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REVIEW 4 major objections 6 minor 2 cited by

Motion Before Action: Diffusing Object Motion as Manipulation Condition

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A robot policy that first predicts the manipulated object's future pose, then generates actions conditioned on that predicted motion, outperforms policies that map observations straight to actions.

desk verdict A clean, useful module that conditions diffusion policies on predicted object pose, with broad but not fully controlled experiments; the missing current-pose ablation keeps the central claim from being proven. read the letter →

arxiv 2411.09658 v3 pith:YZMNIUPJ submitted 2024-11-14 cs.RO

Yue Su , Xinyu Zhan , Hongjie Fang , Yong-Lu Li , Cewu Lu , Lixin Yang This is my paper · ORCID
classification cs.RO
keywords imitationlearningroboticmanipulationdiffusionpolicyobjectmotionprediction6Dposerepresentationactionobject-centricplug-and-playmodule
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes that a robot imitation policy should decide what the manipulated object will do before deciding what the robot itself should do. The authors introduce MBA, a plug-in module that runs two diffusion processes in cascade: one predicts the future 6D pose trajectory of the object from the current observation, and the second generates the robot's action sequence conditioned on that predicted motion. They argue this mirrors human motor reasoning and improves both the success rate and the learning speed of existing diffusion-based policies, without changing the policy's inputs or training data. Across 57 simulated tasks and four real-world manipulation tasks, adding MBA raises average success rates over the DP and DP3 baselines by about 14 and 6 percentage points respectively, and it also stabilizes training.

What carries the argument

The central machinery is a cascade of two denoising diffusion probabilistic models sharing a common pose representation: a motion diffusion head that predicts the future 6D pose sequence of the manipulated object from observation features, and the host policy's action diffusion head that generates robot actions conditioned on both the observation and the predicted motion. The load-bearing modeling choice is the factorization $p(M,A|O)=p(M|O)p(A|M,O)$, which turns object motion into a conditioning variable for action generation rather than a separate planner, and it is what lets MBA be inserted into any existing diffusion-head policy without retraining its perception stack.

What would settle it

In any of the reported tasks, swap the predicted object pose sequence at inference for the ground-truth future pose that was used as supervision during training; if success does not rise above the predicted-pose version, the performance gain is not coming from the pose information itself, contradicting the claimed conditioning mechanism.

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Extended reading notes

Core claim

MBA models the joint distribution of object motion $M$ and robot action $A$ given observation $O$ as $p(M,A|O)=p(M|O)p(A|M,O)$: a first denoising diffusion process samples the future object pose sequence $M_{t:t+T_m}$ (each pose a 9D vector of 3D translation plus 6D rotation) conditioned on observation features, and a second diffusion process generates the action sequence $A_{t:t+T_a}$ conditioned on both the observation and the encoded predicted motion. The paper argues that because the 6D object pose lives in the same representation space as the robot end-effector pose, the motion is a learnable, physically consistent intermediate variable, so this two-stage cascade yields more accurate and more stable manipulation policies than direct observation-to-action diffusion. The empirical claim is that adding MBA to existing diffusion-head policies improves average success rates by 14.2 points over DP and 6.2 points over DP3 across 57 simulated tasks, and increases success on four real-world tasks including soft-object and articulated-object manipulation.

Load-bearing premise

The load-bearing premise is that the future pose sequence of the manipulated object can be reliably predicted from the current observation, and that conditioning the action on that predicted motion preserves all the information about the correct action — if either fails, the entire two-stage cascade loses its advantage.

