Multi-Robot Teams Separating Teamwork from Taskwork
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Multi-Robot Teams Separating Teamwork from Taskwork Gal A. Kaminka The MAVERICK Group Computer Science Department Bar Ilan University, Israel
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Multi-Robot Teams Separating Teamwork from Taskwork. Gal A. Kaminka The MAVERICK Group Computer Science Department Bar Ilan University, Israel. Teams of Robots and Agents. Rescue, disaster management Coordinated search Mapping Exploration Space and science applications Satellite formations - PowerPoint PPT Presentation
Transcript of Multi-Robot Teams Separating Teamwork from Taskwork
Multi-Robot TeamsSeparating Teamwork from Taskwork
Gal A. Kaminka
The MAVERICK GroupComputer Science Department
Bar Ilan University, Israel
Teams of Robots and Agents
Rescue, disaster management Coordinated search Mapping
Exploration Space and science applications Satellite formations
Cleaning Vacuum cleaning
Surveillance, Security Defense, civilian Hazardous materials
Sensor, ad-hoc networks Agriculture
Plowing Medical applications
Hospital deliveries
10-15 years ago: Application-specific solutions
Then, 10-15 Years ago…
Philosophy, Computer Science Theory Joint intentions (Cohen & Levesque 1990, 1991) SharedPlans (Sidner & Grosz 1986, Grosz & Kraus 1996)
Systems needing automated collaboration Robot foraging, coordinated movement (Parker 1993, 1998) Simulated helicopter teams (Tambe 1996)
Team = Teamwork + Taskwork
Soccer Taskwork: Kicking to a target Turning, tackling Tracking the ball, goal …
Soccer Teamwork: Allocating players to roles Synchronizing tactics Sharing relevant information …….
Teamwork and Taskwork
MAVERICK Research in teamwork(See elsewhere for plan recognition and social cognition)
Task-Specific Teamwork: Foraging (AIJ 07, ICRA 10)
Coverage (ICRA 05,06,…)
Passing (FSS 01)
Scheduling (SSS 06)
Coordinated movement (AAAI 05,ICRA 06,JOPHA 09)
Patrolling (ICRA 07,AAMAS 08,IJCAI 09)
Multi-robot HRI (ICRA 06)
….
General Teamwork: Monitoring (JAIR 00,02, JAAMAS,
AAMAS 07,ICRA 10)
Fault diagnosis (AAAI 98, IJCAI 03, AAAI 05, AAAI 06)
Synchronization (AAAI 05, ICRA 07)
Allocation (AAMAS 03, AAAI 05, AAMAS 07)
Learning, adaptation (DARS 06) Maintenance (AAMAS 07)
….
MAVERICK Research in teamwork(See elsewhere for plan recognition and social cognition)
Task-Specific Teamwork: Foraging (AIJ 07, ICRA 10)
Coverage (ICRA 05,06,…)
Passing (FSS 01)
Scheduling (SSS 06)
Coordinated movement (AAAI 05,ICRA 06,JOPHA 09)
Patrolling (ICRA 07,AAMAS 08,IJCAI 09)
Multi-robot HRI (ICRA 06)
….
General Teamwork: Monitoring (JAIR 00,02, JAAMAS,
AAMAS 07,ICRA 10)
Fault diagnosis (AAAI 98, IJCAI 03, AAAI 05, AAAI 06)
Synchronization (AAAI 05, ICRA 07)
Allocation (AAMAS 03, AAAI 05, AAMAS 07)
Learning, adaptation (DARS 06) Maintenance (AAMAS 07)
….
BDI robot control:A quick review
Each node is a behavior (a controller) Nodes arranged hierarchically (to decompose tasks)
Also sequentially
Every behavior Preconditions End conditions
Position
Find Leader
Ready Leader
Move
Lead
Move Formation
Follow
End
(a)
A behavior graph (a)
Teamwork?
