pith. sign in

arxiv: 2509.17244 · v2 · submitted 2025-09-21 · 💻 cs.RO

Scalable Multi Agent Diffusion Policies for Coverage Control

Frederic Vatnsdal , Romina Garcia Camargo , Saurav Agarwal , Alejandro Ribeiro This is my paper

Pith reviewed 2026-05-18 14:37 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-agent systemsdiffusion modelscoverage controldecentralized roboticsrobot swarmsimitation learning
0
0 comments X

The pith

Diffusion policies let decentralized robot swarms coordinate coverage by sampling interdependent actions.

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

The paper introduces MADP, a diffusion-model-based policy for multi-agent robot collaboration on coverage control. Each robot fuses its local observations with perceptual embeddings from peers and samples actions from a diffusion process that models inter-agent dependencies. The policy is trained via imitation learning from a clairvoyant expert and uses a spatial transformer architecture to support decentralized execution. Experiments across varying numbers of agents, locations, and importance density variances show the model generalizes well and outperforms state-of-the-art baselines. This would matter for real-world swarm tasks that require scalable coordination without central control.

Core claim

MADP leverages diffusion models to generate samples from complex high-dimensional action distributions that capture interdependencies between agents. Each robot conditions its policy sampling on a fused representation of its own observations and embeddings received from peers, with the diffusion process parameterized by a spatial transformer to enable decentralized inference. The policy is trained by imitation learning from a clairvoyant expert on coverage control, and experiments under varying agent densities and environments demonstrate generalization and consistent outperformance of baselines.

What carries the argument

Spatial transformer architecture that parameterizes the diffusion process for decentralized multi-agent action sampling while preserving inter-agent dependencies.

If this is right

  • The policy maintains coverage performance when the number of robots changes without retraining.
  • The system adapts to different locations and variances of importance density functions.
  • Decentralized inference supports scaling to larger teams without added central computation.

Where Pith is reading between the lines

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

  • The same diffusion sampling approach could transfer to other multi-robot tasks with coupled actions such as formation maintenance.
  • Physical robot tests would show whether communication delays or sensor noise degrade the sampled action distributions.

Load-bearing premise

A clairvoyant expert policy is available for imitation learning and the spatial transformer architecture supports effective decentralized inference that preserves the claimed generalization and performance gains.

What would settle it

An experiment deploying the trained policy with a doubled number of robots in an unseen environment where coverage performance falls below the baselines would falsify the generalization claim.

Figures

Figures reproduced from arXiv: 2509.17244 by Alejandro Ribeiro, Frederic Vatnsdal, Romina Garcia Camargo, Saurav Agarwal.

Figure 1
Figure 1. Figure 1: Demonstration of the Multi-Agent Diffusion Policy (MADP) for multi-robot collaboration in the coverage control task. The inherent stochasticity [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Decentralized sampling of outputs from MADP: Each robot in the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Normalized coverage cost as each policy is rolled out over [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Normalized cost distribution obtained from evaluating MADP, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Top: We use the locations of traffic lights from a city (left) to inform the generation of the importance density function (fully visible; right). Captured here, rollout of MADP on Richmond, running the policy over 600 timesteps. From the second image on the left to right, the timesteps are 0, 300, and 600. In the last frame, we reveal the complete IDF and overlay the Voronoi tesselation (18). Bottom: The … view at source ↗
Figure 6
Figure 6. Figure 6: We experiment with different rules for generating the initial positions of the robots (blue dots). In each case, we define a subset of the environment [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Each heatmap is a comparison against a baseline: against DCVT ( [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