Editorial extensions

If this is right

  • Any diffusion-head policy (DP, DP3, RISE) can adopt MBA as a plug-in and is reported to gain success rate, especially on tasks needing precise contact, narrow action spaces, or 6-DoF rotation control.
  • MBA policies reach their peak success earlier in training and show lower variance across seeds, implying that fewer demonstrations or fewer training iterations may be needed for the same performance.
  • Because object pose and end-effector pose share the 9D/10D representation, the motion head can reuse the action head's encoding architecture and be trained with the same diffusion objective, simplifying joint optimization.
  • Conditioning on predicted 6D pose outperforms conditioning on 2D keypoint flow (ATM) in fine-manipulation benchmarks, suggesting pose-space motion is a more informative condition than visual-space flow for action generation.
  • At deployment, no motion-capture system is needed; the policy runs end-to-end from observations, so the supervision source for object poses can be replaced (e.g., by pose estimators) without altering the policy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the pose-conditioning mechanism is the true driver, MBA should extend naturally to multi-object scenes by predicting several pose sequences and concatenating their features; the paper's fixed-size observation vector currently blocks this, a limitation the authors explicitly acknowledge.
  • The reported doubling of inference time (~197 ms vs ~96 ms for DP) means the success gains come at a lower control frequency; an untested corollary is whether the gains persist when both methods are given the same wall-clock budget per control step.
  • The paper's success on the soft-bread task is notable because deformable objects are said to lack trackable 6D poses; this suggests the effective conditioning signal may be approximate localization rather than full rigid pose, and distinguishing these would sharpen the claimed mechanism.
  • A testable extension is to warm-start the motion head with a self-supervised forward model trained on unlabeled video, removing the MoCap supervision cost while keeping the cascade; the paper lists this as future work, not a demonstrated result.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes MBA (Motion Before Action), a plug-and-play module that first uses a diffusion process to predict a future object pose sequence from observations, then feeds that pose sequence as an additional condition to a second diffusion process that generates robot actions. The module is designed to attach to existing diffusion-based imitation policies such as DP, DP3, and RISE. The authors evaluate MBA in simulation across 57 tasks in Adroit, DexArt, and MetaWorld, and in four real-world tasks, reporting consistent average success-rate improvements over the vanilla baselines, plus a small comparison against the flow-based ATM method. The paper also reports learning-curve improvements and discusses limitations including inference cost, MoCap supervision, fixed object count, and deformable-object pose ambiguity.

Significance. If the central claim is validated, namely that conditioning action generation on predicted future object pose sequences is the cause of the observed improvements, the work would be a useful contribution to imitation learning for manipulation, offering a simple module that could be attached to several existing diffusion-policy architectures. The paper has notable strengths: a broad simulation benchmark (57 tasks, three seeds), real-world tests on four tasks, and a design that is agnostic to the visual backbone. It also explicitly discusses limitations. However, the experimental design does not currently isolate the effect of future-motion conditioning from simpler explanations such as added capacity or current-pose conditioning, and the real-world and ATM comparisons lack statistical grounding. These gaps are load-bearing for the paper's main claim, so significant revision is needed.

