Cooperative Grasping for Collective Object Transport in Constrained Environments

David Alvear; George Turkiyyah; Shinkyu Park

REVIEW 3 major objections 4 minor 22 references

Cooperative Grasping for Collective Object Transport in Constrained Environments

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Two-robot object transport can be planned by ranking grasp pairs with a learned embedding, so only a few candidates need a full trajectory check.

desk verdict A useful learned pre-filter for two-robot grasp selection, with a solid but incomplete evaluation that hinges on the planner as oracle. read the letter →

arxiv 2509.03638 v1 pith:PICNNQGU submitted 2025-09-03 cs.RO cs.MA

David Alvear , George Turkiyyah , Shinkyu Park This is my paper · ORCID

classification cs.ROcs.MA

keywords cooperativegraspingmulti-robotobjecttransportconditionalembeddingmodelgraspconfigurationselectionconstrainedenvironmentsnegativesamplingmobilemanipulatorsaffinitymatrix

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 tries to show that deciding where two robots should grasp an object for transport through obstacles can be treated as a retrieval problem rather than an exhaustive planning problem. The authors build a Conditional Embedding (CE) model—two neural networks that embed a center grasp and a context grasp into one shared vector space—and train it so that the dot-product similarity of a pair predicts whether an optimization-based trajectory planner would find a feasible transporting path. On the held-out test set, ranking by embedding similarity finds at least one feasible pair in 83.05% of scenarios with the top-1 candidate and 99.22% with the top-5, compared with 41.11% for random selection; on three novel objects in an unseen environment it reaches 91.11% top-5. The paper further demonstrates the selected grasps on physical two-robot platforms carrying a bar and an L-shaped panel through a narrow corridor. The payoff is that the expensive trajectory feasibility check is run only for a small constant number of top-ranked candidates instead of every pair.

What carries the argument

The central object is the Conditional Embedding model: a center embedding network and a context embedding network that map grasp configurations, object geometry, and environment map into unit vectors in a shared space. The load-bearing mechanism is the negative-sampling training objective, which converts the softmax over all possible context grasps into a binary classification of feasible positives versus uniformly sampled infeasible negatives. At inference the mechanism is the affinity matrix of all center-context dot products; the top-k entries are the model's recommended grasp pairs, with the trajectory planner used only as a final verifier.

What would settle it

Generate a fresh set of, say, 100 unseen environments and object shapes, label every grasp pair with the paper's own trajectory planner, and check the top-5 success rate; if it falls well below the reported 91.11%, the embedding ranking is not capturing planner feasibility as claimed. A more direct test: find one scenario where a planner-feasible pair exists but is ranked below dozens of infeasible pairs, which would falsify the claim that embedding similarity orders feasibility.

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Extended reading notes

Core claim

The paper's central claim is that the feasibility score S(Gcenter, Gcontext)—a binary label produced by a trajectory planner—can be approximated by the dot product of two learned unit-norm embeddings. The architecture assigns one network to the 'center' grasp and another to the 'context' grasp, and training maximizes a negative-sampling log-likelihood that encourages feasible pairs to have high similarity while pushing infeasible negative samples apart. At inference the model builds an affinity matrix A = Ecenter Econtext^T for all candidate pairs and returns the top-k entries as recommended grasp configurations; only those few are then validated by the planner. The paper reports that this r

Load-bearing premise

The paper's central results depend on the assumption that the single trajectory planner used to label every grasp pair gives a correct yes/no answer about whether two robots can actually transport the object; if that planner is wrong in either direction, the learned model inherits the error and the reported success rates are measured against that same planner's decisions.