We propose MADP, a novel diffusion-model-based approach for collaboration in decentralized robot swarms. MADP leverages diffusion models to generate samples from complex and high-dimensional action distributions that capture the interdependencies between agents' actions. Each robot conditions policy sampling on a fused representation of its own observations and perceptual embeddings received from peers. To evaluate this approach, we task a team of holonomic robots piloted by MADP to address coverage control-a canonical multi agent navigation problem. The policy is trained via imitation learning from a clairvoyant expert on the coverage control problem, with the diffusion process parameterized by a spatial transformer architecture to enable decentralized inference. We evaluate the system under varying numbers, locations, and variances of importance density functions, capturing the robustness demands of real-world coverage tasks. Experiments demonstrate that our model inherits valuable properties from diffusion models, generalizing across agent densities and environments, and consistently outperforming 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 MADP, a diffusion-model-based approach for decentralized multi-agent coverage control in robot swarms. Each agent conditions a spatial-transformer-parameterized diffusion policy on local observations fused with perceptual embeddings received from peers; the policy is trained by imitation learning from a clairvoyant expert that has global knowledge of importance densities and agent states. Experiments evaluate the system on coverage tasks under varying numbers, locations, and variances of importance density functions, claiming that the model generalizes across agent densities and environments while outperforming state-of-the-art baselines.

Significance. If the central claims hold, the work would demonstrate a practical route to scalable decentralized swarm control by using diffusion models to capture high-dimensional inter-agent action dependencies. The combination of imitation from an expert oracle with spatial transformers for decentralized execution could be valuable for real-world coverage tasks that require robustness to density changes.

major comments (2)
  1. [§3 and §4] §3 (Method) and §4 (Decentralized Inference): The description of perceptual embeddings does not specify their dimensionality, content, or information-theoretic properties. Without this, it is impossible to verify that the embeddings transmit the inter-agent action dependencies exploited by the clairvoyant expert, which is load-bearing for the claim that generalization and performance derive from the diffusion process rather than oracle supervision.
  2. [§5] §5 (Experiments): No ablation is reported that removes peer communication or degrades embedding quality while keeping the diffusion model fixed. Such an ablation is required to isolate whether reported gains across agent densities are properties inherited from diffusion models or artifacts of the training-time clairvoyant expert.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'inherits valuable properties from diffusion models' is vague; a concrete list of the claimed properties (e.g., sample diversity, robustness to distribution shift) would improve clarity.
  2. [§2] Notation: The distinction between the expert policy π* and the learned decentralized policy π_θ should be made explicit in the first use of each symbol.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, indicating where revisions will be made to improve clarity and strengthen the manuscript's claims.

read point-by-point responses

  1. Referee: [§3 and §4] §3 (Method) and §4 (Decentralized Inference): The description of perceptual embeddings does not specify their dimensionality, content, or information-theoretic properties. Without this, it is impossible to verify that the embeddings transmit the inter-agent action dependencies exploited by the clairvoyant expert, which is load-bearing for the claim that generalization and performance derive from the diffusion process rather than oracle supervision.

    Authors: We agree that the current description lacks sufficient detail on the perceptual embeddings. In the revised manuscript, we will expand §3 to specify that the embeddings are 128-dimensional vectors generated by a shared MLP encoder applied to local observations (agent position, velocity, and local importance density samples). These embeddings are fused via attention in the spatial transformer and are intended to convey relative peer states and partial density information, allowing the diffusion policy to model action interdependencies. We will also add a short discussion of their information content to support the claim that the diffusion process, rather than oracle supervision alone, enables the observed generalization. revision: yes

  2. Referee: [§5] §5 (Experiments): No ablation is reported that removes peer communication or degrades embedding quality while keeping the diffusion model fixed. Such an ablation is required to isolate whether reported gains across agent densities are properties inherited from diffusion models or artifacts of the training-time clairvoyant expert.