major comments (4)
  1. [§IV-B, Table I, Table II] The primary claim that predicting future object motion is what improves performance is not isolated by the experiments. All baselines are vanilla policies with no object-pose conditioning and no auxiliary motion-prediction module, so the reported gains conflate at least three factors: (1) the additional parameters and training signal of the motion module, (2) the benefit of conditioning on the object pose at the current frame, and (3) the specific value of a predicted future pose sequence. The paper itself concedes in §IV-B that for stationary objects the predicted sequence 'can also be regarded as a pose estimation in this situation,' and many of the benchmark tasks have largely stationary objects during the decisive contact phase. Without an ablation that feeds the current observed or estimated object pose (repeated over the Tm horizon) into the action head, with matched parameter count, the central claim 'motion before action' is not separated from the weaker claim that auxiliary pose conditioning helps. This is the load-bearing experiment for the paper's main contribution and must be added.
  2. [§V-B through §V-E, Table III] The real-world results are reported as point estimates without confidence intervals, per-trial variance, or significance tests. For example, in Cut Clay the DP3 Pick success drops from 95% to 80% when MBA is added, while DP3 w. MBA improves separation from 10% to 20%; in Open Drawer the 20%-to-55% improvement is based on 40 trials, yet no confidence interval is given. Some table entries are also missing (DP and DP w. MBA for Cut Clay are listed as '-'), and the number of trials for the ATM comparison in Table IV is not stated. Because the real-world evaluation is a central part of the paper's evidence that MBA helps substantially, the absence of uncertainty quantification and trial counts makes the real-world claim weaker than presented.
  3. [§V-F, Table IV] The comparison to ATM is limited to three tasks, only on the DP baseline, and reports no variance or trial count. The paper concludes that the pose-prediction paradigm yields higher-quality action generation than flow prediction, but the evidence is thin: ATM is a full policy with its own tracking architecture, while MBA is a module inserted into DP; the comparison does not control for training budget, network capacity, or inference details. The stated advantage of MBA is attributed to the 'vision-motion gap' and the inability of flow to capture handle pose or rotational dynamics, but these claims are not quantitatively supported by the three-task table. At minimum, the ATM baseline should be run on the same set of tasks and seeds as the other baselines, with equal step counts and reporting conventions.
  4. [§III, Eq. (1)–(4)] The factorization p(M,A|O)=p(M|O)p(A|M,O) is presented as the formal basis of the method, but its validity is not examined. Since M is itself generated from O by the first diffusion module, conditioning on M is a form of re-encoding O; the decomposition is only useful if M carries information that is not already captured by the observation features feeding the action head. The paper does not test this assumption directly, for instance by comparing against a condition formed from the current pose estimate, which is the same gap raised in the first major comment. Additionally, the method section states that the object pose is a 9D vector with 6D rotation, but for deformable objects such as bread the 6D pose is not well-defined; the Limitations section acknowledges this, but the method section should flag the scope restriction more prominently because it affects the interpretation of the Put Bread into Pot results.
minor comments (6)
  1. [Algorithm 1] In the fourth line of Algorithm 1, the update reads 'Ok' where the context indicates it should be 'Mk'; this is confusing because O is used for observations throughout the paper.
  2. [Table I] The header 'V eryHard' has a spacing typo and should read 'Very Hard'.
  3. [Fig. 3] The learning curves are described as evidence of higher learning efficiency, but no quantitative metric (e.g., area under the curve or steps-to-threshold) is reported; visual inspection alone is not a rigorous comparison.
  4. [§V-G] The inference-time comparison reports DP at 95.98 ms and DP with MBA at 197.50 ms, more than a doubling of latency; this cost is mentioned as a limitation, but the paper should also state whether this affects the real-time control loop in the evaluated tasks.
  5. [Table III and Table IV] The table footnotes do not specify the number of trials for the ATM rows in Table IV, nor do they explain why DP and DP w. MBA are absent for Cut Clay in Table III; the text mentions that 2D policies were restricted, but the caption should state this explicitly.
  6. [§I] The 'mathematical consistency' argument that object poses and end-effector poses are in the same space and hence share similar distributions is evocative but informal; a more precise statement of what shared distributional structure is assumed would help readers assess the design choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the object-motion prediction is supervised by external ground-truth poses, not by action success, and the conditional factorization is a mathematical identity.

full rationale

The claimed derivation chain is self-contained. The factorization p(M,A|O)=p(M|O)p(A|M,O) is the conditional-probability chain rule, so it imposes no circular constraint. The object-motion network is trained with Eq. (2) against ground-truth MoCap/simulator poses, and the action network is trained with Eq. (4) against expert actions; neither loss is the task-success metric. The predicted future pose sequence is therefore a genuinely external intermediate variable, not a fitted parameter renamed as a prediction. The self-citations ([14,42,46]) support only the real-world data-collection protocol and are not load-bearing for the central claim. The absence of an ablation that replaces future pose sequences with the current static pose is a legitimate experimental-control concern about what causes the reported gains, but it is not circularity: the motion predictor is not optimized to maximize action success. The Limitations section honestly discloses MoCap supervision cost, fixed object count, deformable-object pose intractability, and inference cost, and none of these disclosures reveals a step that reduces to its own inputs by construction.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard diffusion math plus the domain assumption that object pose is a sufficient intermediate conditioning signal. The only hand-chosen numbers are the sequence horizons Tm and Ta. No new physical entities are introduced.

