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REVIEW 1 major objections 1 minor 36 references

Decomposing tasks into scene, skill, and object components and recombining them generates diverse demonstrations that improve 3D visuomotor policy generalization.

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T0 review · grok-4.3

2026-06-27 22:07 UTC pith:PLGA3SSO

load-bearing objection Task-Edit's scene-skill-object recombination is a clear step past object-centric edits, but the paper must show how it avoids generating invalid trajectories or the gains won't hold. the 1 major comments →

arxiv 2606.07012 v1 pith:PLGA3SSO submitted 2026-06-05 cs.RO

Task Editing for Generalizable 3D Visuomotor Policy Learning

Jian-Jian Jiang , YiHan Yang , Lan Wei , Yuming Luo , Xiao-Ming Wu , Xuhang Chen , Bin Fan , Dandan Zhang
show 1 more author
Wei-Shi Zheng
This is my paper
classification cs.RO
keywords task editingdemonstration generation3D visuomotor policiesrobotic manipulationgeneralizationlong-horizon tasksscene-skill-object decomposition
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.

The paper proposes Task-Edit to address the high cost of collecting large-scale demonstrations for 3D visuomotor policies in robotic manipulation. It decomposes tasks into scene, skill, and object components from existing demonstrations and flexibly recombines them to synthesize new trajectories. This goes beyond prior object-centric transformations that largely preserve original scene structures and skill sequences. The approach is evaluated through real-world experiments showing gains in effectiveness, generalization across scenarios, and handling of hard cases like disturbances and clutter.

Core claim

Task-Edit is a demonstration generation framework that decomposes a task into scene, skill and object components and flexibly recombines them to synthesize diverse trajectories. This enables scalable demonstration generation and significantly improves generalization for long-horizon manipulation tasks, demonstrated across real-world tasks, robot embodiments, and scenario setups including disturbance resistance, obstacle avoidance, and unseen cluttered scenes.

What carries the argument

Task-Edit framework that decomposes tasks into scene, skill, and object components for flexible recombination to generate new demonstration trajectories.

Load-bearing premise

Recombining scene, skill, and object components extracted from existing demonstrations will produce valid, executable, and beneficial new trajectories that improve policy learning rather than introducing invalid motions or distribution shift.

What would settle it

Compare policy success rates on held-out long-horizon real-world tasks when trained on original demonstrations versus the same number augmented by Task-Edit recombinations; no consistent improvement or frequent execution failures on recombined trajectories would falsify the claim.

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

If this is right

  • Task-Edit significantly improves 3D visuomotor policies across various real-world tasks and robot embodiments.
  • Task-Edit improves model generalization across different scenario setups.
  • Task-Edit enables models to handle scenarios that are difficult to collect in the real world, including disturbance resistance, obstacle avoidance and unseen cluttered scenes.

Where Pith is reading between the lines

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

  • The method could lower the barrier to deploying visuomotor policies in new environments by leveraging smaller initial demonstration sets.
  • Component extraction accuracy becomes a key variable that future work could isolate by comparing manual versus automated decomposition.
  • Similar recombination logic might apply to other sequential control domains where demonstrations are expensive but partial structure can be reused.

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

1 major / 1 minor

Summary. The manuscript presents Task-Edit, a demonstration generation framework for 3D visuomotor policy learning. The method decomposes tasks into scene, skill, and object components extracted from human demonstrations and recombines them to synthesize new trajectories, with the goal of improving generalization in long-horizon robotic manipulation tasks. The authors report real-world experiments showing effectiveness, generalizability, and applicability to challenging scenarios such as disturbance resistance, obstacle avoidance, and unseen cluttered scenes across multiple robot embodiments.

Significance. If the recombined trajectories are shown to be kinematically valid and distributionally beneficial, Task-Edit could meaningfully reduce the cost of scaling demonstration data for visuomotor policies by enabling combinatorial diversity without new human collection. The focus on real-world evaluation across embodiments and long-horizon tasks provides a relevant testbed for assessing practical impact.

major comments (1)
  1. [Abstract] Abstract: The central claim that flexible recombination of scene/skill/object components 'enables scalable demonstration generation and significantly improves generalization' rests on the unstated assumption that recombined trajectories remain executable and useful. No mechanism (e.g., forward simulation, kinematic constraints, or post-generation filtering) is described to prevent physically invalid motions or large distribution shifts when objects are swapped into new scenes or skills are re-sequenced. This is load-bearing for the reported gains on disturbance resistance and obstacle avoidance, as invalid data would be expected to degrade rather than enhance policy performance.
minor comments (1)
  1. The abstract would benefit from one sentence specifying the 3D input representation (point cloud vs. depth) and the base policy architecture to allow readers to situate the contribution relative to prior visuomotor work.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on the abstract and the underlying assumptions of Task-Edit. We address the point directly below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that flexible recombination of scene/skill/object components 'enables scalable demonstration generation and significantly improves generalization' rests on the unstated assumption that recombined trajectories remain executable and useful. No mechanism (e.g., forward simulation, kinematic constraints, or post-generation filtering) is described to prevent physically invalid motions or large distribution shifts when objects are swapped into new scenes or skills are re-sequenced. This is load-bearing for the reported gains on disturbance resistance and obstacle avoidance, as invalid data would be expected to degrade rather than enhance policy performance.

