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REVIEW 2 major objections 2 minor 60 cited by

Robot visuomotor policies can be represented as conditional denoising diffusion processes to outperform state-of-the-art methods by 46.9% on average.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-05-13 00:15 UTC

load-bearing objection Diffusion Policy adapts diffusion models to visuomotor robot control with receding-horizon and transformer tweaks, delivering reported gains on 12 tasks, but the size of those gains needs closer scrutiny on baseline fairness and inference timing. the 2 major comments →

arxiv 2303.04137 v5 submitted 2023-03-07 cs.RO

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Cheng Chi , Zhenjia Xu , Siyuan Feng , Eric Cousineau , Yilun Du , Benjamin Burchfiel , Russ Tedrake , Shuran Song This is my paper
classification cs.RO
keywords diffusion policyvisuomotor policy learningaction diffusionrobot manipulationdenoising diffusion processmultimodal action distributionspolicy learning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper tries to establish that modeling a robot's policy for visuomotor control as a conditional denoising diffusion process produces more effective behavior generation than existing approaches. A sympathetic reader would care because it provides concrete advantages in dealing with the inherent uncertainty and complexity of robot actions from visual inputs. The method learns the score function gradient of the action distribution and refines actions through iterative stochastic steps, leading to better results on manipulation benchmarks. This could open doors to policies that are more robust and easier to train for physical robots.

Core claim

Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. The diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential, the paper incorporates receding horizon control, visual conditioning, and the time-series diffusion transformer, resulting in consistent outperformance over existing state-of-the-art robot learning methods with an 46

What carries the argument

The conditional denoising diffusion process that represents the visuomotor policy and generates actions by starting from noise and iteratively denoising guided by visual observations.

Load-bearing premise

The iterative stochastic Langevin dynamics steps required for inference can be executed at a rate compatible with real-time closed-loop control on physical robot hardware without unacceptable latency or instability.

What would settle it

Observing the actual inference latency and closed-loop stability when deploying the diffusion policy on physical robot hardware for the benchmark tasks.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Gracefully handles multimodal action distributions
  • Suitable for high-dimensional action spaces
  • Exhibits impressive training stability
  • Supports receding horizon control for improved performance

Where Pith is reading between the lines

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

  • Diffusion-based policies may scale to more complex tasks or different modalities like language-conditioned actions.
  • The stability during training suggests diffusion models could replace less stable generative methods in other control applications.
  • Further work on accelerating the Langevin dynamics steps could broaden the applicability to faster control loops.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Diffusion Policy, representing visuomotor robot policies as conditional denoising diffusion processes. It reports benchmarking results on 12 tasks from 4 robot manipulation benchmarks, with consistent outperformance of prior state-of-the-art methods by an average of 46.9%. The method adapts diffusion-model inference via stochastic Langevin dynamics, with technical extensions for receding-horizon control, visual conditioning, and a time-series diffusion transformer. Code, data, and training details are released publicly.

Significance. If the empirical results hold under rigorous re-evaluation, the work demonstrates that diffusion-based generative modeling can yield substantial gains in robot policy learning, especially for multimodal action distributions and high-dimensional spaces, while offering training stability advantages. The public release of code and data is a notable strength that supports reproducibility and extension by the community.

major comments (2)
  1. [§4 (Experiments) and Table 1] §4 (Experiments) and Table 1: The central claim of a 46.9% average improvement across 12 tasks provides no statistical significance tests, standard deviations across random seeds, or explicit confirmation that all baselines were re-implemented with equivalent hyperparameter search and evaluation protocols; this weakens confidence that the reported gains are robust rather than sensitive to implementation details.
  2. [§3.3 (Inference Procedure)] §3.3 (Inference Procedure): The assertion that the iterative denoising steps are compatible with real-time closed-loop control on physical hardware is load-bearing for the practical contribution, yet no wall-clock latency measurements, control-frequency benchmarks, or hardware-specific timing results are reported to substantiate this.
minor comments (2)
  1. [Abstract] Abstract: The specific names of the 4 benchmarks and 12 tasks are not listed, which would allow readers to immediately assess task diversity and difficulty.
  2. [§3.1] §3.1: The notation for the conditional score function and the precise form of the visual conditioning could be made more explicit with an additional equation or diagram for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their positive assessment of the work and recommendation for minor revision. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [§4 (Experiments) and Table 1] The central claim of a 46.9% average improvement across 12 tasks provides no statistical significance tests, standard deviations across random seeds, or explicit confirmation that all baselines were re-implemented with equivalent hyperparameter search and evaluation protocols; this weakens confidence that the reported gains are robust rather than sensitive to implementation details.

