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REVIEW 4 major objections 4 minor 33 references

MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read In federated robot fleets, a multi-tier client-selection scheme that has data-rich robots train seed models for data-poor robots reduces the accuracy loss from quantity-skewed non-IID data, beating vanilla federated learning by up to 8…

desk verdict A clearly written, legitimate multi-tier FL extension to grasping with a correct communication-cost analysis, but the headline 8% gain is unsupported because the baseline training budget is unreported and there are no variance estimates. read the letter →

arxiv 2507.10158 v2 pith:D5757ODU submitted 2025-07-14 cs.LG cs.RO

Obaidullah Zaland , Erik Elmroth , Monowar Bhuyan This is my paper · ORCID
classification cs.LGcs.RO
keywords federatedlearningroboticgraspingnon-IIDdataquantityskewmulti-tierclientselectionFedAvgNova
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

MTF-Grasp is a federated learning scheme for robot fleets where each robot owns private, non-identically distributed grasp images and some robots have far fewer samples than others. The paper argues that instead of letting every robot train directly from the same global model, a server should rank robots by a combined score of data quantity and class-balance, pick the top few as "top-level" robots, and have them train seed models that are then passed to the remaining robots for further local training. On quantity-skewed versions of the Cornell and Jacquard-4k grasping datasets, this tiered scheme improves grasping accuracy by up to 8% over standard FedAvg and FedNova baselines while keeping the per-round communication volume identical. The reason this matters is that data scarcity and skewed class distributions are realistic in robot fleets, where collecting new grasp data is costly.

What carries the argument

The load-bearing object is the importance score $IS_r = \lambda_{\text{DDS}} DDS_r + \lambda_{\text{DQS}} DQS_r$, which ranks robots by how evenly their data spans global classes and by how many samples they hold relative to the richest robot. This ranking determines which robots become seed-model trainers; the multi-tier training loop, with two-step aggregation (low-level to top-level, top-level to server), is the mechanism that transfers the better-distributed knowledge of top-level robots to data-poor robots. The DDS/DQS scoring is what lets the method act on quantity skew specifically, and the seed-model handoff is what prevents low-level robots with few samples from overfitting to their own narrow data.

What would settle it

Run the vanilla baselines with the same total per-round local epochs and learning rate as MTF-Grasp, then compare accuracy on the same $\beta = 0.8$ quantity-skewed splits; if the gap shrinks to near zero, the reported advantage is a training-budget artifact rather than an effect of tiered client selection.

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Extended reading notes

Core claim

The paper's central discovery is that performance degradation from quantity-skewed non-IID data in federated robotic grasping can be reduced by a two-tier knowledge-transfer structure. Each robot reports a data distribution score DDS and a data quantity score DQS; the server combines these into an importance score IS, selects the j robots with the highest scores, and assigns the rest to one of these top-level robots. In each round the server sends the current global model only to top-level robots; after they train for et epochs, their intermediate models are passed to the low-level robots assigned to them, who train for er epochs; models are then aggregated first at top-level robots and then at the server. On the Cornell and Jacquard-4k datasets with controlled quantity skew, MTF-Grasp-Avg and MTF-Grasp-Nova consistently exceed their vanilla counterparts, with the largest gains at the highest skew levels.

Load-bearing premise

The central claim assumes the vanilla FedAvg and FedNova baselines were trained with the same total local update budget as MTF-Grasp; the paper reports MTF-Grasp's local epochs but not the baselines', so part of the 8% gap could be extra computation rather than the tiered design.

Editorial extensions

If this is right

  • With the simplest aggregation (FedAvg), the tiered scheme matches or beats the vanilla FedNova baseline in quantity-skewed settings, so the tiering itself, not the aggregation rule, is what recovers accuracy.
  • Local models of low-level robots improve alongside the global model, not just the centrally aggregated model.
  • Per communication round, MTF-Grasp transmits the same number of model parameters as conventional FL: $2 \times n \times |\theta|$.
  • The gains grow as quantity skew increases: at $\beta = 0.8$, MTF-Grasp-Nova reaches 82.80% on Cornell versus 80.00% for FedNova, and 87.40% versus 84.66% on Jacquard-4k.