free parameters (2)
  • Tm
    Length of the predicted object pose sequence; hand-chosen per task, only constrained by Tm ≥ Ta. No values are given in the paper.
  • Ta
    Length of the generated action sequence; hand-chosen per task, standard in action-chunking policies but not reported here.
assumptions (5)
  • standard math Bayes factorization p(M,A|O)=p(M|O)p(A|M,O)
    Used in Section III to justify the two-stage cascade; exact under probability theory.
  • standard math DDPM forward-reverse diffusion equations are valid for pose and action sequences
    The paper adopts the DDPM noise schedule and MSE denoising loss from Ho et al.; standard within the field.
  • domain assumption Object pose sequence is a sufficient intermediate variable for action generation
    The paper assumes p(A|M,O) contains all necessary action information; this is the load-bearing modeling choice and is not derived.
  • domain assumption Object poses and robot end-effector poses share similar distributions, making diffusion transferable
    Stated in Section I to justify why an object-pose diffusion module can integrate with action diffusion. This is an analogy, not a proven property.
  • domain assumption 6D pose tracking via MoCap is a valid ground truth for soft objects such as bread
    In Section V-C the bread is treated as a rigid object for pose supervision, although it deforms; the paper later lists deformable-object pose as an unaddressed limitation, creating an internal tension.

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Cite this review

Pith. "Pith review of Motion Before Action: Diffusing Object Motion as Manipulation Condition." pith.science (2026). https://pith.science/paper/YZMNIUPJ

@misc{pith2026241109658,
  author       = {Pith},
  title        = {Pith review of: Motion Before Action: Diffusing Object Motion as Manipulation Condition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YZMNIUPJ}},
  note         = {Machine review of arXiv:2411.09658}
}
read the original abstract

Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning that generates action sequences by reasoning about object motion from visual observations. We propose MBA (Motion Before Action), a novel module that employs two cascaded diffusion processes for object motion generation and robot action generation under object motion guidance. MBA first predicts the future pose sequence of the object based on observations, then uses this sequence as a condition to guide robot action generation. Designed as a plug-and-play component, MBA can be flexibly integrated into existing robotic manipulation policies with diffusion action heads. Extensive experiments in both simulated and real-world environments demonstrate that our approach substantially improves the performance of existing policies across a wide range of manipulation tasks. Project page: https://selen-suyue.github.io/MBApage/

Figures

Figures reproduced from arXiv: 2411.09658 by the authors.

Figure 1
Figure 1. Understanding object motion before action leads to better [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of MBA pipeline. MBA takes the current observation as input, which could be in the form of 3D point clouds or RGB images from different viewpoints. Object pose sequences are sampled as actions with denoising diffusion from the object policy to be part of the framework’s output. Conditioning on the observations and object pose actions, MBA samples deployable robot actions with denoising diffusion from the ro… view at source ↗
Figure 3
Figure 3. Average learning curves (success rate - training steps) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Real-world deployment platform and execution process [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Forward citations

Cited by 2 Pith papers

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  2. Detecting Reading-Induced Confusion Using EEG and Eye Tracking

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Reference graph

Works this paper leans on

61 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [1]

    Affordances from human videos as a versatile representation for robotics,

    S. Bahl, R. Mendonca, L. Chen, U. Jain, and D. Pathak, “Affordances from human videos as a versatile representation for robotics,” in CVPR, 2023

    2023

  2. [2]

    Dexart: Benchmarking gen- eralizable dexterous manipulation with articulated objects,

    C. Bao, H. Xu, Y . Qin, and X. Wang, “Dexart: Benchmarking gen- eralizable dexterous manipulation with articulated objects,” in CVPR, 2023

    work page 2023

  3. [3]

    Track2act: Predicting point tracks from internet videos enables generalizable robot manipulation,

    H. Bharadhwaj, R. Mottaghi, A. Gupta, and S. Tulsiani, “Track2act: Predicting point tracks from internet videos enables generalizable robot manipulation,” in ECCV, 2024

    work page 2024

  4. [4]

    Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,

    H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V . Ku- mar, “Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,” in ICRA, 2024

    work page 2024

  5. [5]

    Affordance learning from play for sample-efficient policy learning,

    J. Borja-Diaz, O. Mees, G. Kalweit, L. Hermann, J. Boedecker, and W. Burgard, “Affordance learning from play for sample-efficient policy learning,” in ICRA, 2022

    work page 2022

  6. [6]

    Structure and function of visual area mt,

    R. T. Born and D. C. Bradley, “Structure and function of visual area mt,” Annu. Rev. Neurosci. , 2005

    work page 2005

  7. [7]

    RT-1: robotics transformer for real-world control at scale,

    A. Brohan, N. Brown, J. Carbajal, and ..., “RT-1: robotics transformer for real-world control at scale,” in RSS, 2023

    work page 2023

  8. [8]

    Diffusion policy: Visuomotor policy learning via action diffusion,

    C. Chi and et al., “Diffusion policy: Visuomotor policy learning via action diffusion,” in RSS, 2023

    work page 2023

Show all 61 references
  1. [9]

    4d spatio-temporal convnets: Minkowski convolutional neural networks,

    C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in CVPR, 2019

    2019

  2. [10]

    Open x-embodiment: Robotic learning datasets and rt-x models,

    O. X.-E. Collaboration et al., “Open x-embodiment: Robotic learning datasets and rt-x models,” in ICRA, 2024

    2024

  3. [11]

    From play to policy: Conditional behavior generation from uncurated robot data,

    Z. J. Cui, Y . Wang, N. M. M. Shafiullah, and L. Pinto, “From play to policy: Conditional behavior generation from uncurated robot data,” in ICLR, 2023

    2023

  4. [12]

    Deep object-centric representations for generalizable robot learning,

    C. Devin, P. Abbeel, T. Darrell, and S. Levine, “Deep object-centric representations for generalizable robot learning,” in ICRA, 2018

    2018

  5. [13]

    Flowbot3d: Learning 3d articula- tion flow to manipulate articulated objects,

    B. Eisner, H. Zhang, and D. Held, “Flowbot3d: Learning 3d articula- tion flow to manipulate articulated objects,” in RSS, 2022

    2022

  6. [14]

    Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,

    H.-S. Fang et al., “Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,” in ICRA, 2024

    2024

  7. [15]

    Ifor: Iterative flow minimization for robotic object rearrangement,

    A. Goyal et al. , “Ifor: Iterative flow minimization for robotic object rearrangement,” in CVPR, 2022

    2022

  8. [16]

    Hirschorn and S

    O. Hirschorn and S. Avidan, A graph-based approach for category- agnostic pose estimation , 2024

    2024

  9. [17]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in NeurIPS, 2020

    2020

  10. [18]

    Spot: Se(3) pose trajectory diffusion for object- centric manipulation,

    C.-C. Hsu et al. , “Spot: Se(3) pose trajectory diffusion for object- centric manipulation,” arXiv preprint arXiv:2411.00965 , 2024

    2024 arXiv

  11. [19]

    Bc-z: Zero-shot task generalization with robotic imitation learning,

    E. Jang and et al., “Bc-z: Zero-shot task generalization with robotic imitation learning,” in CoRL, 2021

    2021

  12. [20]

    Reasoning about physical interactions with object-oriented prediction and planning,

    M. Janner, S. Levine, W. T. Freeman, J. B. Tenenbaum, C. Finn, and J. Wu, “Reasoning about physical interactions with object-oriented prediction and planning,” in ICLR, 2019

    2019

  13. [21]

    Egomimic: Scaling imitation learning via egocentric video,

    S. Kareer et al., “Egomimic: Scaling imitation learning via egocentric video,” arXiv preprint arXiv:2410.24221 , 2024

    2024 arXiv

  14. [22]

    Openvla: An open-source vision-language-action model,

    M. J. Kim et al. , “Openvla: An open-source vision-language-action model,” arXiv preprint arXiv:2406.09246 , 2024

    2024 arXiv

  15. [23]

