1. Trends in Robotics Research
Classical AI Robotics (mid-70’s)
Sense-Plan-Act
Complex world model and reasoning
Indoor, wheeled, static blocks world
Reactive Paradigm (mid-80’s)
No models: “the world is the model”
Simple sense-act functions
Emergent behavior
Static legged motion,
robot swarms,
reactive
Complex environments,
mapping and localization,
human-robot interactions
Hybrid Architectures (90’s)
Models at higher levels, reactive at lower levels
Mid-level executive to sequence actions
Challenging outdoor environments
Air, water vehicles
Dynamic legged motion
Probabilistic Methods (mid-90’s)
Uncertain sensing and acting
Integration of models, sensing, acting
[Again – Thanks to Steffen Gutmann for many slides]

2. Classic AI Robotics Shakey (1967) at SRI – Rosen, Nilsson, Hart
First AI Robot
Foundational study: reason about the world, e.g., “block a doorway”
How do you represent the environment?
How do you plan to change the environment?
Set of predicates describing the world
AT(Box1, (32, 11)) ON(Box2, Box1)
Rules among the predicates (predicate logic)
Operators describing how actions affect the world
=> STRIPS planner
9/21/2018
CS225B Kurt Konolige

3. Sense-Plan-Act Paradigm
Architecture:
Sense
Plan
Act
STRIPS
Exec
ILUs DB
Exec was in charge
ILUs were reactive
Opportunistic use of plans
Replanning
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CS225B Kurt Konolige

4. Shakey’69
Stanford Research Institute
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5. Stanford CART ‘73 Stanford AI Laboratory / CMU (Moravec) 9/21/2018
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6. Classical Paradigm - Stanford Cart
Take nine images of the environment, identify interesting points in one image, and use other images to obtain depth estimates
Integrate information into global world model
Correlate images with previous image set to estimate robot motion
On basis of desired motion, estimated motion, and current estimate of environment, determine direction in which to move
Execute the motion
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CS225B Kurt Konolige

7. Classical Paradigm as Horizontal/Functional Decomposition
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8. Classical Paradigm as Horizontal/Functional Decomposition
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9. Behavioral Paradigm Vertical vs. Horizontal Decomposition
Reaction to perceived inadequacies of the SPA paradigm
Brooks, Arkin, Payton
Radical change: use a short Sense-Act Cycle
Many different incarnations
Subsumption (Brooks, Connell, …)
Potential Fields, Motor Schemas (Arkin, Gat)
Rule-based (Saffiotti, Ruspini, Konolige)
Circuits (Gat, Rosenschein and Kaelbling-Pack)
Biological Inspiration
No complex data structures
No complex sensory processing
Sense
Act
Vertical vs. Horizontal Decomposition
9/21/2018
CS225B Kurt Konolige

10. Reactive Paradigm as Vertical Decomposition
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11. Behavioral Paradigm: Tenets
Swarm robots:
Robots are situated
No abstract thinking
Interpretation of robot state depends on environment
Behavior-based programming, emergent behaviors
No hierarchical controller
Distributed, concurrent behaviors
Behavior-specific sensing
Quick and dirty (e.g., seagull chicks)
Genghis:
Sense
Act
How do behaviors combine?
9/21/2018
CS225B Kurt Konolige

12. Motor Schema Direct mapping from the environment to a control signal
obstacle-avoiding behavior
goal-seeking behavior
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13. Motor Schema path taken by a robot controlled by the resulting field
vector sum of the avoid and goal motor schemas
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14. Behavior Design Behavior design is more an art than a science
In what situation does the behavior apply?
What is the result of the behavior?
Easy to program?
Robustness?
Scalability?
Good behaviors produce smoothly varying control signals
Control signals that oscillate or otherwise jump around lead to poor control performance
Emergent behavior is difficult to predict
9/21/2018
CS225B Kurt Konolige

15. Project 1: Wall Following
Find a wall to travel along
Use right-hand rule: keep wall on the right
Keep a short distance from the wall, going parallel to it
NOTE: *must* interpret LRF readings by finding “wall” features
Follow along inside and outside bends
Go through reasonable openings (> 1m)
Suggestions:
Use behaviors for different situations: along wall, far from wall, at inside corner, etc.
Debug them separately
Invoke behaviors based on the situation
Use heading control rather than separate wheel velocities
9/21/2018
CS225B Kurt Konolige

16. Complex Control Architectures
Task: there are three robots to deliver six packages to four people.
Question: how much force should Robot 1 apply to its left wheel?
state -> a1, a2 ... an
Multi-robot
coordination
Planner
Sequencer
Behaviors
Hybrid
Architectures
Faster
More
Capable
SSS Connell
Motor Schemas Arkin
RAPS Firby
Saphira Konolige et al
..\..\..\writings\talks\videos\flakey etc\flakey-sap.avi
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17. Final Project (Fall 2001)
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18. QRIO’s Navigation Architecture
Each module runs in own thread
Message passing between modules
Aperios/OPEN-R real-time system
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CS225B Kurt Konolige

19. Environment Classification
6 different types:
Floor
Stairs
Border
Tunnel
Obstacle
Unknown
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20. Configuration and Modularity
Only enabled actions are allowed when expanding a node during path search
Motion behavior is selected based on the types of cells on the path and on the path direction as reported by the path planner.
9/21/2018
CS225B Kurt Konolige

21. QRIO autonomously navigates on an obstacle course (IJCAI-2005)
Experiments
narrow
obstacles
Stairs (2 x 3cm)
Table (35 cm)
QRIO autonomously navigates on an obstacle course (IJCAI-2005)

22. Video