Reading between the lines

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

  • Beyond the paper's experiments, the same tiered seed-model idea could transfer to other FL domains with quantity-skewed clients, such as medical imaging or mobile sensing, whenever a per-client quality score can be computed from label histograms or sample counts.
  • A direct test of the mechanism would hold the total local training budget fixed across methods and vary only the tiered seed-model handoff; if the 8% gap persists under matched compute, the tiering would be confirmed as the cause of the gain.
  • The data-quality score is a proxy based on class-count balance, not on actual image difficulty or grasp success; replacing or augmenting it with data-diversity metrics could make top-level selection more robust when a robot has many near-duplicate images.
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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 / 4 minor

Summary. The paper proposes MTF-Grasp, a multi-tier federated learning method for robotic grasping. The server ranks robots by a weighted combination of a data-quality score (DDS) and a data-quantity score (DQS), selects j top-level robots, has them train seed models, and then passes those models to attached low-level robots before two-stage FedAvg/FedNova aggregation. The authors claim up to 8% higher grasping accuracy over vanilla FL on quantity-skewed Cornell and Jacquard-4k datasets, as well as gains under class-based non-IID splits, with no additional communication load compared to standard FL.

Significance. The idea of using data-quality and data-quantity statistics to choose initial seed trainers is intuitive and relevant to federated robotic manipulation, where clients are heterogeneous and data is scarce. If the claimed gains were robust, the method would be a simple, communication-neutral addition to existing FL pipelines. The paper also correctly identifies a gap in the literature: most FL grasping work does not specifically address quantity skew. However, the empirical support as presented is not sufficient to establish the central claim, because the baselines are not shown to be compute-matched, no variance or statistical testing is reported, and key hyperparameters are tuned without a validation split. The contribution is therefore promising but unvalidated in its current form.

major comments (4)
  1. [§V.B and Table II] The baseline training budget is not reported. The paper specifies MTF-Grasp's local epochs (et=5 for top-level, er=15 for low-level) and global rounds (10), but it never states the local epochs, learning rate, or optimizer used for the vanilla FedAvg and FedNova baselines in Tables II–IV. Since each MTF-Grasp round performs 5 epochs on a top-level robot and then 15 epochs on each attached low-level robot, the effective per-round gradient updates are up to 20 per robot. If the baselines use one or a few local epochs, the reported gains may be a pure compute artifact rather than an effect of the multi-tier design. The authors must either report matched hyperparameters or provide an ablation that controls for the number of local updates.
  2. [Tables II–IV] All results are single runs with no error bars, no seeds, and no statistical tests. The reported improvements range from below one percentage point to about eight percentage points; without variance estimates or significance testing, these differences cannot be distinguished from noise. Since the paper's central claim is entirely empirical and is repeated in the abstract and conclusion, the absence of any repeated-seed evaluation is a load-bearing deficiency. Multiple seeds and standard deviations (or confidence intervals) are needed for every reported accuracy figure.
  3. [§V.B, Eq. (7)] The importance-score weights λ_DDS and λ_DQS are set to 0.5 because the authors state this value 'worked best', but no validation split or tuning procedure is described. Selecting hyperparameters based on test-set performance can inflate the reported gains and makes the comparison to vanilla FL unfair if the baselines were not tuned in the same way. The authors should use a validation split for hyperparameter selection and report sensitivity to λ_DDS and λ_DQS.
  4. [§VI.A and Algorithm 1] The low-level robot assignment is underspecified. Algorithm 1 says 'Assign robots to Lt for t ∈ T' and the text notes that each top-level robot is also in its own Lt, but the criterion for attaching a low-level robot to a particular top-level robot is never defined. Since the aggregation in Eq. (8) and Eq. (9) depends on the partition of robots into the sets Lt, this omission leaves the method ambiguous and prevents reproduction. The authors should state the assignment rule (e.g., random, by similarity, by co-location) and justify it.
minor comments (4)
  1. [Author affiliation] The affiliation contains a typo: 'Ume ˚a Unviersity' should be 'Ume ˚a University'.
  2. [Algorithm 2] The indentation of Algorithm 2 is ambiguous: the 'return wt' statement appears inside the inner batch loop in the typeset pseudocode, which would make the function return after the first batch. The intended control flow should be clarified with proper indentation.
  3. [Table I] The artificial split of Cornell into seven classes is introduced briefly, but it is not explained how these class labels are obtained from the original grasp annotations. A short clarification would improve reproducibility.
  4. [Table IV] The column headers '0 .25', '0 .1', and '0 .0' contain spacing artifacts that make the table hard to read.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core claim is an empirical comparison on held-out test sets, not a derivation equivalent to its inputs.