Editorial extensions

If this is right

On the held-out test set, top-5 retrieval finds a feasible grasp pair in 99.22% of scenarios, with top-1 alone at 83.05%, versus 41.11% for random selection (Table II).
On three novel objects in an unseen six-table environment, top-5 success is 91.11% and top-1 is 73.33%, versus 45.56% random (Table IV).
Because only the top-k pairs are sent to the planner, the number of expensive feasibility checks drops from all O(m^2) candidate pairs to a small constant k.
The same embedding objective extends to N robots by averaging the context embeddings and maximizing their dot product with the center embedding, providing a route beyond two-robot transport.
Physical experiments on two mobile manipulators show a straight bar and an L-shaped panel being carried through a 65 cm narrow corridor using the top-affinity grasp pair.

Reading between the lines

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

If embedding similarity truly tracks planner feasibility, the same ranking could be retrained against any other feasibility oracle—a more global planner, or physical trial outcomes—and would then approximate transportability directly rather than one planner's output.
The paper's own numbers show performance drops when candidate grasp configurations are scarce (the table location with 149 candidates has 63.3% success), suggesting a geometric limit: when feasible pairs are rare, top-k ranking has less room to be right. This points to regrasping or pre-moving the object to open space as a principled remedy.
Since the affinity score is a dot product between unit vectors, the learned embedding space may encode more than feasibility—for instance, geometric compatibility of grasp positions—which could enable compositional queries such as ranking N-robot grasp tuples by averaging context embeddings without retraining the networks.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this paper does something genuinely useful—it learns to rank two-robot grasp pairs for constrained transport tasks using a conditional embedding model trained with negative sampling, and it evaluates on held-out objects and environments. The main caveat is that the labels come from a single trajectory planner, so the reported success rates are agreement rates with that planner, not physical transportability.

The adaptation of skip-gram-style negative sampling to grasp feasibility is new as far as I can tell, and the architecture (dual encoders with a Bi-GRU context network) is non-trivially adapted. The evaluation is honest: a real 70/20/10 split, a separate 180-scenario generalization test with objects never seen in training, and the numbers are internally consistent. The physical demos, while anecdotal, show the pipeline works end-to-end.

The soft spots are the ones you'd expect. The planner in Appendix B is a local optimization over IRIS-NP polytopes with manually chosen seeds, so its 'feasible' labels can be wrong in both directions. Since the same planner provides training labels and test ground truth, the absolute success rates (83–99% top-k) may not transfer to real robots. The paper does not address this. There are also no confidence intervals, the only baseline is random choice, and no code or data is released. The authors do flag the Table 2 case (63.3% success in a tightly constrained setting), but they attribute it to limited grasp options, which is plausible but not the only reading.

Still, the central claim—that a learned embedding can replace exhaustive trajectory-planning checks for pre-filtering grasp pairs—holds up as a computational speedup. For a reader interested in multi-robot manipulation or learning-based grasp selection, this is a worthwhile paper. It deserves peer review; I'd send it out rather than desk-reject. I'd ask the authors for stronger baselines, confidence bounds, and ideally some analysis of how sensitive the results are to planner noise.

Referee Report

3 major / 4 minor

Summary. The paper proposes a Conditional Embedding (CE) framework for selecting two-robot grasp configurations in object-transport tasks through constrained environments. The feasibility of a grasp pair is defined by a binary metric S(Gcenter, Gcontext) that is computed by a trajectory planner (Appendix B, Eq. (6)). Two neural networks embed the center and context grasp configurations; a dot-product affinity matrix ranks all pairs, and the top-k pairs are returned. The model is trained with negative sampling on 576 planner-labeled samples and evaluated on a held-out 10% test split and on 180 scenarios with three novel objects and an unseen environment. The paper reports top-1/3/5 retrieval success rates of 83.05%/96.39%/99.22% on the held-out split and 73.33%/89.44%/91.11% in the generalization test, with a random baseline for comparison. A small physical demonstration with two Dingo-O mobile manipulators is described qualitatively, and a multi-robot extension is outlined.