    Authors: We appreciate the referee's emphasis on rigorous empirical validation. Our baseline re-implementations followed the original papers' protocols with hyperparameter tuning, and the public code release enables independent verification of these details. We agree, however, that explicitly reporting standard deviations across random seeds and statistical significance tests would strengthen confidence in the results. In the revised manuscript we will update Table 1 and §4 to include mean performance with standard deviations over multiple seeds and paired statistical tests for the key comparisons. revision: yes

  2. Referee: [§3.3 (Inference Procedure)] The assertion that the iterative denoising steps are compatible with real-time closed-loop control on physical hardware is load-bearing for the practical contribution, yet no wall-clock latency measurements, control-frequency benchmarks, or hardware-specific timing results are reported to substantiate this.

    Authors: We agree that concrete timing measurements are necessary to fully substantiate the claim of real-time compatibility. The inference procedure was designed with a fixed number of denoising steps and receding-horizon control precisely to enable closed-loop operation. In the revised manuscript we will add wall-clock latency results, achieved control frequencies, and hardware specifications from our experimental platforms to §3.3 and the experimental section. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper adapts established conditional diffusion models to represent visuomotor policies as denoising processes. All load-bearing claims are empirical benchmark results (46.9% average improvement across 12 tasks) rather than derivations that reduce by construction to fitted parameters, self-citations, or renamed inputs. The receding-horizon, visual conditioning, and transformer components are presented as standard extensions of the diffusion framework without internal self-definition or fitted-input-as-prediction patterns. No equations or steps equate outputs to inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The approach rests on standard assumptions of diffusion models and imitation learning from demonstrations; no new physical entities are postulated.

free parameters (2)
  • number of diffusion steps
    Hyperparameter controlling the number of denoising iterations during training and inference.
  • noise schedule parameters
    Parameters defining how noise is added and removed, chosen as part of model design.
axioms (2)
  • domain assumption Robot action distributions can be effectively modeled as the reverse of a forward diffusion process conditioned on visual observations.
    Invoked in the definition of the policy as a conditional denoising diffusion process.
  • domain assumption Demonstration data provides sufficient coverage for supervised training of the score function.
    Implicit in the imitation-learning setup used to train the diffusion policy.

pith-pipeline@v0.9.0 · 5519 in / 1377 out tokens · 44278 ms · 2026-05-13T00:15:44.928352+00:00 · methodology

0 comments
read the original abstract

This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is publicly available diffusion-policy.cs.columbia.edu

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · cited by 100 Pith papers · 6 internal anchors

  1. [1]

    Is Conditional Generative Modeling all you need for Decision-Making?

    Ajay A, Du Y , Gupta A, Tenenbaum J, Jaakkola T and Agrawal P (2022) Is conditional generative modeling all you need for decision-making? arXiv preprint arXiv:2211.15657 . Argall BD, Chernova S, Veloso M and Browning B (2009) A survey of robot learning from demonstration. Robotics and autonomous systems 57(5): 469–483. 14 Atkeson CG and Schaal S (1997) Ro...

    work page internal anchor Pith review arXiv 2022

  2. [2]

    pp. 12–20. Avigal Y , Berscheid L, Asfour T, Kr¨oger T and Goldberg K (2022) Speedfolding: Learning efficient bimanual folding of garments. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1–8. Bishop CM (1994) Mixture density networks. Aston University. Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp ...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  3. [3]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Deng J, Dong W, Socher R, Li LJ, Li K and Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition . Ieee, pp. 248–255. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al. (2020) An image is worth 16x16...