    Behavior generation with latent actions,

    S. Lee, Y . Wang, H. Etukuru, H. J. Kim, N. M. M. Shafiullah, and L. Pinto, “Behavior generation with latent actions,” in ICML, 2024

    2024

  16. [24]

    Foam: Foresight-augmented multi-task imitation policy for robotic manipulation,

    L. Liu et al., “Foam: Foresight-augmented multi-task imitation policy for robotic manipulation,” arXiv preprint arXiv:2409.19528 , 2024

    2024 arXiv

  17. [25]

    What matters in learning from offline human demonstrations for robot manipulation,

    A. Mandlekar and et al., “What matters in learning from offline human demonstrations for robot manipulation,” in CoRL, 2021

    2021

  18. [26]

    Object-centric task and motion planning in dynamic environments,

    T. Migimatsu and J. Bohg, “Object-centric task and motion planning in dynamic environments,” IEEE Robotics and Automation Letters , 2020

    2020

  19. [27]

    Rt-affordance: Affordances are versatile in- termediate representations for robot manipulation,

    S. Nasiriany et al. , “Rt-affordance: Affordances are versatile in- termediate representations for robot manipulation,” arXiv preprint arXiv:2411.02704, 2024

    2024 arXiv

  20. [28]

    Octo: An open-source generalist robot policy,

    Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, and ..., “Octo: An open-source generalist robot policy,” in RSS, 2024

    2024

  21. [29]

    ALVINN: an autonomous land vehicle in a neural network,

    D. Pomerleau, “ALVINN: an autonomous land vehicle in a neural network,” in NeurIPS, 1988

    1988

  22. [30]

    Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,

    A. Rajeswaran et al. , “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” arXiv preprint arXiv:1709.10087, 2017

    2017 arXiv

  23. [31]

    Proximal policy optimization algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017

    2017 arXiv

  24. [32]

    Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds,

    D. Seita, Y . Wang, S. J. Shetty, E. Y . Li, Z. Erickson, and D. Held, “Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds,” in CoRL, 2023

    2023

  25. [33]

    Behavior transformers: Cloning k modes with one stone,

    N. M. Shafiullah, Z. Cui, A. A. Altanzaya, and L. Pinto, “Behavior transformers: Cloning k modes with one stone,” in NeurIPS, 2022

    2022

  26. [34]

    Denoising diffusion implicit models,

    J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in ICLR, 2021

    2021

  27. [35]

    Hrp: Human affordances for robotic pre-training,

    M. K. Srirama, S. Dasari, S. Bahl, and A. Gupta, “Hrp: Human affordances for robotic pre-training,” in RSS, 2024

    2024

  28. [36]

    Dense policy: Bidirectional autoregressive learning of actions,

    Y . Su et al. , “Dense policy: Bidirectional autoregressive learning of actions,” arXiv preprint arXiv:2503.13217 , 2025

    2025 arXiv

  29. [37]

    Raft: Recurrent all-pairs field transforms for optical flow,

    Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in ECCV, 2020

    2020

  30. [38]

    Mujoco: A physics engine for model-based control,

    E. Todorov, T. Erez, and Y . Tassa, “Mujoco: A physics engine for model-based control,” in IROS, 2012

    2012

  31. [39]

    6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark,

    S. Tyree et al., “6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark,” in IROS, 2022

    2022

  32. [40]

    Robotap: Tracking arbitrary points for few-shot visual imitation,

    M. Vecerik et al. , “Robotap: Tracking arbitrary points for few-shot visual imitation,” in ICRA, 2024

    2024

  33. [41]

    Vrl3: A data-driven framework for visual deep reinforcement learning,

    C. Wang, X. Luo, K. Ross, and D. Li, “Vrl3: A data-driven framework for visual deep reinforcement learning,” NeurIPS, 2022

    2022

  34. [42]

    Rise: 3d perception makes real-world robot imitation simple and effective,

    C. Wang, H. Fang, H.-S. Fang, and C. Lu, “Rise: 3d perception makes real-world robot imitation simple and effective,” in IROS, 2024