Significance. If the reported results are taken at face value, the paper provides a practical learned surrogate for exhaustive trajectory-planning checks in cooperative grasping: the CE model can retrieve a feasible grasp pair among the top-5 candidates with high probability while avoiding the O(m^2) planner evaluations. The held-out test split and the novel-object/unseen-environment generalization test are genuine out-of-sample evaluations, and the numbers in Tables II-IV are internally consistent. The negative-sampling formulation is a reasonable way to scale the softmax over a large candidate set. However, the absolute claims of 'reliably identify feasible grasp configurations' and 'practical applicability' depend critically on the reliability of the trajectory planner that generates the labels. That planner is a local optimization over manually seeded convex regions, and no quantitative physical validation is provided. The significance is therefore conditional on the oracle's fidelity, which the current manuscript does not establish.

major comments (3)

[§II-A, Appendix B (Eq. 6), §IV-A] The binary feasibility labels S(Gcenter, Gcontext) are defined as the outcome of a single trajectory planner: IPOPT over B\'ezier curves inside manually seeded IRIS-NP polytopes. The same planner generates the training labels (Section IV-A) and serves as the test oracle for Tables II-IV. Consequently, the reported top-k success rates quantify agreement with this particular planner, not physical transportability. A false negative (the local optimization fails for a pair that is actually transportable) would remove feasible pairs from both the training set and the test oracle, and a false positive is possible because constraints (6b)-(6d) do not model contact stability, gripper slip, or controller tracking error. This concern is concrete: the Table-2 result of 63.3% success is interpreted as 'few feasible grasp options,' but it is equally consistent with the oracle missing feasible pairs.
[§IV-B, Tables II-IV] The evaluation lacks uncertainty quantification. The held-out test set contains roughly 58 samples (10% of 576); a top-1 success rate of 83.05% has a 95% binomial confidence interval of approximately [71%, 92%], and the 99.22% top-5 rate is compatible with values as low as roughly 95%. The generalization set (180 scenarios) is better, but the per-table breakdowns are based on only 30 scenarios each, so the 63.3% Table-2 figure has a very wide interval. Reporting confidence intervals (or at least exact binomial intervals) and rerunning with multiple random seeds would substantially strengthen the paper. The authors should also state explicitly that the 'success rate' in Tables II and IV is the fraction of scenarios where at least one feasible pair appears in the top-k, not the end-to-end transport success rate; the current wording, especially in Table IV and the conclusion, can be misread
[§IV-D] The physical experiments are presented qualitatively with snapshots only (Fig. 10). There are no trial counts, success/failure rates, or a comparison between the grasps selected by the CE model and the labels produced by the trajectory planner. Given that the central claims concern 'practical applicability' and that all quantitative success rates are planner-based, this section is not yet sufficient to support the absolute physical-validity claim. A small quantitative study—e.g., a fixed number of runs per object, reporting the fraction of successful transports, and at least a few cases where the planner said feasible and the physical system failed—would directly address the oracle-fidelity concern raised in Major Comment 1.

minor comments (4)

[§III-B, Eq. (4) and Eq. (5)] Equation (4) presents the negative-sampling objective as a product over negative samples, but the implemented loss in Eq. (5) uses a weighted sum with weights |DC(Gcenter)|/|N(Gcenter)|. The relationship between these two forms should be explained, and the notation p(Gcenter, G) should be introduced before Eq. (5).
[§III-D] The extension to N-robot transport is described only as a formulation, with no experiments or even a proof-of-concept. It should either be labeled as future work or accompanied by at least a simple validation; otherwise it reads as a claim without support.
[§II-B] The brute-force complexity statement says the number of evaluations is m!/(m-N)!, which for N=2 is m(m-1), i.e., O(m^2). This is fine, but the formula is unnecessary and could be removed for clarity.
[§IV-C, Fig. 9] The positive correlation between the number of candidate grasp configurations and success rate should be quantified (e.g., Spearman correlation) rather than only described visually; the sample size of six table locations is small.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the CE model is a supervised surrogate for the trajectory-planner oracle, evaluated on held-out labels and checked by physical experiments; oracle noise is a validity concern, not a circular derivation.