Each robot executing own copy of recipe
No agreement Coordination,
but no collaboration
Principle #1:Synchronizing Plan Execution
Agents must achieve mutual belief with teammates Sidner & Grosz & Kraus 1986,1996; Cohen& Levesque 1990
Team behaviors: Start together, end together Jennings 1995, Tambe 96
Position
Find Leader
Ready Leader
Move
Lead
Move Formation
Follow
End
(a)
Principle #2:Allocate tasks to team-members
Teammates assist each other, take over when failure: Allocate tasks to members (Task Allocation)
e.g., Shehory & Kraus 1995, Dias et al. 2000.
Anticipate needs of teammates Proactive Communications (Yen et al. 2001)
Dynamically re-adjust team to address failures Parker 1998, Tambe 1996, 1997, Kumar et al. 2001
Principle #2:Allocate tasks to team-members
Teammates assist each other, take over when failure: Allocate tasks to members (Task Allocation)
e.g., Shehory & Kraus 1995, Dias et al. 2000.
Anticipate needs of teammates Proactive Communications (Yen et al. 2001)
Dynamically re-adjust team to address failures Parker 1998, Tambe 1996, 1997, Kumar et al. 2001
Organization hierarchy, allocated tasks
Team Hierarchy (b) (Tambe et al. 2000) Behavior Graph (a)
{X,Y,Z}
{X} {Y,Z}
(b)
Position
Find Leader
Ready Leader
Walk
Lead
Move Formation
Follow
End
(a)
Agents can find out with whom to communicate Agents also know who responsible for a behavior
Opportunities for interaction
Behavior graph offers opportunities for protocol use Conditions trigger synchronous selection Decomposition triggers allocation
Position
Find Leader
Ready Leader
Walk
Lead
Move Formation
Follow
End
SynchronizedStart Synchronized
End
Allocation
BITE: Bar Ilan Teamwork Engine (IAS 04, AAAI 05, ICRA 07, AAMAS 07)
Interaction protocols (behaviors) first-class objects Allow using different protocols at different times
Insead of e.g., always vote, always ask leader, …
Context-dependent coordination policies
BITE Components
Team Hierarchy (b)
Behavior Graph (a)
Interaction Behaviors (c)
{X,Y,Z}
{X} {Y,Z}
(b)
Wait
Announce Vote Tally
Ack. Readiness
CFP Bid Select
(c)
Position
Find Leader
Ready Leader
Walk
Lead
Move Formation
Follow
End
(a)
BITE Control Algorithm
Each robot has a copy of the three structures Each robot executes the following control loop
1. push initial behavior onto execution stack2. repeat
a. e current top behavior on stackb. if (e can be decomposed)
push ALLOCATE(children(e))c. else break
3. Execute stack until a behavior b terminates4. SYNC_TERM(b, children(b))5. e SYNC_SELECT(b, followers(b)), push e6. GOTO 2
Lesson: Flexibility matters (I)
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Configuration: Same behaviors, different protocols
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Patrolling
Adversarial(w/ Kraus, Agmon)
Frequency-Based(w/ Elmaliach)
Area Perimeter Fence (Border)
ICRA 07, AMAI 10 AAMAS 08 AAMAS 08,
AAMAS 09
IAS 08,
IJCAI 09
Patrolling
Adversarial(w/ Kraus, Agmon)
Frequency-Based(w/ Elmaliach)
Area Perimeter Fence (Border)
ICRA 07, AMAI 10 AAMAS 08 AAMAS 08,
AAMAS 09
IAS 08,
IJCAI 09
Multi-robot, single zoneSynchronized patrol
edcba
Theorem: Synchronized patrol is better.(response time is at most half-segment.)
Multi robots, multi zones (overlap):
Internal points visited by multiple robots Improved uniformity everywhere except for edges Low frequency in edges
Important for system design, placement of cameras
Responding to break-ins, faults
Detected by the robots or operator Affected robot is committed
Sends a message to others
Other re-allocate segments Maintain frequency (now reduced)
Many issues in practice: Intermittent minor stops vs. events Noise in the actual velocity of the robots Turning points, communication ranges
Principle #3:Organizational Maintenance
Teammates assist each other, take over when failure: Allocate tasks to members (Task Allocation)
e.g., Shehory & Kraus 1995, Dias et al. 2000.