    Authors: We agree that the abstract does not describe an explicit mechanism (simulation, constraints, or filtering) to guarantee kinematic validity or to bound distribution shift after recombination. The current manuscript presents the recombination process as operating on components extracted from real human demonstrations and reports that the resulting policies improve performance on the cited tasks, which provides indirect empirical support that the generated trajectories were executable and beneficial. However, this leaves the validity assumption implicit. We will revise the abstract for precision and add a short subsection in the method describing the recombination rules (component compatibility checks derived from the original demonstration structure) together with a note that trajectories are collected and verified in the real world before policy training. This revision will make the load-bearing assumption explicit without altering the reported results. revision: yes

Circularity Check

0 steps flagged

No circularity: procedural framework with no equations or fitted predictions

full rationale

The paper describes a demonstration-generation procedure that decomposes tasks into scene/skill/object components and recombines them. No equations, fitted parameters, or derivation chain appear in the provided text. The central claim is an empirical assertion about improved generalization from this recombination process, which stands as an independent proposal rather than a quantity that reduces to its own inputs by construction. No self-citation load-bearing steps or ansatz smuggling are present.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the premise that component recombination yields useful new data; no free parameters or invented physical entities are mentioned.

axioms (1)
  • domain assumption Recombining decomposed scene, skill, and object components from human demonstrations produces valid and distributionally useful trajectories for policy training.
    This premise is required for the generation step to improve rather than degrade generalization.

pith-pipeline@v0.9.1-grok · 5813 in / 1201 out tokens · 21433 ms · 2026-06-27T22:07:28.832252+00:00 · methodology

0 comments
read the original abstract

3D visuomotor policies offer a promising direction for complex robotic manipulation, as depth maps and point clouds provide rich geometric information for spatial reasoning. However, their success often depends on large-scale real-world demonstrations, which are costly and time-consuming to collect. To this end, existing methods commonly use demonstration generation strategies to improve data efficiency by applying object-centric transformations to human-collected demonstrations, such as varying object poses or scales. While effective for local variation, these transformations largely preserve the original scene structure and skill sequence, limiting their ability to synthesize diverse scene-skill-object combinations for complex tasks. In this paper, we propose Task-Edit, a novel demonstration generation framework that generates diverse trajectories from a task-centric editing perspective. The key insight of Task-Edit is to decompose a task into scene, skill and object components, and flexibly recombine them. In this way, Task-Edit enables scalable demonstration generation and significantly improves generalization for long-horizon manipulation tasks. We evaluate Task-Edit through extensive real-world experiments and demonstrate three advantages: (1) Effectiveness: Task-Edit significantly improves 3D visuomotor policies across various real-world tasks and robot embodiments. (2) Generalizability: Task-Edit improves model generalization across different scenario setups. (3) Applicability: Task-Edit enables models to handle scenarios that are difficult to collect in the real world, including disturbance resistance, obstacle avoidance and unseen cluttered scenes.

Figures

Figures reproduced from arXiv: 2606.07012 by Bin Fan, Dandan Zhang, Jian-Jian Jiang, Lan Wei, Wei-Shi Zheng, Xiao-Ming Wu, Xuhang Chen, Yihan Yang, Yuming Luo.

Figure 1
Figure 1. Figure 1: Task-Edit is a data-efficient framework that uses only a single demonstration to generate trajectories from a task-centric editing perspective. The key insight of Task-Edit lies in the decomposition of tasks into three core components: scenes, manipulation skills and objects. By editing the attributes of each component separately, Task-Edit generates diverse demonstrations for training. Furthermore, Task￾E… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the Task-Edit Framework. Our framework decomposes tasks into scenes, manipulation skills and objects, and edits each component independently. For object editing, Task-Edit aligns object origins and uses keypoint matching with spatial transformations to transfer skills to new objects. For skill editing, it converts the source demonstration into a Skill Directed Acyclic Graph and identifies i… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of execution in the Sorting (ABB) task. This time-series visualization compares the execution process of DP3 trained on DemoGen data [8] and Task-Edit data in the Sorting task. DemoGen: Failed Grasp on Basket Ours: Successful Grasp on Basket (a) In-Distribution Settings (b) Out-Of-Distribution Settings Ours(60): Failed Grasp on Little Tomato Ours(240): Successful Grasp on Little Tomato [PITH… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Results. In Fig. (a), we compare DP3 models trained on DemoGen and Task-Edit data in in-distribution Lifting. The model trained with DemoGen data fails to grasp the container edge stably, while our method achieves a steady grasp. In Fig. (b), we show DP3 performance on the Sorting task under an out-of-distribution setting. As the data scale and object-combination diversity increase, the model t… view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

