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SNOW: AUTONOMY IN THE OPEN WORLD
BY JORIS SIJS
Joris Sijs
Electrical Engineering TU Eindhoven
MSc in Systems and Control
PhD in State estimation (Kalman filtering)
Visiting researcher
University of Karlsruhe
TU Delft
Scientist at TNO
Image processing
Sensor networks
Autonomous robotics
SHORT BIO
Autonomous, robotic systems
Part of a team
Real world
General tasking
A robot that is able to
conduct a part of the
operation autonomously
Navigation
Exploration
Surveying
WHAT ARE THEY TASKED TO DO
PURPOSE
Automated, robotic systems
Stand-alone
Prepped surrounding
Repetative motion
A robot that is able to repeat
the same task over and over
Production
“Inspection”
To decrease manpower and maintain or increase performance
Extend endurance
Extend capabilities
Replace human operation
To keep people safe
Less physical harm and mental stress
Less risky situation entering
Solve ‘unsolvable’ dangers (e.g. nuclear threat)
To operate remotely
Extend or cope with communication limits
Operations in remote environments; expertise at a distance
WHY WOULD A ROBOT BE A SOLUTION
NEEDS
FROM AUTOMATED IN A CLOSED WORLD
TO AUTONOMOUS IN AN OPEN WORLD
SNO
from closed & prepped world to open and real world
How do robotic systems know how to respond/behave in the real, open world?
FROM AUTOMATED IN A CLOSED WORLD
TO AUTONOMOUS IN AN OPEN WORLD
SNO
from closed & prepped world to open and real world
How do robotic systems know how to respond/behave in the real, open world?
WHICH METHODS TO KNOW
SOME CAPABILITIES FOR A ROBOT
Interaction
Cognition
Simulation Engineering
Perception
Navigation
Scene graph &
Scene assessment
Motion Planning
Localisation
Mapping (SLAM)
Verification & Validation
Design & Implementation
Knowledge Eng.
Operational post
Legal directions
Edge computing
Human Machine Language
System-system Collaboration
Human Machine Delegation
15-02-2022
7
Governance
Platform
Obejct Manipulation
Knowledge Discovery
Mission Planning / Task Execution
Active learning / Hypothesis testing
Self-Management
Actuators Sensors
Communications
Detection/ Recognition/ Tracking
Active Perception / Hypothesizing
Model-Based Engineering
Self-assessment
Env. and object
assessment
World (knowledge) modelling
System (knowledge) modelling
Telepresence
Immersive reality (Social) XR
Scene Management
Env. & object
Management
Digital Twin
ROS
Semantic Navigation
Fundamental R
Mature (partially)
Practical R&D
System integration
Transparency
HARDWARE FOR THE REAL WORLD
HARDWARE FOR THE REAL WORLD
Open world means
Being sub-confident
Encountering novelties
… and cope with them
UNCERTAIN AND UNKNOWN OBSERVATIONS
REAL WORLD: CAMERA IMAGES
pole
pole
zebrapad
zebrapad
zebrapad
truck truck
road
road
person
person
person
person
person
person
person
person
person
person
sidewalk
???
Open world means
Being sub-confident
Encountering novelties
… and cope with them
UNCERTAIN AND UNKNOWN OBSERVATIONS
REAL WORLD: MICROPHONE RECORDINGS
Ideal versus on Robot
Tough conditions…
REAL WORLD USE-CASE
SNOW
2020
SNOW
2021
~ ~
locate & identify
locate & assess
Newly found room (level 5)
change in the problem itself
Door that is blocked (level 4)
change in relations between rooms
Person with particular clothing (level 2)
change in features of an object
New conditions in a room (level 4)
change in the expected performance
Person that needs rescue (level 3)
change in relations between person and ladder
Detected New Human
REAL WORLD BOOLEAN?
Novelty level 0: update instances
Automatically update attributes, such as Position
EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
Novelty level 0: update instances
Automatically update attributes, such as Position
Novelty level 1: re-use classes
Instantiate a new Human (class) in case of a newly detected victim
Instantiate and remove relations as Well-Being and Located (Possibly, NotPossibly, Actually)
EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
Novelty level 0: update instances
Automatically update attributes, such as Position
Novelty level 1: re-use classes
Instantiate a new Human (class) in case of a newly detected victim
Instantiate and remove relations as Well-Being and Located (Possibly, NotPossibly, Actually)
Novelty level 2: change in feature not previously relevant
In case Human#i is identified (as George), all information of Human#i is transferred to George
Novelty level 3: change how entities and features are specified
The position of a Door is relative to the Origin of the Room that Snowboy is located
EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
Recall the detection of a human in the living
REAL WORLD BOOLEAN?