Dynamically re-adjust team to address failures Parker 1998, Tambe 1996, 1997, Kumar et al. 2001
Anticipate needs of teammates Proactive Communications (Yen et al. 2001)
Task: Formation Maintenance
Separation-Bearing Formation Control (SBC):
One robot assigned to lead the group (leader) Others maintain distance and angle to peers (targets)
Each robot monitors single target
Monitoring rules imply a control graph Leader node: Out degree of 0 Other nodes: Out degree of 1 Connected, path from all to leader Proven stable (e.g., Fierro et al. 2001)
Target of 0
2
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LeaderTarget of 1Target of 3
Generating an Organizational Structure:
Control Graph Generation (AAAI 05, ICRA 06)
Key idea:
Generate a multi-graph containing all possible control-graphs
Then, induce a single control graph
Multi-graph:
• Vertices are robots in formation positions
• Directed edge (r1,r2) implies r1 can sense r2.
• Multiple edges between a pair of vertices allowed
– Correspond to multiple sensing options
– According to sensing morphology of robot
Sensing Costs: Setting edge weights
• A robot can have multiple sensors (edges)– Each sensor has range, field of view, pan angle– Cost associated with use to monitor target
• Edge weights correspond to sensor usage costs
• Induce minimum-cost organization:– Min weight path from each vertex to leader– Single target shortest paths (Dijkstra)
Re-organization: Dynamic Switching of Control Graphs
Called when robot (or more) loses its target Frequent failures update costs
Recovery algorithm updates control graph
Glick and Kaminka,ICRA 2006
Time spent in formation
Significantly higher percent of time (two-tailed t-test, p < 2.40109e−15)
Better
Reorganization
No Reorganization
Time to complete task
Significantly shorter duration in all cases(two-tailed t-test, p < 5e-13)
Better
No Reorganization
Reorganization
BITE Revisited
Common lessons Synchronized execution is better for task Balancing load among homogeneous robots Improved robustness, bug-free development
60% reduction in software engineering effort!
Is this it?
MAVERICK Research
Task-Specific Teamwork: Foraging (ECAI 04)
Coverage (ICRA 05,06)
Passing (FSS 01)
Scheduling (SSS 06)
Coordinated movement (AAAI 05, ICRA 06)
Patrolling (ICRA 07, 08)
….
General Teamwork: Monitoring (JAIR 00,02, JAAMAS,
AAMAS 07)
Fault diagnosis (AAAI 98, IJCAI 03, AAAI 05, AAAI 06)
Synchronization (AAAI 05, ICRA 07)
Allocation (AAMAS 03, AAAI 05, AAMAS 07)
Learning, adaptation (DARS 06) Maintenance (AAMAS 07)
….
Multi-Robot Coverage:Can we separate teamwork from taskwork?
Goal: Visit each point in an area Applications: Floor cleaning, mowing, de-mining, painting …. Many approaches, distinguishing properties:
Off-line/on-line, heuristic/complete, approximate/exact
Existing literature: Typical goal: Increase efficiency Often, no analytically proven robustness to catastrophic failures
Catastrophic: Complete robot failure Some systems show robustness empirically
MSTC: Multirobot Spanning-Tree Coverage(ICRA 2005, ICRA 2006, ICRA 2006, RAS 08, AMAI 09)
Family of multi-robot coverage algorithms Off-line, on-line Backtracking, non-backtracking Allow for heterogeneous robots, fuel/speed constraints
Common theme: Guaranteed Robustness
Will complete as long as one robot is alive
Spanning-Tree CoverageSingle robot [Gabriely and Rimon, 2001] Robots with coverage tool of size D Grid terrains, each cell of size D Switch to coarse grid, each cell of size 4D Draw Spanning Tree (ST) on the coarse grid Robot moves along ST path
A Hamiltonian cycle!
