 
Typified by Roomba cat transportation robots
* Rodney Brooks, Intelligence Without Representation, Artificial Intelligence 47 (1991), 139–159
THE SUBSUMPTION ARCHITECTURE
• 
Situated action uses the world as its own model
• 
Cheap and noisy sensors and actuators in the loop
• 
Built using networks of augmented state machines • 
Hierarchy of behaviours (simple to more complex)
• 
Incremental design for ‘minimum viable robots’
• 
Action selection by suppression or inhibition
• 
Sensor data is used locally and discarded
• 
Avoids symbol grounding by not using symbols!
WHAT HAPPENS IF…
…the state machines were connected via a wireless network? …we put the sensors and actuators in different locations?
…humans could be incorporated into the control loop?
…we could embed machine learning in the state machines?
IMPLICATIONS OF WIRELESS NETWORKS
• 
Need to deal with lossy, unreliable data transmission
• 
No resources available for ‘reliable’ protocols (eg TCP)
• 
Architecture must be able to cope with dropped messages
• 
Unreliability rules out event driven communication
• 
Solved by using timestamped state value propagation
• 
• 
Regular messages convey current state values • 
Extra notification messages on critical value changes
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Timestamp critical value changes to ensure timeliness
Processed asynchronously on state value changes RETHINKING SENSORS AND ACTUATORS
Deploying To Multiple Locations
PuQing Humans In The Loop
• 
Requires a new concept of ‘situatedness’ for the system
• 
Can act as ‘sensors’ by turning observations into data input
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Not just situated in an environment, but part of it
• 
Can work as ‘actuators’ using information to drive actions
• 
Provides scope for extension by adding sensors and actuators
• 
Observations and actions will be noisy and unpredictable
• 
Potential to scale out using multiple interacting systems
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Ties in with existing work on human computer interaction
WHAT ABOUT MACHINE LEARNING?
In the world of neural computing, this approach is seen as being somewhat unorthodox*
But that’s a whole new talk…
* Igor Aleksander & Helen Morton, An Introduction To Neural Computing, Chapman & Hall (1990)
IN CONCLUSION…
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Collecting data for the sake of it is not good business
• 
Most IoT platforms are architected for data aggregation
• 
Some similarities between hierarchical IoT and robotics
• 
Planning based on ‘big data’ might not reflect reality
• 
Behaviour based robotics sidesteps symbol grounding
• 
Distributed architecture maps well to sensor networks
…most robots are now hybrid systems.
WHAT BEHAVIOUR-BASED ROBOTS
CAN TEACH US ABOUT WIRELESS
SENSOR/ACTUATOR NETWORKS
Chris Holgate ([email protected])
Zynaptic Limited Lancashire Digital Technology Centre Burnley BB10 2TP