full rationale

The paper's derivation chain is a standard supervised-learning pipeline: the binary feasibility metric S(Gcenter, Gcontext) is defined by the trajectory planner in Appendix B (Eq. 6); the CE model is trained on S labels generated by that planner; and the model's top-k retrieval performance is evaluated against held-out S labels. This is not circular because the training and test sets are disjoint, the model does not use S at inference, and the reported success rates measure generalization to unseen objects/environments under the same oracle. The physical experiments in Section IV-D provide an independent, if qualitative, external check. The main weakness—that the planner is a local optimization and may produce false positives/negatives relative to physically realizable transport—is a correctness or external-validity threat, not a circularity of the paper's own derivations. No load-bearing self-citations, imported uniqueness claims, or ansatz-smuggling-via-citation are present. The same-oracle training and evaluation is appropriate for the claimed goal of replacing exhaustive trajectory-planning checks with a learned embedding ranking.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

Free-parameter count is dominated by hand-tuned hyperparameters and planner weights that change which grasp pairs are labeled feasible. No new physical entities are introduced; the CE model is a supervised function approximator rather than a postulated mechanism. The most consequential ledger item is the planner-as-oracle assumption, which loads a single local-optimization pipeline with the role of ground truth for both training and evaluation.

free parameters (7)

embedding dimension d = 44
Tuned via Bayesian optimization on validation performance; controls capacity of the embedding space.
temperature tau = 6.15e-2
Sigmoid sharpness in (3), tuned via Bayesian optimization.
scaling factor alpha = 1.10
Sets negative sample budget K = alpha * max|DC|; tuned on validation.
classification threshold = 0.64
Applied to sigmoid output of (3) in discriminative evaluation; selected by maximizing F1 score on validation data.
formation consistency weight w_F = 20.0
Weight on the formation-consistency term in the trajectory planner objective (6a); set by hand, and it changes which pairs are labeled feasible.
grasp sampling radius r = 0.55 m
Distance from grasp point at which base positions are sampled (Appendix A); chosen by hand and controls the candidate set G.
number of sampled base positions per grasp point = 60
Sampling density for candidate base positions in Appendix A; chosen without sensitivity analysis.

assumptions (6)

domain assumption The trajectory planner's success/failure output is a correct and consistent oracle for physical transportability.
Invoked when S is defined in Section II-A as the outcome of the planner in Appendix B; if false, training labels and evaluation ground truth are both corrupted.
domain assumption The 2D top-down model with yaw-only object rotation and fixed gripper height captures the essential transport task.
Section II states gripper operates at fixed height and the object rotates only about its yaw axis; controls on z are discarded.
domain assumption PyBullet physics simulations faithfully model grasp stability and collisions during transport.
Dataset labels are generated in PyBullet (Section IV-A); no validation against alternative physics engines is reported.
domain assumption Grasp points F are predefined on the object boundary and are graspable from any direction.
Section II-A defines F as a set of predefined grasp points; the method does not discover grasp points on arbitrary geometry.
standard math Negative sampling with a constant sample budget K approximates the full softmax objective well enough for accurate ranking.
Inherited from Word2Vec (Section III-B); the paper uses it without a formal guarantee, only citing prior NLP practice.
domain assumption The IRIS-NP convex decomposition with manually selected seeds covers all relevant robot and object configurations.
Appendix B relies on manually seeded polytopes; poor seeds would produce false infeasibility labels.

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Cite this review

Pith. "Pith review of Cooperative Grasping for Collective Object Transport in Constrained Environments." pith.science (2026). https://pith.science/paper/PICNNQGU

@misc{pith2026250903638,
  author       = {Pith},
  title        = {Pith review of: Cooperative Grasping for Collective Object Transport in Constrained Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PICNNQGU}},
  note         = {Machine review of arXiv:2509.03638}
}

read the original abstract

We propose a novel framework for decision-making in cooperative grasping for two-robot object transport in constrained environments. The core of the framework is a Conditional Embedding (CE) model consisting of two neural networks that map grasp configuration information into an embedding space. The resulting embedding vectors are then used to identify feasible grasp configurations that allow two robots to collaboratively transport an object. To ensure generalizability across diverse environments and object geometries, the neural networks are trained on a dataset comprising a range of environment maps and object shapes. We employ a supervised learning approach with negative sampling to ensure that the learned embeddings effectively distinguish between feasible and infeasible grasp configurations. Evaluation results across a wide range of environments and objects in simulations demonstrate the model's ability to reliably identify feasible grasp configurations. We further validate the framework through experiments on a physical robotic platform, confirming its practical applicability.

