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CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces (AAMAS'22)

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces (AAMAS'22)

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces (AAMAS'22)

Keisuke Okumura**, Ryo Yonetani*, Mai Nishimura*, Asako Kanezaki**
* OMRON SINIC X | ** Tokyo Institute of Technology
Presented at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2022

OMRON SINIC X

May 09, 2022
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  1. CTRMs: Learning to Construct Cooperative Timed
    Roadmaps for Multi-agent Path Planning in
    Continuous Spaces (AAMAS 2022)
    Keisuke Okumura**, Ryo Yonetani*, Mai Nishimura*, and Asako Kanezaki**
    * OMRON SINIC X | ** Tokyo Institute of Technology
    Presented at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2022
    May 9-13, 2022
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  2. 1Tokyo Institute of Technology 2OMRON SINIC X
    Keisuke Okumura1, Ryo Yonetani2, Mai Nishimura2 & Asako Kanezaki1
    AAMAS-22, Online, May 9th–13th 2022
    CTRMs:
    Learning to Construct Cooperative Timed Roadmaps
    for Multi-agent Path Planning in Continuous Spaces
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  3. /30
    2
    Background
    collision-free path planning for multiple robots
    real-time, scalable, and high-quality
    recent progress of multi-agent pathfinding (MAPF)
    for discretized environments
    https://automation.omron.com/en/us/industries/logistics/
    i.e., roadmaps
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  4. /30
    3
    Roadmap design affects solution quality
    on non-deliberative
    discretized spaces ideal paths
    Making roadmaps denser help situations?
    goal
    start
    often used in MAPF studies
    [Stern+ SOCS-19]
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  5. /30
    4
    dense sparse
    large small
    planning effort
    high low
    solution quality
    big impact in multi-agent cases
    ideal: small search space containing high-quality solutions
    No, there is a trade-off
    *produced by PRM [Kavraki+ 1996]
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  6. /30
    5
    constructing small roadmaps
    containing high-quality solutions
    multi-agent path planning in continuous spaces
    solving multi-agent pathfinding efficiently
    1. agent-specific 2. cooperative 3. timed
    CTRMs: cooperative timed roadmaps
    Our Approach
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  7. /30
    6
    How to Construct CTRMs?
    casting as a machine learning problem
    from planning demonstraitons,
    learning important regions of each agent and interactions between agents
    agent-specific cooperative
    using the trained model to construct timed roadmaps
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  8. /30
    7
    Outline of Approach
    new instance
    𝐹!"#$
    random
    walk
    sampling module
    next
    locations
    starts
    path generation
    compositing
    solution
    MAPF
    algorithm

    t=0 t=1 t=2
    CTRMs
    𝐹!"#$
    model training
    instances & solutions predict next locations
    Online Inference
    Offline Training
    CVAE: Conditional Variational Autoencoder
    [Sohn+ NeurIPS-15]
    +importance sampling
    [Salzmann+ ECCV-20]
    +multi-agent attention
    [Hoshen NeurIPS-17]
    𝐹!"#$
    :
    *independent from map size and #agent, not limited to homo-agents
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  9. /30
    8
    Outline of Approach
    new instance
    𝐹!"#$
    random
    walk
    sampling module
    next
    locations
    starts
    path generation
    compositing
    solution
    MAPF
    algorithm

    t=0 t=1 t=2
    CTRMs
    𝐹!"#$
    model training
    instances & solutions predict next locations
    Online Inference
    Offline Training
    CVAE: Conditional Variational Autoencoder
    [Sohn+ NeurIPS-15]
    +importance sampling
    [Salzmann+ ECCV-20]
    +multi-agent attention
    [Hoshen NeurIPS-17]
    𝐹!"#$
    :
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  10. /30
    9
    𝐹!"#$
    next position
    Offline Training & Model Arch.
    instance & solution generative
    model
    collected by intensive computation
    with conventional roadmaps
    CVAE [Sohn+ NeurIPS-15]
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  11. /30
    10
    Offline Training & Model Arch.
    occupancy
    cost-to-go
    env.
    info
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  12. /30
    11
    Offline Training & Model Arch.
    features
    ?
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  13. /30
    12
    Offline Training & Model Arch.
    +
    +
    relative positions,
    size, speeds, etc
    goal-driven
    features
    encoded by
    neural network
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  14. /30
    13
    Offline Training & Model Arch.
    +
    +
    communication
    features
    learning interactions
    with nearby agents
    multiple
    agents
    multi-agent attention
    [Sohn+ NeurIPS-15]
    relative positions,
    size, speeds, etc
    env. info
    encoded by
    neural network
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  15. /30
    14
    Offline Training & Model Arch.
    go right
    [0,0,1]
    indicator
    feature
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  16. /30
    15
    +
    +
    +
    +
    go right
    [0,0,1]
    𝐹!"#$
    next position
    goal-driven
    features
    comm.
    features
    indicator
    feature
    Offline Training & Model Arch.
    instance & solution
    occupancy
    cost-to-go
    env.
    info
    relative positions,
    size, speeds, etc
    attention
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  17. /30
    16
    Outline of Approach
    new instance
    𝐹!"#$
    random
    walk
    sampling module
    next
    locations
    starts
    path generation
    compositing
    solution
    MAPF
    algorithm