full rationale

The paper's central claim is that MTF-Grasp outperforms vanilla FL by up to 8% on quantity-skewed Cornell and Jacquard grasping datasets. This claim is evaluated empirically on test sets, so it is not a tautology or a construction-level identity. The top-level robot selection uses data-quality and data-quantity scores computed from the local data statistics, not from the test accuracy, so the selection mechanism is not fitted to the reported outcome. The lambda hyperparameters were chosen as 'worked best,' which is ordinary tuning rather than circular reasoning; it introduces a free parameter but does not make the prediction equivalent to the input. The paper's self-citations, such as to the authors' prior multi-tier FL work, are used for background context and are not load-bearing for the present experimental result. The unreported baseline local epochs and the lack of error bars are correctness, reproducibility, and fairness concerns, but they are not circularity: they do not show that the claimed improvement is true by definition or by fit. No step in the derivation chain reduces to its own inputs, so the paper is not circular.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The core method is purely empirical; there are no invented physical entities. The central claim rests on a small set of hand-chosen hyperparameters (lambda, j, epochs) and on untested modeling assumptions about how to simulate non-IID data and about which robots are good teachers.

free parameters (6)
  • lambda_DDS = 0.5
    Weights the data-quality score in Eq. 7; the paper states 'we found that the value 0.5 worked best for both' with no validation split or sensitivity analysis.
  • lambda_DQS = 0.5
    Weights the data-quantity score in Eq. 7; same tuning issue as lambda_DDS.
  • j = 2
    Number of top-level robots selected in all experiments; no sensitivity analysis or guidance for choosing it.
  • et = 5
    Local training epochs for top-level robots; no comparison to baseline local epochs, so the compute budget differs.
  • er = 15
    Local training epochs for low-level robots; much larger than typical FL local epochs, potentially explaining accuracy gains over unreported baseline.
  • E = 10
    Number of global communication rounds; fixed without justification.
assumptions (5)
  • domain assumption FedAvg aggregation weights client models by dataset size (Eq. 2, 8, 9).
    The paper uses weighted averaging as the standard FL aggregation and as the baseline for comparison.
  • domain assumption The grasping network from [33] is used as the base model without modification.
    The paper relies on the correctness and suitability of the SE2-equivariant grasping network from prior work.
  • ad hoc to paper The artificial class split of Cornell into seven categories (Table I) captures the class-based non-IID structure of real robot fleets.
    This split is used to simulate non-IID distributions; the choice of seven classes is inspired by FOGL and is not validated against real deployment data.
  • ad hoc to paper Robots with higher data quality (DDS) and quantity (DQS) scores are better initial trainers for low-data robots.
    This is the central design hypothesis of MTF-Grasp; the paper provides no proof beyond the reported experiments.
  • ad hoc to paper The beta-splitting procedure (a fraction beta of each class assigned to j robots) simulates quantity skew in the experiments.
    Used to generate the quantity-skewed data partitions; the mapping between beta and real-world skew is not established.

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Cite this review

Pith. "Pith review of MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping." pith.science (2026). https://pith.science/paper/D5757ODU

@misc{pith2026250710158,
  author       = {Pith},
  title        = {Pith review of: MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5757ODU}},
  note         = {Machine review of arXiv:2507.10158}
}
read the original abstract

Federated Learning (FL) is a promising machine learning paradigm that enables participating devices to train privacy-preserved and collaborative models. FL has proven its benefits for robotic manipulation tasks. However, grasping tasks lack exploration in such settings where robots train a global model without moving data and ensuring data privacy. The main challenge is that each robot learns from data that is nonindependent and identically distributed (non-IID) and of low quantity. This exhibits performance degradation, particularly in robotic grasping. Thus, in this work, we propose MTF-Grasp, a multi-tier FL approach for robotic grasping, acknowledging the unique challenges posed by the non-IID data distribution across robots, including quantitative skewness. MTF-Grasp harnesses data quality and quantity across robots to select a set of "top-level" robots with better data distribution and higher sample count. It then utilizes top-level robots to train initial seed models and distribute them to the remaining "low-level" robots, reducing the risk of model performance degradation in low-level robots. Our approach outperforms the conventional FL setup by up to 8% on the quantity-skewed Cornell and Jacquard grasping datasets.

Figures

Figures reproduced from arXiv: 2507.10158 by the authors.