A
Non-backtracking MSTC
• Initialization phase: Build STC, distribute to robots
• Distributed execution: Each robot follows its section– Low risk of collisions
Robot A is done!
Robot B is done!
Robot C is done!
A
B
C
Coverage completed as long as one robot is alive Teamwork requirement:
Need to know task completion status of others
Guaranteed robustness
A
B
C
Analysis: Non-backtracking MSTC
• Running time = max i k step(i)
• Best case:
• Worst case: n – k– Unfortunately, common case
• General non-backtracking worst case: n - 2(k-1) - 1
1k
n
A
D
BC
A
D B
C
A
D
B
C
Backtracking MSTC
• Similar initialization phase
• Robots backtrack to assist others
• No point is covered more than twice
A
D
C
B
A
D
C
B
Backtracking MSTC analysis
• Best case: The same
• Worst case: k=2
k>2
1k
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D
BC
A
B
Generating a good spanning tree(believed to be NP-Hard)
AA
BB
CC
A B = 12 cells
B C = 12 cells
C A = 12 cells
A B = 28 cells
B C = 4 cells
C A = 4 cells
New (and in development):
Exploration for SaR, Clearing Team of 3-10 robots, 1-2 operators Fast exploration of single floor, indoor
Allow surveillance by human Provide approximate positions
Technology-transfer goal: An advanced prototype Buy COTS when possible Aim for platform-independence, standards-based Multiple deliverables (maximize impacts) Early involvement of customer, suppliers (ODF, Cogniteam)
Challenges in Exploration
Single human is THE monitor No autonomous sensor readings Means difficult to save exploration time
Real-time on small fast robots No quad-core laptops on top….
Ideal: Vision-only
Adaptive Coordination(Managing interdependence)ECAI 06, AIJ 07, ICRA 10 (Rosenfeld, Erusalimchik)
Foraging: Collect pucks, bring them home In nature:
Bees, ants Humans…
Many coordination methods: Arkin & Balch 1998 Rybski et al. 1998 Vaughan et al. 2002 ….
Adaptive Coordination
Track cost of coordination
Costs spent before, during, after collision
e.g., time spent avoiding, time spent moving away
Switch to cheaper methods (triggered by thresholds)
Rosenfeld et al.: Use offline of thresholds
Erusalimchik et al.: Online reinforcement learning
Converges to optimal! Long economics-based discussion of why (it was a surprise!)
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Adaptive Coordination Results
Lesson: Teamwork is about robustness
Imagine two cars driving one behind another If the cars are in a convoy (a team), then:
If one of them stops, other should stop or … Convoy leader uses signals, drives slowly, looks in mirror ….
As long as all goes well, no need for teamwork Simple spatial coordination is sufficient
Lesson:Reuse through architectures
Reuse in multiple applications New commercial tools (www.cogniteam.com)
Position
Find Leader
Ready Leader
Walk
Lead
Move Formation
Follow
End
SynchronizedStart Synchronized
End
Allocation
Challenges
From coverage, patrolling, exploration: Temporal synchronization is an invariant. Spatial task allocation: An Invariant? Run-time (can we compile into behavioral rules?)
From more complex tasks (RoboCup, exploration): Looser teamwork: Joining, retiring, asynchronous Proactive teamwork, anticipation, planning Role of plan-recognition within teamwork model
Other challenges
From working with industry (Cogniteam) Validation and verification, at least probabilistically Commercial significance of re-use, explicit teamwork
From HART: Where to publish, what to read? Cannot believe I missed Stefan’s work! ARMS (Autonomous Robots Workshop) @ AAMAS HART mailing list?
Thank you ([email protected])
Inna Frenkel
Asaf Shiloni
Thuc Vu
Ruti Glick
Noam Hazon
Noa Agmon-Segal
Avi Rosenfeld
Ari Yakir
Yehuda Elmaliach
Milind Tambe
Sarit Kraus
Manuela Veloso
Meir Kalech
Dan Erusalimchik
Tom Shpigelman
Meirav Hadad
Gilad Armon-Kest
Jared Go
Acknowledgments (subset)
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