    t=0 t=1 t=2
    CTRMs
    𝐹!"#$
    model training
    instances & solutions predict next locations
    Online Inference
    Offline Training
    CVAE: Conditional Variational Autoencoder
    [Sohn+ NeurIPS-15]
    +importance sampling
    [Salzmann+ ECCV-20]
    +multi-agent attention
    [Hoshen NeurIPS-17]
    𝐹!"#$
    :
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  18. /30
    17
    Online Inference
    observations
    for agent-i
    next predicted location
    for agent-i
    trained model
    likely to be used by planners
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  19. /30
    18
    Online Inference
    observations
    for agent-1
    next predicted location
    for agent-1
    observations
    for agent-N
    observations
    for agent-2
    next predicted location
    for agent-2
    next predicted location
    for agent-N


    timestep t timestep t+1
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  20. /30
    19
    Online Inference
    timestep t timestep t+1
    next predicted locations
    for all agents
    observations
    for all agents
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  21. /30
    20
    Online Inference
    t=0 t=1 t=2 t=T
    t=T-1

    initial locations
    timed path for agent-i
    each path is agent-specific and cooperative
    hyperparameter
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  22. /30
    21
    Online Inference

    t=0 t=1 t=2 t=T
    t=T-1
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  23. /30
    22
    Online Inference




    compositing
    t=0 t=1 t=2 t=T
    t=T-1
    timed roadmap for agent-i
    each roadmap is agent-specific and cooperative
    hyperparameter:
    #(path generation)
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  24. /30
    23
    Outline of Approach
    new instance
    𝐹!"#$
    random
    walk
    sampling module
    next
    locations
    starts
    path generation
    compositing
    solution
    MAPF
    algorithm

    t=0 t=1 t=2
    CTRMs
    𝐹!"#$
    model training
    instances & solutions predict next locations
    Online Inference
    Offline Training
    CVAE: Conditional Variational Autoencoder
    [Sohn+ NeurIPS-15]
    +importance sampling
    [Salzmann+ ECCV-20]
    +multi-agent attention
    [Hoshen NeurIPS-17]
    𝐹!"#$
    :
    *independent from map size and #agent, not limited to homo-agents
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  25. /30
    24
    Evaluation
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  26. /30
    25
    Roadmap Visualization
    SPARS
    [Dobson & Bekris, IJRR-14]
    (random)
    simplified PRM
    [Karaman & Frazzoli, IJRR-11]
    square
    as agent-specific roadmaps
    grid
    as used in MAPF studies
    CTRMs
    20-30 homo agents
    corresponding to 32x32 grids
    CTRMs produce small but effective roadmaps specific to each agent
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  27. /30
    26
    Quantitative Results
    0 3000 6000
    saPSles Ser (agent, tiPesteS)
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    success rate
    CTR0s
    randRP
    grid
    63AR6
    sTuare
    20-30 homo agents
    corresponding to 32x32 grids
    100 instances
    solved by prioritized planning
    [Silver, AIIDE-05, Van Den Berg &
    Overmars IROS-05, etc]
    CTRMs contain solutions
    in small search spaces
    sparse dense
    params of CTRMs:
    #(path generation)
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  28. /30
    27
    Quantitative Results
    103 104 105
    exSanded nRdes / agents
    0
    10
    20
    30
    40
    suP-Rf-cRsts / agents
    average Rver 40/100 instances
    CT50s
    randRP
    grid
    S3A5S
    sTuare
    20-30 homo agents
    corresponding to 32x32 grids
    100 instances
    solved by prioritized planning
    [Silver, AIIDE-05, Van Den Berg &
    Overmars IROS-05, etc]
    CTRMs reduce planning effort
    while keeping solution qualities
    params of CTRMs:
    #(path generations)
    sparse
    dense
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  29. /30
    28
    Quantitative Results
    CT50
    s
    randRP
    grid
    S3A5S
    sTuare
    0
    100
    200
    300
    400
    500
    runtiPe (sec)
    x
    average Rver 40/100 instances
    rRadPaS
    Slanner
    20-30 homo agents
    corresponding to 32x32 grids
    100 instances
    solved by prioritized planning
    [Silver, AIIDE-05, Van Den Berg &
    Overmars IROS-05, etc]
    CTRMs achieve efficient path-planning
    from the end-to-end perspective
    sparse dense
    *Roadmap construction can be much faster. Check our latest implementation: https://github.com/omron-sinicx/jaxmapp
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  30. /30
    29
    Quantitative Results
    basic no obstacles more obstacles more agents hetero agents
    CTRMs consistently outperform other baselines
    small search spaces but containing plausible solutions
    (Check our paper for details)
    reducing planning effort by orders of magnitude
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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  31. /30
    30
    multi-agent path planning in continuous spaces
    motivation
    effective roadmaps for multiple agents?
    challenge
    CTRMs / data-driven roadmap construction
    proposal
    reducing planning effort (e.g., runtime) significantly
    result
    Concluding Remarks
    project page: https://omron-sinicx.github.io/ctrm/
    anytime planning, higher-dimensional spaces
    future work
    instance roadmaps solution
    start
    multi-agent
    pathfinding
    goal
    Ⓒ 2022 OMRON SINIC X Corporation. All Rights Reserved.

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