Figure 1
Figure 1. An overview of the federated learning pipeline. can be transferred to robots for local inference [5]. While pro￾viding collaboration, cloud-based training risks privacy [6] and increases communication load, as it requires data transfer to the cloud. Federated learning (FL) [7] emerges as a promising middle ground, enabling collaboration while preserving data privacy. FL represents a decentralized training methodolog… view at source ↗
Figure 2
Figure 2. MTF-Grasp process. In (a), the robots send the calculated DDS score, and the server selects top-level robots after calculating IS for each robot. In (b), the server sends an initialized model to be trained in top-level robots. (c) The top-level robots then send the trained models to be trained in low-level robots. (d) A two-step aggregation is carried out. wr i+1 = wr i − η∇l(wr i , D r ) (1) where η denotes the lea… view at source ↗
Figure 3
Figure 3. Data division among clients in class-based non-IID and quantity-skewed non-IID experimental setups. To simulate the non-IID distribution of data, the dataset must be divided into classes, and while each object category can act as a class, this distribution would significantly dilute objects per class. Hence, we split the Cornell dataset into seven classes for the experiment’s purpose. The choice of the number of cla… view at source ↗

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Reference graph

Works this paper leans on

33 extracted references · 24 canonical work pages

  1. [1]

    Federated learning for large-scale cloud robotic manipulation: Opportunities and challenges,

    O. Zaland, C. Nguyen, F. T. Pokorny, and M. Bhuyan, “Federated learning for large-scale cloud robotic manipulation: Opportunities and challenges,” in 2025 IEEE International Conference on Machine Learn- ing and Cybernetics (ICMLC) . IEEE, 2025

    work page 2025

  2. [2]

    Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things

    H. Wang, X. Zhong, K. Liu, F. Liu, and W. Zhang, “Denoising and adaptive online vertical federated learning for sequential multi-sensor data in industrial internet of things,” arXiv preprint arXiv:2501.01693 , 2025

    work page Pith review arXiv 2025

  3. [3]

    Roco: Dialectic multi-robot col- laboration with large language models,

    Z. Mandi, S. Jain, and S. Song, “Roco: Dialectic multi-robot col- laboration with large language models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 286–299

    2024

  4. [4]

    A survey on learning-based robotic grasping,

    K. Kleeberger, R. Bormann, W. Kraus, and M. F. Huber, “A survey on learning-based robotic grasping,” Current Robotics Reports , vol. 1, pp. 239–249, 2020

    work page 2020

  5. [5]

    Privacy-preserving grasp planning in the cloud,

    J. Mahler, B. Hou, S. Niyaz, F. T. Pokorny, R. Chandra, and K. Goldberg, “Privacy-preserving grasp planning in the cloud,” in 2016 IEEE Inter- national Conference on Automation Science and Engineering (CASE) . IEEE, 2016, pp. 468–475

    work page 2016

  6. [6]

    Privacy risks in reinforcement learning for household robots,

    M. Li, W. Ding, and D. Zhao, “Privacy risks in reinforcement learning for household robots,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 5148–5154

    2024

  7. [7]

    Fed- erated learning: Strategies for improving communication efficiency,

    H. B. McMahan, F. Yu, P. Richtarik, A. Suresh, D. Bacon, et al., “Fed- erated learning: Strategies for improving communication efficiency,” in Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain , 2016, pp. 5–10

    work page 2016

  8. [8]

    Mitigating data heterogeneity with multi-tier federated gan,

    O. Zaland, Y . Onur, and M. Bhuyan, “Mitigating data heterogeneity with multi-tier federated gan,” in International Conference on Neural Information Processing. Springer, 2025, pp. 225–239

    work page 2025

Show all 33 references
  1. [9]

    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,” in 2020 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 11 785–11 792

    2020

  2. [10]

    One-shot federated learning with classifier-free diffusion models,

    O. Zaland, S. Jin, F. T. Pokorny, and M. Bhuyan, “One-shot federated learning with classifier-free diffusion models,” 2025

    2025

  3. [11]

    Fedcgsu: Client grouping based on similar uncertainty for non-iid federated learning,

    H. Liu, L. Feng, and M. Mei, “Fedcgsu: Client grouping based on similar uncertainty for non-iid federated learning,” in 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2024, pp. 1773–1778

    2024

  4. [12]

    Advances and open problems in federated learning,

    P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021

    2021

  5. [13]

    Approaches to address the data skew problem in federated learning,

    D. C. Verma, G. White, S. Julier, S. Pasteris, S. Chakraborty, and G. Cir- incione, “Approaches to address the data skew problem in federated learning,” in Artificial Intelligence and Machine Learning for Multi- Domain Operations Applications , vol. 11006. SPIE, 2019, pp. 542– 557

    2019

  6. [14]

    Federated optimization in heterogeneous networks,

    T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V . Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems , vol. 2, pp. 429–450, 2020

    2020

  7. [15]

    Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

    J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V . Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,” Advances in neural information processing systems , vol. 33, pp. 7611–7623, 2020