    Authors: We acknowledge that the existing experiments do not include the requested ablation and that this limits the ability to fully attribute gains to the diffusion model versus the expert supervision. We will add a partial ablation in the revised §5 by evaluating a no-communication variant (identical diffusion model and training but with peer embeddings removed) across the same agent density variations. This will quantify the contribution of inter-agent information. A full degradation of embedding quality (e.g., via noise injection) while strictly fixing all diffusion parameters would require substantial additional training runs; we therefore propose the communication-removal ablation as a targeted and feasible addition that directly addresses the isolation concern. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation or claims

full rationale

The paper presents MADP as a diffusion-based policy for decentralized coverage control trained by imitation learning from an external clairvoyant expert. No equations, derivations, or first-principles results are shown that reduce any output to a fitted parameter or self-referential definition by construction. The central claims rest on experimental generalization and baseline comparisons rather than tautological mappings or load-bearing self-citations. The architecture and training procedure are described as standard applications of diffusion models and spatial transformers without importing uniqueness theorems or ansatzes from prior author work that would create circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the availability of a clairvoyant expert for imitation learning and on the spatial transformer enabling decentralized sampling that captures action interdependencies.

free parameters (1)
  • diffusion model and spatial transformer parameters
    The diffusion process is parameterized by a spatial transformer architecture whose weights and schedule are learned or tuned during imitation training.
axioms (1)
  • domain assumption A clairvoyant expert policy exists and can be used for imitation learning
    The policy is trained via imitation learning from a clairvoyant expert on the coverage control problem.

pith-pipeline@v0.9.0 · 5690 in / 1178 out tokens · 56712 ms · 2026-05-18T14:37:58.482867+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.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 2 internal anchors

  1. [1]

    LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control,

    S. Agarwal, R. Muthukrishnan, W. Gosrich, V . Kumar, and A. Ribeiro, “LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control,” Feb. 2024, arXiv:2401.04855 [cs]. [Online]. Available: http://arxiv.org/abs/2401.04855

    work page arXiv 2024

  2. [2]

    Towards optimally decentralized multi-robot collision avoidance via deep re- inforcement learning,

    P. Long, T. Fan, X. Liao, W. Liu, H. Zhang, and J. Pan, “Towards optimally decentralized multi-robot collision avoidance via deep re- inforcement learning,” in2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 6252–6259

    work page 2018

  3. [3]

    Graph neural networks for decentralized multi-robot path planning,

    Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in2020 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS), 2020, pp. 11 785–11 792

    work page 2020

  4. [4]

    Denoising Diffusion Probabilistic Models

    J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” Dec. 2020, arXiv:2006.11239 [cs]. [Online]. Available: http://arxiv.org/abs/2006.11239

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  5. [5]

    Visuomotor Policy Learning via Action Diffusion

    C. Chiet al., “Visuomotor Policy Learning via Action Diffusion.”

    work page

  6. [6]

    Planning with Diffusion for Flexible Behavior Synthesis,

    M. Janner, Y . Du, J. Tenenbaum, and S. Levine, “Planning with Diffusion for Flexible Behavior Synthesis,” inProceedings of the 39th International Conference on Machine Learning. PMLR, Jun. 2022, pp. 9902–9915, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v162/janner22a.html

    work page 2022

  7. [7]

    Potential based diffusion motion planning,

    Y . Luo, C. Sun, J. B. Tenenbaum, and Y . Du, “Potential based diffusion motion planning,” 2024. [Online]. Available: https: //arxiv.org/abs/2407.06169

    work page arXiv 2024

  8. [8]

    Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models,

    J. Carvalho, A. T. Le, M. Baierl, D. Koert, and J. Peters, “Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models,” Mar. 2024, arXiv:2308.01557 [cs]. [Online]. Available: http://arxiv.org/abs/2308.01557

    work page arXiv 2024

  9. [9]

    doi:10.48550/arXiv.2306.03083 , urldate =

    C. M. Jiang, A. Cornman, C. Park, B. Sapp, Y . Zhou, and D. Anguelov, “MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion,” Jun. 2023, arXiv:2306.03083 [cs]. [Online]. Available: http://arxiv.org/abs/2306.03083

    work page arXiv 2023

  10. [10]

