pith. sign in

arxiv: 2509.14159 · v3 · pith:CRVBK64Lnew · submitted 2025-09-17 · 💻 cs.RO

MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies

Dayi Dong , Maulik Bhatt , Seoyeon Choi , Negar Mehr This is my paper

Pith reviewed 2026-05-18 15:46 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-agent imitation learningdiffusion modelsdecentralized executionmulti-modal coordinationimplicit coordinationroboticsimitation learning
0
0 comments X

The pith

Joint training of decentralized diffusion policies produces implicit multi-agent coordination on multi-modal tasks using only local information.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces MIMIC-D, a diffusion-based imitation learning method for multi-agent systems that must handle tasks with several valid solutions. Standard approaches often average those solutions or lock onto one, which breaks coordination. Diffusion models can represent the full range of demonstrated behaviors, but prior multi-agent versions relied on a central planner or direct messages between agents. MIMIC-D instead trains every agent's policy jointly yet lets each run independently from its own local observations, producing coordinated behavior without explicit communication. Simulation and hardware tests show the resulting policies handle varied environments more reliably than current baselines.

Core claim

MIMIC-D jointly trains decentralized diffusion policies for all agents using only local information so that each policy captures the multi-modal distribution of expert trajectories and executes coordinated actions without a centralized planner or inter-agent communication.

What carries the argument

Jointly trained decentralized diffusion policies that model multi-modal trajectory distributions from local observations to enable implicit coordination.

If this is right

  • Robots can coordinate on tasks with multiple valid solutions without relying on communication infrastructure.
  • The same training procedure supports interaction with non-communicating partners such as humans.
  • Performance improves over prior multi-agent imitation methods in both simulation and physical hardware across multiple environments.
  • Decentralized execution remains feasible even when a central planner would be impractical.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach may scale to human-robot teams where reliable communication cannot be assumed.
  • Similar joint-training ideas could transfer to multi-agent reinforcement learning settings that also require multi-modal behavior.
  • Reducing dependence on centralized control could simplify deployment of larger robot fleets in unstructured spaces.

Load-bearing premise

Joint training on local information alone is enough to produce effective implicit coordination among agents on multi-modal tasks.

What would settle it

A controlled experiment in which agents trained with MIMIC-D are placed in a multi-modal coordination task and fail to select consistent complementary modes, resulting in collisions or stalled progress while centralized or communicating baselines succeed.

Figures

Figures reproduced from arXiv: 2509.14159 by Dayi Dong, Maulik Bhatt, Negar Mehr, Seoyeon Choi.

Figure 1
Figure 1. Figure 1: MIMIC-D deployed on a bimanual manipulation setup. An xArm7 and a Kinova3 robotic arm collaborate to lift a basket around an obstacle. The task presents a multi-modal challenge as the arms can either pass the obstacle on the right or on the left. Using our method, the arms achieve coordination by independently sampling their respective policies based on local observations without explicit communication. Ou… view at source ↗
Figure 2
Figure 2. Figure 2: An overview of our MIMIC-D framework. In the centralized training process (top), we utilize a dataset of multi￾agent expert demonstrations to train the robot policies jointly. During the decentralized execution process (bottom), each agent plans their trajectory independently by only making use of its local observations to sample the diffusion model. (minimize the divergence between the learner’s and exper… view at source ↗
Figure 3
Figure 3. Figure 3: Visualizations of the Two-Agent Swap and Three [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two-arm lift environment visualizations. We pro￾vide an example of what the two-arm lift task looks like for the Robosuite simulation version and the hardware demon￾stration. The simulation task uses two Kinova3 arms, while the hardware task uses one Kinova3 and one XArm7 arm. B. Baselines We compare our performance against multiple established imitation learning baselines. 1) BC: We frame this baseline as… view at source ↗
read the original abstract

As robots become more integrated in society, their ability to coordinate with other robots and humans on multi-modal tasks (those with multiple valid solutions) is crucial. Such behaviors can be learned from expert demonstrations via imitation learning (IL), but when expert demonstrations are multi-modal, standard IL approaches usually average across modes or collapse to a single mode, preventing effective coordination. Being inspired by diffusion models' ability to capture complex multi-modal trajectory distributions in single-agent settings, we develop a diffusion-based framework for coordinated multi-modal behavior in multi-agent systems. However, existing multi-agent diffusion approaches typically require a centralized planner or explicit communication among agents. This assumption can fail in real-world scenarios where robots must operate independently or with agents like humans that they cannot directly communicate with. Therefore, we propose MIMIC-D, a joint training with decentralized execution paradigm for multi-modal multi-agent IL via diffusion. We jointly train all agents' policies with only local information to achieve implicit coordination. In simulation and hardware experiments, our method exhibits robust multi-modal coordination behavior in various tasks and environments, improving upon state-of-the-art baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes MIMIC-D, a joint-training decentralized-execution framework for multi-modal multi-agent imitation learning based on diffusion policies. Agents are trained together on expert demonstrations using only local observations; at test time each agent runs an independent diffusion process conditioned on its own observations. The central claim is that this produces implicit coordination on multi-modal tasks without explicit communication or a centralized planner, with supporting evidence from simulation and hardware experiments that outperform state-of-the-art baselines.