    2020

  8. [16]

    Scaffold: Stochastic controlled averaging for federated learn- ing,

    S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learn- ing,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143

    2020

  9. [17]

    Federated learning on non-iid data silos: An experimental study,

    Q. Li, Y . Diao, Q. Chen, and B. He, “Federated learning on non-iid data silos: An experimental study,” in 2022 IEEE 38th ICDE . IEEE, 2022, pp. 965–978

    2022

  10. [18]

    An experimental study of data heterogeneity in federated learning methods for medical imaging,

    L. Qu, N. Balachandar, and D. L. Rubin, “An experimental study of data heterogeneity in federated learning methods for medical imaging,” arXiv preprint arXiv:2107.08371 , 2021

    2021 arXiv

  11. [19]

    Fedccea: A practical approach of client contribution evaluation for federated learning,

    S. K. Shyn, D. Kim, and K. Kim, “Fedccea: A practical approach of client contribution evaluation for federated learning,” arXiv preprint arXiv:2106.02310, 2021

    2021 arXiv

  12. [20]

    Dynamic margin for federated learning with imbalanced data,

    X. Ran, L. Ge, and L. Zhong, “Dynamic margin for federated learning with imbalanced data,” in IJCNN. IEEE, 2021, pp. 1–8

    2021

  13. [21]

    Self-balancing federated learning with global imbalanced data in mobile systems,

    M. Duan, D. Liu, X. Chen, R. Liu, Y . Tan, and L. Liang, “Self-balancing federated learning with global imbalanced data in mobile systems,”IEEE Trans. on Parallel and Distributed Systems , vol. 32, no. 1, pp. 59–71, 2020

    2020

  14. [22]

    Swarm deep reinforcement learning for robotic manipulation,

    X. Zhu, F. Zhang, and H. Li, “Swarm deep reinforcement learning for robotic manipulation,” Procedia Computer Science , vol. 198, pp. 472– 479, 2022

    2022

  15. [23]

    Federated reinforcement learning for collective navigation of robotic swarms,

    S. Na, T. Rou ˇcek, J. Ulrich, J. Pikman, T. s Krajn ´ık, B. Lennox, and F. Arvin, “Federated reinforcement learning for collective navigation of robotic swarms,” IEEE Trans on Cognitive and Developmental Systems , 2023

    2023

  16. [24]

    Federated imitation learning: A novel framework for cloud robotic systems with heterogeneous sensor data,

    B. Liu, L. Wang, M. Liu, and C.-Z. Xu, “Federated imitation learning: A novel framework for cloud robotic systems with heterogeneous sensor data,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3509– 3516, 2020

    2020

  17. [25]

    Grasp moduli spaces

    F. T. Pokorny, K. Hang, and D. Kragic, “Grasp moduli spaces.” in Robotics: Science and Systems , 2013

    2013

  18. [26]

    Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,

    M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” inICRA. IEEE, 2021, pp. 13 459–13 466

    2021

  19. [27]

    End-to-end learning to grasp via sampling from object point clouds,

    A. Alliegro, M. Rudorfer, F. Frattin, A. Leonardis, and T. Tommasi, “End-to-end learning to grasp via sampling from object point clouds,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 9865–9872, 2022

    2022

  20. [28]

    Fed-hanet: Federated visual grasping learning for human robot handovers,

    C.-I. Huang, Y .-Y . Huang, J.-X. Liu, Y .-T. Ko, H.-C. Wang, K.-H. Chiang, and L.-F. Yu, “Fed-hanet: Federated visual grasping learning for human robot handovers,” IEEE Robotics and Automation Letters , 2023

    2023

  21. [29]

    Fogl: Federated object grasping learning,

    S.-K. Kang and C. Choi, “Fogl: Federated object grasping learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 5851–5857

    2023

  22. [30]

    Deep learning for detecting robotic grasps,

    I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, 2015

    2015

  23. [31]

    Jacquard: A large scale dataset for robotic grasp detection,

    A. Depierre, E. Dellandr ´ea, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 3511–3516

    2018

  24. [32]

    Shapenet: An information- rich 3d model repository,

    A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su,et al., “Shapenet: An information- rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015

    2015 arXiv

  25. [33]

    Sam- ple efficient grasp learning using equivariant models,

    X. Zhu, D. Wang, O. Biza, G. Su, R. Walters, and R. Platt, “Sam- ple efficient grasp learning using equivariant models,” arXiv preprint arXiv:2202.09468, 2022

    2022 arXiv

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Reviewed August 6, 2026 · model on record in the stance chip above.