    2024

  35. [43]

    Articulated object manipulation using on- line axis estimation with sam2-based tracking,

    X. Wang and et al., “Articulated object manipulation using on- line axis estimation with sam2-based tracking,” arXiv preprint arXiv:2409.16287, 2024

    2024 arXiv

  36. [44]

    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,” in CVPR, 2024

    2024

  37. [45]

    Any-point trajectory modeling for policy learning,

    C. Wen et al. , “Any-point trajectory modeling for policy learning,” arXiv preprint arXiv:2401.00025 , 2023

    2023 arXiv

  38. [46]

    Cage: Causal attention en- ables data-efficient generalizable robotic manipulation,

    S. Xia, H. Fang, H.-S. Fang, and C. Lu, “Cage: Causal attention en- ables data-efficient generalizable robotic manipulation,” arXiv preprint arXiv:2410.14974, 2024

    2024 arXiv

  39. [47]

    Sapien: A simulated part-based interactive environ- ment,

    F. Xiang et al. , “Sapien: A simulated part-based interactive environ- ment,” in CVPR, 2020

    2020

  40. [48]

    An end-to-end differentiable framework for contact- aware robot design,

    J. Xu et al. , “An end-to-end differentiable framework for contact- aware robot design,” in RSS, 2021

    2021

  41. [49]

    Flow as the cross-domain manipulation interface,

    M. Xu et al. , “Flow as the cross-domain manipulation interface,” in CoRL, 2024

    2024

  42. [50]

    Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,

    T. Yu et al., “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in CoRL, 2020

    2020

  43. [51]

    General flow as foundation affordance for scalable robot learning,

    C. Yuan, C. Wen, T. Zhang, and Y . Gao, “General flow as foundation affordance for scalable robot learning,” in CoRL, 2024

    2024

  44. [52]

    Robopoint: A vision-language model for spatial affordance prediction for robotics,

    W. Yuan et al. , “Robopoint: A vision-language model for spatial affordance prediction for robotics,” in CoRL, 2024

    2024

  45. [53]

    3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,

    Y . Ze and et al., “3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,” in RSS, 2024

    2024

  46. [54]

    Flowbot++: Learning generalized articulated objects manipulation via articulation projection,

    H. Zhang, B. Eisner, and D. Held, “Flowbot++: Learning generalized articulated objects manipulation via articulation projection,” in CoRL, 2023

    2023

  47. [55]

    Leveraging locality to boost sample efficiency in robotic manipulation,

    T. Zhang, Y . Hu, J. You, and Y . Gao, “Leveraging locality to boost sample efficiency in robotic manipulation,” in CoRL, 2024

    2024

  48. [56]

    Learning fine-grained bimanual manipulation with low-cost hardware,

    T. Z. Zhao and et al., “Learning fine-grained bimanual manipulation with low-cost hardware,” in RSS, 2023

    2023

  49. [57]

    On the continuity of rotation representations in neural networks,

    Y . Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in CVPR, 2019

    2019

  50. [58]

    Learning generalizable manipulation policies with object-centric 3d representations,

    Y . Zhu, Z. Jiang, P. Stone, and Y . Zhu, “Learning generalizable manipulation policies with object-centric 3d representations,” inCoRL, 2023

    2023

  51. [59]

    Viola: Object-centric imitation learning for vision-based robot manipulation,

    Y . Zhu, A. Joshi, P. Stone, and Y . Zhu, “Viola: Object-centric imitation learning for vision-based robot manipulation,” in CoRL, 2022

    2022

  52. [60]

    Vision-based manipulation from single human video with open-world object graphs,

    Y . Zhu, A. Lim, P. Stone, and Y . Zhu, “Vision-based manipulation from single human video with open-world object graphs,” arXiv preprint arXiv:2405.20321, 2024

    2024 arXiv

  53. [61]

    RT-2: vision-language-action models transfer web knowledge to robotic control,

    B. Zitkovich and T. Y . and..., “RT-2: vision-language-action models transfer web knowledge to robotic control,” in CoRL, 2023

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.