Robot in
LivingRoom
GoTo next
waypoint
Get
Image
Analyse
Image
Final
waypoint?
no
Terminate
Behavior
yes
When:
Robot in Room X
Human Y in Image
Then
Human Y in Room X
The robot is not always succeeding and makes mistakes
REAL WORLD IS PROBABILISTIC
PROCESS PROBABILISTIC INFORMATION
Approach & Identify (fail)
Needs rescue
(person_A)?
Question
OR
AtBottom
(person_A, Ladder)?
LayingFaceDown
(person_A)?
AND
person_A
Ladder
OnFloor
(PersonA)
Evidence = NewQuestion
Evidence
Person
Knowledge
(common sense)
Ladder
Floor
Now what?
PROCESS PROBABILISTIC INFORMATION
Markov Logic Networks into TypeDB
man phone
Far_a
way
object
object
man
family
father
weight
When:
x isa man
y isa phone
(object: Y, object: X)
Far_away
Then
(x: father) isa family
PROCESS PROBABILISTIC INFORMATION
Markov Logic Networks into TypeDB
^
Logic
weight
conclusion
statement
schema
statement
man phone
#objectx
#objecty
Far_a
way
object object
family
father
weight
conclusion
statement
statement
instance
schema
PROCESS PROBABILISTIC INFORMATION
Markov Logic Networks into TypeDB
person
#objectx
ladder
#objecty
At_bot
tom
high
low
Well_
being
being
pose
laying
health
Physical
_state
fallen
health
Color:
green ^
weight
statement
SG_la
ying
node
node
#object
statement
statement
statement
statement
SG_fal
len
node
#object
node
conclusion
When
x isa person
y isa ladder
y has color ‘’green’’
(x,y) isa at_bottom
subnetwork(laying)
Then
fallen(x)
PROCESS PROBABILISTIC INFORMATION
Markov Logic Networks into TypeDB
prob
prob
prob
prob
prob prob
prob
Pthyon & TypeQL
Extract statement
Transform to MLN
Run PracMLN
Read result
Write to database
Autonomous robot that operate in the real world
Have to assume open world
Evidences from the real world are uncertain
Most concepts are unknown
Uncertainties and unknowns cause interventions by operator
We try to reduce such interventions
But we need probabilistic reasoning
Markov Logic Networks
Bayesian Networks
…
SUMMARY:
1
2
4
3
1
2
4
3
5
2020
2021
2022
YOUR TIME
THANK YOU FOR

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Open World Robotics

  • 1. SNOW: AUTONOMY IN THE OPEN WORLD BY JORIS SIJS
  • 2. Joris Sijs Electrical Engineering TU Eindhoven MSc in Systems and Control PhD in State estimation (Kalman filtering) Visiting researcher University of Karlsruhe TU Delft Scientist at TNO Image processing Sensor networks Autonomous robotics SHORT BIO
  • 3. Autonomous, robotic systems Part of a team Real world General tasking A robot that is able to conduct a part of the operation autonomously Navigation Exploration Surveying WHAT ARE THEY TASKED TO DO PURPOSE Automated, robotic systems Stand-alone Prepped surrounding Repetative motion A robot that is able to repeat the same task over and over Production “Inspection”
  • 4. To decrease manpower and maintain or increase performance Extend endurance Extend capabilities Replace human operation To keep people safe Less physical harm and mental stress Less risky situation entering Solve ‘unsolvable’ dangers (e.g. nuclear threat) To operate remotely Extend or cope with communication limits Operations in remote environments; expertise at a distance WHY WOULD A ROBOT BE A SOLUTION NEEDS
  • 5. FROM AUTOMATED IN A CLOSED WORLD TO AUTONOMOUS IN AN OPEN WORLD SNO from closed & prepped world to open and real world How do robotic systems know how to respond/behave in the real, open world?
  • 6. FROM AUTOMATED IN A CLOSED WORLD TO AUTONOMOUS IN AN OPEN WORLD SNO from closed & prepped world to open and real world How do robotic systems know how to respond/behave in the real, open world?