36 extracted references · 4 canonical work pages

  1. [1]

    Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks,

    J. W. Kim, T. Z. Zhao, S. Schmidgall, A. Deguet, M. Kobilarov, C. Finn, and A. Krieger, “Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks,” inCoRL, 2024

    2024

  2. [2]

    Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method,

    D. Jiang, H. Wang, and Y . Lu, “Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method,” IEEE RAM, 2023

    2023

  3. [3]

    MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations,

    A. Mandlekar, S. Nasiriany, B. Wen, I. Akinola, Y . Narang, L. Fan, Y . Zhu, and D. Fox, “MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations,” inCoRL, 2023

    2023

  4. [4]

    DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning,

    Z. Jiang, Y . Xie, K. Lin, Z. Xu, W. Wan, A. Mandlekar, L. J. Fan, and Y . Zhu, “DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning,” inICRA, 2025

    2025

  5. [5]

    Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware,

    J. Yu, L. Fu, H. Huang, K. El-Refai, R. A. Ambrus, R. Cheng, M. Z. Irshad, and K. Goldberg, “Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware,” inCoRL, 2025

    2025

  6. [6]

    Constraint-Preserving Data Generation for Visuomotor Policy Learning,

    K. Lin, V . Ragunath, A. McAlinden, A. Prasad, J. Wu, Y . Zhu, and J. Bohg, “Constraint-Preserving Data Generation for Visuomotor Policy Learning,” inCoRL, 2025

    2025

  7. [7]

    SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment,

    C. R. Garrett, A. Mandlekar, B. Wen, and D. Fox, “SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment,” inCoRL, 2024

    2024

  8. [8]

    DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Pol- icy Learning,

    Z. Xue, S. Deng, Z. Chen, Y . Wang, Z. Yuan, and H. Xu, “DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Pol- icy Learning,” inRSS, 2025

    2025

  9. [9]

    Autotrialgen: Au- tomated data generation from few human demonstrations via trajectory annotation and simulation trials,

    H. Ma, A. Song, M. He, M. Li, Y . Yan, and L. Wei, “Autotrialgen: Au- tomated data generation from few human demonstrations via trajectory annotation and simulation trials,”IEEE RAL, 2026

    2026

  10. [10]

    Data Scaling Laws in Imitation Learning for Robotic Manipulation,

    F. Lin, Y . Hu, P. Sheng, C. Wen, J. You, and Y . Gao, “Data Scaling Laws in Imitation Learning for Robotic Manipulation,” inICLR, 2025

    2025

  11. [11]

    Isaac lab,

    NVIDIA, “Isaac lab,” https://github.com/isaac-sim/IsaacLab, 2025

    2025

  12. [12]

    Deemos, “Rodin,” https://hyperhuman.deemos.com/rodin, 2025

    2025

  13. [13]

    Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation

    X. Yang, H. Shi, B. Zhang, F. Yang, J. Wang, H. Zhao, X. Liu, X. Wang, Q. Lin, J. Yu, L. Wang, Z. Chen, S. Liu, Y . Liu, Y . Yang, D. Wang, J. Jiang, and C. Guo, “Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation,”arXiv preprint arXiv:2411.02293, 2024

    work page Pith review arXiv 2024

  14. [14]

    Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots

    M. Liu, Z. Zhu, X. Han, P. Hu, H. Lin, X. Li, J. Chen, J. Xu, Y . Yang, Y . Lin, X. Li, Y . Yu, W. Zhang, T. Kong, and B. Kang, “Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots,” arXiv preprint arXiv:2509.02530, 2025

    work page Pith review arXiv 2025

  15. [15]

    SAM 2: Segment Anything in Images and Videos,

    N. Ravi, V . Gabeur, Y . Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R ¨adle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V . Alwala, N. Carion, C. Wu, R. B. Girshick, P. Doll ´ar, and C. Feichtenhofer, “SAM 2: Segment Anything in Images and Videos,” inICLR, 2025

    2025

  16. [16]

    Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,

    C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,” inRSS, 2023

    2023

  17. [17]