Significance. If the empirical claims hold, the work would be a useful contribution to decentralized multi-robot imitation learning. It directly tackles the practical setting in which agents (including humans) cannot communicate and must still coordinate on tasks that admit multiple valid solutions. The hardware experiments are a positive feature that increases the practical relevance of the results.

major comments (2)
  1. [§3] §3 (Method): The training objective is the standard per-agent diffusion loss; no cross-agent consistency term, shared noise schedule, or mode-embedding mechanism is introduced. Because each agent performs independent denoising at execution time, it is not obvious why joint training on local observations alone guarantees that the agents select the same mode from the multi-modal expert distribution. This consistency is load-bearing for the coordination claim.
  2. [§4] §4 (Experiments): The reported improvements over baselines are presented without error bars, statistical significance tests, or ablation on the number of agents or noise levels. Without these, it is difficult to judge whether the decentralized execution reliably produces coordinated behavior or whether the gains are sensitive to particular random seeds or task instances.
minor comments (2)
  1. Notation for the local observation and action spaces is introduced without an explicit table or diagram relating them to the global state; a small diagram would improve readability.
  2. [Abstract] The abstract states that the method 'improves upon state-of-the-art baselines' but supplies no numerical values; the results section should include a concise table of key metrics with standard deviations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their positive evaluation of the work's significance and for the constructive feedback. We address the major comments point by point below, providing clarifications on the method and committing to empirical improvements.

read point-by-point responses

  1. Referee: [§3] §3 (Method): The training objective is the standard per-agent diffusion loss; no cross-agent consistency term, shared noise schedule, or mode-embedding mechanism is introduced. Because each agent performs independent denoising at execution time, it is not obvious why joint training on local observations alone guarantees that the agents select the same mode from the multi-modal expert distribution. This consistency is load-bearing for the coordination claim.

    Authors: We appreciate the referee's focus on the source of coordination. While the loss is applied independently per agent, joint training on synchronized expert demonstrations of multi-modal tasks allows each policy to learn mappings from its local observations to mode-consistent actions. In the demonstration data, agents' local views are correlated with the shared mode choice; thus, at test time, independent denoising conditioned on these views produces aligned trajectories without explicit mechanisms. We will revise §3 to elaborate this data-driven implicit coordination with an illustrative example. revision: partial

  2. Referee: [§4] §4 (Experiments): The reported improvements over baselines are presented without error bars, statistical significance tests, or ablation on the number of agents or noise levels. Without these, it is difficult to judge whether the decentralized execution reliably produces coordinated behavior or whether the gains are sensitive to particular random seeds or task instances.

    Authors: We agree that these additions would strengthen the presentation of results. In the revision we will report means and standard deviations over multiple random seeds, include statistical significance tests for key comparisons, and add ablations on agent count and diffusion noise levels to demonstrate robustness of the decentralized coordination. revision: yes

Circularity Check

0 steps flagged

No significant circularity in MIMIC-D derivation chain

full rationale

The paper proposes MIMIC-D as a new joint-training decentralized-execution framework extending diffusion-based imitation learning to multi-agent multi-modal coordination. The central claim rests on empirical results from simulation and hardware experiments showing improved coordination over baselines, not on a closed mathematical derivation. No equations or steps in the abstract or method description reduce a claimed prediction or result to a fitted parameter or self-referential definition by construction. Joint training on local observations is presented as an explicit design choice whose effectiveness is validated externally rather than assumed tautologically. The derivation is therefore self-contained against the provided benchmarks and does not trigger any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on extending single-agent diffusion capabilities to decentralized multi-agent settings through a new training paradigm whose effectiveness is asserted but not derived from first principles.

axioms (2)
  • domain assumption Diffusion models can capture complex multi-modal trajectory distributions in single-agent settings
    Invoked as the inspiration for extending the approach to multi-agent coordination.
  • ad hoc to paper Joint training with only local information suffices to achieve implicit coordination
    This premise is the load-bearing justification for the decentralized execution claim.

pith-pipeline@v0.9.0 · 5732 in / 1243 out tokens · 58348 ms · 2026-05-18T15:46:26.753344+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations

    cs.RO 2026-05 unverdicted novelty 7.0

    CoDi decomposes the multi-agent diffusion score into pre-trained single-agent policies plus a gradient-free cost guidance term to generate coordinated behavior from single-agent data alone.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages · cited by 1 Pith paper · 2 internal anchors