  • 7. WHICH METHODS TO KNOW SOME CAPABILITIES FOR A ROBOT Interaction Cognition Simulation Engineering Perception Navigation Scene graph & Scene assessment Motion Planning Localisation Mapping (SLAM) Verification & Validation Design & Implementation Knowledge Eng. Operational post Legal directions Edge computing Human Machine Language System-system Collaboration Human Machine Delegation 15-02-2022 7 Governance Platform Obejct Manipulation Knowledge Discovery Mission Planning / Task Execution Active learning / Hypothesis testing Self-Management Actuators Sensors Communications Detection/ Recognition/ Tracking Active Perception / Hypothesizing Model-Based Engineering Self-assessment Env. and object assessment World (knowledge) modelling System (knowledge) modelling Telepresence Immersive reality (Social) XR Scene Management Env. & object Management Digital Twin ROS Semantic Navigation Fundamental R Mature (partially) Practical R&D System integration Transparency
  • 8. HARDWARE FOR THE REAL WORLD
  • 9. HARDWARE FOR THE REAL WORLD
  • 10. Open world means Being sub-confident Encountering novelties … and cope with them UNCERTAIN AND UNKNOWN OBSERVATIONS REAL WORLD: CAMERA IMAGES pole pole zebrapad zebrapad zebrapad truck truck road road person person person person person person person person person person sidewalk ???
  • 11. Open world means Being sub-confident Encountering novelties … and cope with them UNCERTAIN AND UNKNOWN OBSERVATIONS REAL WORLD: MICROPHONE RECORDINGS Ideal versus on Robot Tough conditions…
  • 12. REAL WORLD USE-CASE SNOW 2020 SNOW 2021 ~ ~ locate & identify locate & assess Newly found room (level 5) change in the problem itself Door that is blocked (level 4) change in relations between rooms Person with particular clothing (level 2) change in features of an object New conditions in a room (level 4) change in the expected performance Person that needs rescue (level 3) change in relations between person and ladder
  • 13. Detected New Human REAL WORLD BOOLEAN?
  • 14. Novelty level 0: update instances Automatically update attributes, such as Position EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
  • 15. Novelty level 0: update instances Automatically update attributes, such as Position Novelty level 1: re-use classes Instantiate a new Human (class) in case of a newly detected victim Instantiate and remove relations as Well-Being and Located (Possibly, NotPossibly, Actually) EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
  • 16. Novelty level 0: update instances Automatically update attributes, such as Position Novelty level 1: re-use classes Instantiate a new Human (class) in case of a newly detected victim Instantiate and remove relations as Well-Being and Located (Possibly, NotPossibly, Actually) Novelty level 2: change in feature not previously relevant In case Human#i is identified (as George), all information of Human#i is transferred to George Novelty level 3: change how entities and features are specified The position of a Door is relative to the Origin of the Room that Snowboy is located EVAL. AUTONOMY: ENVIRONMENTAL COMPLEXITY SNO
  • 17. Recall the detection of a human in the living REAL WORLD BOOLEAN? Robot in LivingRoom GoTo next waypoint Get Image Analyse Image Final waypoint? no Terminate Behavior yes When: Robot in Room X Human Y in Image Then Human Y in Room X
  • 18. The robot is not always succeeding and makes mistakes REAL WORLD IS PROBABILISTIC
  • 19. PROCESS PROBABILISTIC INFORMATION Approach & Identify (fail) Needs rescue (person_A)? Question OR AtBottom (person_A, Ladder)? LayingFaceDown (person_A)? AND person_A Ladder OnFloor (PersonA) Evidence = NewQuestion Evidence Person Knowledge (common sense) Ladder Floor Now what?
  • 20. PROCESS PROBABILISTIC INFORMATION Markov Logic Networks into TypeDB man phone Far_a way object object man family father weight When: x isa man y isa phone (object: Y, object: X) Far_away Then (x: father) isa family
  • 21. PROCESS PROBABILISTIC INFORMATION Markov Logic Networks into TypeDB ^ Logic weight conclusion statement schema statement man phone #objectx #objecty Far_a way object object family father weight conclusion statement statement instance schema
  • 22. PROCESS PROBABILISTIC INFORMATION Markov Logic Networks into TypeDB person #objectx ladder #objecty At_bot tom high low Well_ being being pose laying health Physical _state fallen health Color: green ^ weight statement SG_la ying node node #object statement statement statement statement SG_fal len node #object node conclusion When x isa person y isa ladder y has color ‘’green’’ (x,y) isa at_bottom subnetwork(laying) Then fallen(x)
  • 23. PROCESS PROBABILISTIC INFORMATION Markov Logic Networks into TypeDB prob prob prob prob prob prob prob Pthyon & TypeQL Extract statement Transform to MLN Run PracMLN Read result Write to database
  • 24. Autonomous robot that operate in the real world Have to assume open world Evidences from the real world are uncertain Most concepts are unknown Uncertainties and unknowns cause interventions by operator We try to reduce such interventions But we need probabilistic reasoning Markov Logic Networks Bayesian Networks … SUMMARY: 1 2 4 3 1 2 4 3 5 2020 2021 2022