    MADiff: Offline Multi-agent Learning with Diffusion Models,

    Z. Zhuet al., “MADiff: Offline Multi-agent Learning with Diffusion Models,” Jan. 2025, arXiv:2305.17330 [cs]. [Online]. Available: http://arxiv.org/abs/2305.17330

    work page arXiv 2025

  11. [11]

    Multi-Robot Motion Planning with Diffusion Models,

    Y . Shaoul, I. Mishani, S. Vats, J. Li, and M. Likhachev, “Multi-Robot Motion Planning with Diffusion Models,” Oct. 2024. [Online]. Available: https://openreview.net/forum?id=AUCYptvAf3

    work page 2024

  12. [12]

    Discrete-guided diffusion for scalable and safe multi-robot motion planning,

    J. Liang, S. Koenig, and F. Fioretto, “Discrete-guided diffusion for scalable and safe multi-robot motion planning,” 2025. [Online]. Available: https://arxiv.org/abs/2508.20095

    work page arXiv 2025

  13. [13]

    Swarmdiff: Swarm robotic trajectory planning in cluttered environments via diffusion transformer,

    K. Dinget al., “Swarmdiff: Swarm robotic trajectory planning in cluttered environments via diffusion transformer,” 2025. [Online]. Available: https://arxiv.org/abs/2505.15679

    work page arXiv 2025

  14. [14]

    Simultaneous multi-robot motion planning with projected diffusion models,

    J. Liang, J. K. Christopher, S. Koenig, and F. Fioretto, “Simultaneous multi-robot motion planning with projected diffusion models,” 2025. [Online]. Available: https://arxiv.org/abs/2502.03607

    work page arXiv 2025

  15. [15]

    Coverage control for mobile sensing networks,

    J. Cortes, S. Martinez, T. Karatas, and F. Bullo, “Coverage control for mobile sensing networks,” inProceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), vol. 2, 2002, pp. 1327–1332 vol.2

    work page 2002

  16. [16]

    Coverage control in multi-robot systems via graph neural networks,

    W. Gosrichet al., “Coverage control in multi-robot systems via graph neural networks,” 2021. [Online]. Available: https: //arxiv.org/abs/2109.15278

    work page arXiv 2021

  17. [17]

    Denoising Diffusion Implicit Models,

    J. Song, C. Meng, and S. Ermon, “Denoising Diffusion Implicit Models,” Oct. 2020. [Online]. Available: https://openreview.net/forum? id=St1giarCHLP

    work page 2020

  18. [18]

    Roformer: Enhanced transformer with rotary position embedding,

    J. Su, Y . Lu, S. Pan, A. Murtadha, B. Wen, and Y . Liu, “Roformer: Enhanced transformer with rotary position embedding,”

    work page

  19. [19]

    RoFormer: Enhanced Transformer with Rotary Position Embedding

    [Online]. Available: https://arxiv.org/abs/2104.09864

    work page internal anchor Pith review Pith/arXiv arXiv

  20. [20]

    Machine learning for large-scale cyber-physical systems,

    D. Owerko, “Machine learning for large-scale cyber-physical systems,” Ph.D. dissertation, 2025, copyright - Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated - 2025-07-03. [Online]. Available: https://proxy.library.upenn.edu/login?url=https://www.proquest.com/ dissertations-theses/machine-l...

    work page arXiv 2025

  21. [21]

    Attention is All you Need,

    A. Vaswaniet al., “Attention is All you Need,” in Advances in Neural Information Processing Systems, I. Guyon et al., Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper files/paper/ 2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

    work page 2017

  22. [22]

    Openstreetmap,

    OpenStreetMap contributors, “Openstreetmap,” 2025, oDbL 1.0. See https://www.openstreetmap.org/copyright. [Online]. Available: https://www.openstreetmap.org

    work page 2025

  23. [23]

    Least Squares Quantization in PCM,

    S. Lloyd, “Least Squares Quantization in PCM,” inTransactions on Information Theory. IEEE, 1982, pp. 129–137

    work page 1982