  1. [1]

    R. S. Sutton, A. G. Bartoet al.,Reinforcement learning: An introduc- tion. MIT press Cambridge, 1998, vol. 1, no. 1

    work page 1998

  2. [2]

    Learning to predict by the methods of temporal differences,

    R. S. Sutton, “Learning to predict by the methods of temporal differences,”Machine learning, vol. 3, no. 1, pp. 9–44, 1988

    work page 1988

  3. [3]

    Alvinn: An autonomous land vehicle in a neural network,

    D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,”Advances in neural information processing systems, vol. 1, 1988

    work page 1988

  4. [4]

    Generative adversarial imitation learning,

    J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems, vol. 29, 2016

    work page 2016

  5. [5]

    A reduction of imitation learning and structured prediction to no-regret online learning,

    S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” inProceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2011, pp. 627–635

    work page 2011

  6. [6]

    Multi-agent imitation learning for driving simulation,

    R. P. Bhattacharyya, D. J. Phillips, B. Wulfe, J. Morton, A. Kuefler, and M. J. Kochenderfer, “Multi-agent imitation learning for driving simulation,” in2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 1534–1539

    work page 2018

  7. [7]

    Adversarial skill chaining for long-horizon robot manipulation via terminal state regu- larization,

    Y . Lee, J. J. Lim, A. Anandkumar, and Y . Zhu, “Adversarial skill chaining for long-horizon robot manipulation via terminal state regu- larization,”arXiv preprint arXiv:2111.07999, 2021

    work page arXiv 2021

  8. [8]

    Maximum-entropy multi-agent dynamic games: Forward and inverse solutions,

    N. Mehr, M. Wang, M. Bhatt, and M. Schwager, “Maximum-entropy multi-agent dynamic games: Forward and inverse solutions,”IEEE transactions on robotics, vol. 39, no. 3, pp. 1801–1815, 2023

    work page 2023

  9. [9]

    Towards imitation learning in real world unstructured social mini-games in pedestrian crowds,

    R. Chandra, H. Karnan, N. Mehr, P. Stone, and J. Biswas, “Multi-agent inverse reinforcement learning in real world unstructured pedestrian crowds,”arXiv preprint arXiv:2405.16439, 2024

    work page arXiv 2024

  10. [10]

    Infogail: Interpretable imitation learning from visual demonstrations,

    Y . Li, J. Song, and S. Ermon, “Infogail: Interpretable imitation learning from visual demonstrations,”Advances in neural information processing systems, vol. 30, 2017

    work page 2017

  11. [11]

    Robust imitation of diverse behaviors,

    Z. Wang, J. S. Merel, S. E. Reed, N. de Freitas, G. Wayne, and N. Heess, “Robust imitation of diverse behaviors,”Advances in Neural Information Processing Systems, vol. 30, 2017

    work page 2017

  12. [12]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,”Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020

    work page 2020

  13. [13]

    Planning with Diffusion for Flexible Behavior Synthesis

    M. Janner, Y . Du, J. B. Tenenbaum, and S. Levine, “Plan- ning with diffusion for flexible behavior synthesis,”arXiv preprint arXiv:2205.09991, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  14. [14]

    Diffusion policy: Visuomotor policy learning via ac- tion diffusion,

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y . Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via ac- tion diffusion,”The International Journal of Robotics Research, p. 02783649241273668, 2023

    work page 2023

  15. [15]

    Motiondiffuser: Controllable multi-agent motion prediction using diffusion,

    C. Jiang, A. Cornman, C. Park, B. Sapp, Y . Zhou, D. Anguelov et al., “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 9644–9653

    work page 2023

  16. [16]

    Multi-agent reinforcement learning: Independent vs. cooper- ative agents,

    M. Tan, “Multi-agent reinforcement learning: Independent vs. cooper- ative agents,” inProceedings of the tenth international conference on machine learning, 1993, pp. 330–337

    work page 1993

  17. [17]

    A survey and critique of multiagent deep reinforcement learning,

    P. Hernandez-Leal, B. Kartal, and M. E. Taylor, “A survey and critique of multiagent deep reinforcement learning,”Autonomous Agents and Multi-Agent Systems, vol. 33, no. 6, pp. 750–797, 2019

    work page 2019

  18. [18]

    Multi-agent actor-critic for mixed cooperative-competitive environments,

    R. Lowe, Y . I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mor- datch, “Multi-agent actor-critic for mixed cooperative-competitive environments,”Advances in neural information processing systems, vol. 30, 2017

    work page 2017

  19. [19]

    Monotonic value function factorisation for deep multi- agent reinforcement learning,