    Unified Video Action Model,

    S. Li, Y . Gao, D. Sadigh, and S. Song, “Unified Video Action Model,” inRSS, 2025

    2025

  18. [18]

    π 0: A Vision-Language-Action Flow Model for General Robot Control,

    K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter, S. Jakubczak, T. Jones, L. Ke, S. Levine, A. Li-Bell, M. Mothukuri, S. Nair, K. Pertsch, L. X. Shi, J. Tanner, Q. Vuong, A. Walling, H. Wang, and U. Zhilinsky, “π 0: A Vision-Language-Action Flow Model for General Robot Control,” in RSS, 2025

    2025

  19. [19]

    OpenVLA: An Open-Source Vision-Language-Action Model,

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. P. Foster, P. R. Sanketi, Q. Vuong, T. Kollar, B. Burchfiel, R. Tedrake, D. Sadigh, S. Levine, P. Liang, and C. Finn, “OpenVLA: An Open-Source Vision-Language-Action Model,” inCoRL, 2024

    2024

  20. [20]

    Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation,

    M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation,” inCoRL, 2022

    2022

  21. [21]

    GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields,

    Y . Ze, G. Yan, Y . Wu, A. Macaluso, Y . Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang, “GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields,” inCoRL, 2023

    2023

  22. [22]

    ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation,

    G. Lu, S. Zhang, Z. Wang, C. Liu, J. Lu, and Y . Tang, “ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation,” in ECCV, 2024

    2024

  23. [23]

    Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework,

    J. Jiang, X. Wu, Y . He, L. Zeng, Y . Wei, D. Zhang, and W. Zheng, “Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework,” inICCV, 2025

    2025

  24. [24]

    3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations,

    Y . Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations,” inRSS, 2024

    2024

  25. [25]

    PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,

    C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” inCVPR, 2017

    2017

  26. [26]

    Denoising Diffusion Implicit Models,

    J. Song, C. Meng, and S. Ermon, “Denoising Diffusion Implicit Models,” inICLR, 2021

    2021

  27. [27]

    Real2edit2real: Generating robotic demonstrations via a 3d control interface.arXiv preprint arXiv:2512.19402, 2025

    Y . Zhao, H. Fan, D. Chen, S. Chen, L. Chen, X. Li, G. Ren, and H. Dong, “Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface,”arXiv preprint arXiv:2512.19402, 2025

    work page arXiv 2025

  28. [28]

    R2RGEN: Real- to-Real 3D Data Generation for Spatially Generalized Manipulation,

    X. Xu, A. Ma, H. Li, B. Yu, Z. Zhu, J. Zhou, and J. Lu, “R2RGEN: Real- to-Real 3D Data Generation for Spatially Generalized Manipulation,” RSS, 2026

    2026

  29. [29]

    Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation,

    S. Yang, W. Yu, J. Zeng, J. Lv, K. Ren, C. Lu, D. Lin, and J. Pang, “Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation,” inRSS, 2025

    2025

  30. [30]

    Twinaligner: Visual-dynamic alignment empow- ers physics-aware real2sim2real for robotic manipulation,

    H. Fan, H. Dai, J. Zhang, J. Li, Q. Yan, Y . Zhao, M. Gao, J. Wu, H. Tang, and H. Dong, “TwinAligner: Visual-Dynamic Alignment Em- powers Physics-aware Real2Sim2Real for Robotic Manipulation,”arXiv preprint arXiv:2512.19390, 2025

    work page arXiv 2025

  31. [31]

    Langsam,

    L. Medeiros, “Langsam,” https://github.com/luca-medeiros/ lang-segment-anything, 2024

    2024

  32. [32]

    FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects,

    B. Wen, W. Yang, J. Kautz, and S. Birchfield, “FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects,” inCVPR, 2024

    2024

  33. [33]

    DenseMatcher: Learning 3D Semantic Correspondence for Category- Level Manipulation from a Single Demo,

    J. Zhu, Y . Ju, J. Zhang, M. Wang, Z. Yuan, K. Hu, and H. Xu, “DenseMatcher: Learning 3D Semantic Correspondence for Category- Level Manipulation from a Single Demo,” inICLR, 2025

    2025

  34. [34]

    Revis- iting Point Cloud Completion: Are We Ready For The Real-World?

    S. Pathak, P. Kumar, N. Mboga, G. Steenackers, and R. Penne, “Revis- iting Point Cloud Completion: Are We Ready For The Real-World?” in ICCV, 2025

    2025

  35. [35]

    ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera,

    J. Shi, Y . A, Y . Jin, D. Li, H. Niu, Z. Jin, and H. Wang, “ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera,” inICRA, 2024

    2024

  36. [36]

    A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,

    M. Ester, H. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” in KDD, 1996

    1996