Figures

Figures reproduced from arXiv: 2509.03638 by the authors.

**Figure 2.** (a) A collective object transport task involving two mobile manipula [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Grasp configuration pair selection using the CE model: Candidate grasp configurations for two mobile manipulators are generated (left block). Two [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Architectures for (a) the center embedding network and (b) the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 5.** Figure 5: Environments for collective object transport, featuring two table [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 7.** Figure 7: (a) Three additional objects with diverse geometries used for [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: Object transport success rates by tables and objects. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Object transport success rates in relation to the number of candidate [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

**Figure 10.** Figure 10: Cooperative object transport using two Clearpath Dingo-O mobile manipulators, each equipped with a Kinova Gen3-lite arm. Top row: The robots transport a straight bar through a narrow corridor connecting two areas. Bottom row: The robots carry an L-shaped payload. Each…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Multi-robot cooperative formation for overweight object transportation,

G. Eoh, J. D. Jeon, J. S. Choi, and B. H. Lee, “Multi-robot cooperative formation for overweight object transportation,” in 2011 IEEE/SICE International Symposium on System Integration (SII) , Dec 2011, pp. 726–731

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[2] [2]

Planning multi-robot grasping motions,

N. Vahrenkamp, E. Kuhn, T. Asfour, and R. Dillmann, “Planning multi-robot grasping motions,” in 2010 10th IEEE-RAS International Conference on Humanoid Robots , Dec 2010, pp. 593–600

work page 2010

[3] [3]

Grasp planning for load sharing in collaborative manipulation,

U. Tariq, R. Muthusamy, and V . Kyrki, “Grasp planning for load sharing in collaborative manipulation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 6847– 6854

work page 2018

[4] [4]

Decentralized approaches for coopera- tive grasp planning,

R. Muthusamy and V . Kyrki, “Decentralized approaches for coopera- tive grasp planning,” in2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) , Dec 2014, pp. 693–698

work page 2014

[5] [5]

Task specific cooperative grasp planning for decentralized multi-robot systems,

R. Muthusamy, C. P. Bechlioulis, K. J. Kyriakopoulos, and V . Kyrki, “Task specific cooperative grasp planning for decentralized multi-robot systems,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015, pp. 6066–6073

work page 2015

[6] [6]

Strictly decentralized ap- proaches for multi-robot grasp coordination,

R. Muthusamy, V . Kyrki, P. K. Muthusamy, T. Taha, I. Hussain, Y . Zweiri, D. Prattichizzo, and D. Gan, “Strictly decentralized ap- proaches for multi-robot grasp coordination,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Aug 2023, pp. 1–8

work page 2023

[7] [7]

Multi- agent manipulation via locomotion using hierarchical sim2real,

O. Nachum, M. Ahn, H. Ponte, S. S. Gu, and V . Kumar, “Multi- agent manipulation via locomotion using hierarchical sim2real,” in Proceedings of the Conference on Robot Learning , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 30 Oct–01 Nov 2020, pp. 110– 121

work page 2020

[8] [8]

Occlusion-based cooperative transport with a swarm of miniature mobile robots,

J. Chen, M. Gauci, W. Li, A. Kolling, and R. Groß, “Occlusion-based cooperative transport with a swarm of miniature mobile robots,” IEEE Transactions on Robotics , vol. 31, no. 2, pp. 307–321, 2015

work page 2015