    T. Rashid, M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson, “Monotonic value function factorisation for deep multi- agent reinforcement learning,”Journal of Machine Learning Research, vol. 21, no. 178, pp. 1–51, 2020

    work page 2020

  20. [20]

    The surprising effectiveness of ppo in cooperative multi-agent games,

    C. Yu, A. Velu, E. Vinitsky, J. Gao, Y . Wang, A. Bayen, and Y . Wu, “The surprising effectiveness of ppo in cooperative multi-agent games,”Advances in neural information processing systems, vol. 35, pp. 24 611–24 624, 2022

    work page 2022

  21. [21]

    Multi-agent generative adversarial imitation learning,

    J. Song, H. Ren, D. Sadigh, and S. Ermon, “Multi-agent generative adversarial imitation learning,”Advances in neural information pro- cessing systems, vol. 31, 2018

    work page 2018

  22. [22]

    Misodice: Multi-agent imi- tation from unlabeled mixed-quality demonstrations,

    T. V . Bui, T. Mai, and H. T. Nguyen, “Misodice: Multi-agent imi- tation from unlabeled mixed-quality demonstrations,”arXiv preprint arXiv:2505.18595, 2025

    work page arXiv 2025

  23. [23]

    A diffusion- model of joint interactive navigation,

    M. Niedoba, J. Lavington, Y . Liu, V . Lioutas, J. Sefas, X. Liang, D. Green, S. Dabiri, B. Zwartsenberg, A. Scibioret al., “A diffusion- model of joint interactive navigation,”Advances in Neural Information Processing Systems, vol. 36, pp. 55 995–56 011, 2023

    work page 2023

  24. [24]

    Madiff: Offline multi-agent learning with diffusion models,

    Z. Zhu, M. Liu, L. Mao, B. Kang, M. Xu, Y . Yu, S. Ermon, and W. Zhang, “Madiff: Offline multi-agent learning with diffusion models,”Advances in Neural Information Processing Systems, vol. 37, pp. 4177–4206, 2024

    work page 2024

  25. [25]

    Latent theory of mind: A decentralized diffusion architecture for cooperative manipulation,

    C. He, G. S. Camps, X. Liu, M. Schwager, and G. Sartoretti, “Latent theory of mind: A decentralized diffusion architecture for cooperative manipulation,”arXiv preprint arXiv:2505.09144, 2025

    work page arXiv 2025

  26. [26]

    Elucidating the design space of diffusion-based generative models,

    T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,”Advances in neural information processing systems, vol. 35, pp. 26 565–26 577, 2022

    work page 2022

  27. [27]

    Improved denoising diffusion prob- abilistic models,

    A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion prob- abilistic models,” inInternational conference on machine learning. PMLR, 2021, pp. 8162–8171

    work page 2021

  28. [28]

    Diffusion models beat gans on image synthesis,

    P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,”Advances in neural information processing systems, vol. 34, pp. 8780–8794, 2021

    work page 2021

  29. [29]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 684–10 695

    work page 2022

  30. [30]

    Scalable diffusion models with transformers,

    W. Peebles and S. Xie, “Scalable diffusion models with transformers,” inProceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 4195–4205

    work page 2023

  31. [31]

    Multinash-pf: A particle filtering approach for computing multiple local generalized nash equilibria in trajectory games,

    M. Bhatt, I. Askari, Y . Yu, U. Topcu, H. Fang, and N. Mehr, “Multinash-pf: A particle filtering approach for computing multiple local generalized nash equilibria in trajectory games,”arXiv preprint arXiv:2410.05554, 2024

    work page arXiv 2024

  32. [32]

    robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

    Y . Zhu, J. Wong, A. Mandlekar, R. Mart ´ın-Mart´ın, A. Joshi, S. Nasiriany, Y . Zhu, and K. Lin, “robosuite: A modular simulation framework and benchmark for robot learning,” inarXiv preprint arXiv:2009.12293, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  33. [33]

    The wasserstein distances,

    C. Villani, “The wasserstein distances,” inOptimal transport: old and new. Springer, 2009, pp. 93–111

    work page 2009

  34. [34]

    Computing discrete fr ´echet distance,

    T. Eiter and H. Mannila, “Computing discrete fr ´echet distance,” 1994

    work page 1994

  35. [35]

    Unfreezing the robot: Navigation in dense, interacting crowds,

    P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in2010 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems. IEEE, 2010, pp. 797–803

    work page 2010

  36. [36]

    Geo- metric impedance control on se (3) for robotic manipulators,

    J. Seo, N. P. S. Prakash, A. Rose, J. Choi, and R. Horowitz, “Geo- metric impedance control on se (3) for robotic manipulators,”IFAC- PapersOnLine, vol. 56, no. 2, pp. 276–283, 2023

    work page 2023