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  4. [4]

    Elucidating the Design Space of Diffusion-Based Generative Models

    Karras T, Aittala M, Aila T and Laine S (2022) Elucidating the design space of diffusion-based generative models. arXiv preprint arXiv:2206.00364 . Khatib O (1987) A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal on Robotics and Automation 3(1): 43–53. DOI:10. 1109/JRA.1987.1087068. Con...

    work page internal anchor Pith review arXiv 2022

  5. [5]

    (2021) Learning transferable visual models from natural language supervision

    Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et al. (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning . PMLR, pp. 8748–8763. Rahmatizadeh R, Abolghasemi P, B ¨ol¨oni L and Levine S (2018) Vision-based multi-task manipulation for...

    work page 2021

  6. [6]

    In: Proceedings of Robotics: Science and Systems (RSS)

    Reuss M, Li M, Jia X and Lioutikov R (2023) Goal-conditioned imitation learning using score-based diffusion policies. In: Proceedings of Robotics: Science and Systems (RSS). Ridnik T, Ben-Baruch E, Noy A and Zelnik-Manor L (2021) Imagenet-21k pretraining for the masses. Ronneberger O, Fischer P and Brox T (2015) U-net: Convolu- tional networks for biomedi...

    work page 2023

  7. [7]

    Consistency Models

    Springer. Sohl-Dickstein J, Weiss E, Maheswaranathan N and Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. Song J, Meng C and Ermon S (2021) Denoising diffusion implicit models. In: International Conference on Learning Representations. Song Y , Dhariwal P, Chen M and Sutske...

    work page internal anchor Pith review arXiv 2015

  8. [8]

    In: 2019 IEEE 58th Conference on Decision and Control (CDC)

    Subramanian J and Mahajan A (2019) Approximate information state for partially observed systems. In: 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, pp. 1629–

    work page 2019

  9. [9]

    Conditional energy- based models for implicit policies: The gap between theory and prac- tice,

    Ta DN, Cousineau E, Zhao H and Feng S (2022) Conditional energy-based models for implicit policies: The gap between theory and practice. arXiv preprint arXiv:2207.05824 . Tancik M, Srinivasan P, Mildenhall B, Fridovich-Keil S, Raghavan N, Singhal U, Ramamoorthi R, Barron J and Ng R (2020) Fourier features let networks learn high frequency functions in low...

    work page arXiv 2022

  10. [10]

    Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

    Wang Z, Hunt JJ and Zhou M (2022) Diffusion policies as an expressive policy class for offline reinforcement learning. arXiv preprint arXiv:2208.06193 . Wang Z, Hunt JJ and Zhou M (2023) Diffusion policies as an expressive policy class for offline reinforcement learning. In: The Eleventh International Conference on Learning Representations. URL https://op...

    work page internal anchor Pith review arXiv 2022

  11. [11]

    Diffusion Policy outperforms LSTM-GMM Mandlekar et al

    Data Efficiency Ablation Study. Diffusion Policy outperforms LSTM-GMM Mandlekar et al. (2021) at every training dataset size. except Push-T. Performance reported for DiffusionPolicy-C on Push-T in Tab. 1 used impaiting instead of FiLM. On simulation benchmarks, we used the iDDPM algorithm Nichol and Dhariwal (2021) with the same 100 denoising diffusion it...

    work page 2021

  12. [12]

    Hyperparameters for CNN-based Diffusion Policy Ctrl: position or velocity control To: observation horizon Ta: action horizon Tp: action prediction horizon ImgRes: environment observation resolution (Camera views x W x H) CropRes: random crop resolution #D-Params: diffusion network number of parameters in millions #V-Params: vision encoder number of parame...

    work page 2021

  13. [13]

    (2021)) B.2 Performance Improvement Calculation For each task i (column) reported in Tab

    Hyperparameters for Transformer-based Diffusion Policy Ctrl: position or velocity control To: observation horizon Ta: action horizon Tp: action prediction horizon #D-Params: diffusion network number of parameters in millions #V-Params: vision encoder number of parameters in millions Emb Dim: transformer token embedding dimension Attn Drp: transformer atte...

    work page 2021

  14. [14]

    To ensure the consistency of initial conditions, we carefully adjusted the pose of the T block and the robot according to overlayed images from the top-down camera

    Each method is evaluated for 20 episodes, all starting from the same set of initial conditions. To ensure the consistency of initial conditions, we carefully adjusted the pose of the T block and the robot according to overlayed images from the top-down camera. Each evaluation episode is terminated by either keeping the end-effector within the end-zone for...

    work page 1987