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REVIEW 3 major objections 5 minor 65 references

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that planning to many grasp targets first and then scoring the terminal poses of the planned trajectories raises grasp success over the standard generate-score-plan order across generators, planners, and real-world scenes.

desk verdict A clean plan-then-evaluate inversion with a large empirical win, but the evaluator's off-target scoring is under-validated and the real-world comparison needs matched attempts. read the letter →

arxiv 2509.07162 v2 pith:545E2ZPZ submitted 2025-09-08 cs.RO

Martin Matak , Mohanraj Devendran Shanthi , Karl Van Wyk , Tucker Hermans This is my paper · ORCID
classification cs.RO
keywords robotgraspingmulti-fingeredhandgraspsuccessevaluationvectorizedmotionplanninggenerate-evaluate-plansim-to-realtransferpointcloudparalleltrajectoryoptimization
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 is trying to establish a simple reordering for learning-based robot grasping: instead of generating grasp candidates, scoring them, and then planning a trajectory to the best-scoring one, a robot should plan trajectories to many candidates in parallel and only then score the grasp that each planned trajectory actually reaches. The claim is that the terminal configuration of a real planned motion, not the originally proposed target pose, is the object that should be evaluated for grasp success. The paper argues this removes a lose-lose tradeoff in the standard pipeline, where either the planner repeatedly reoptimizes toward lower-ranked grasps or relaxes its accuracy threshold and degrades the success estimate. In simulation the reordering improves success consistently across three generator architectures and two motion planners, and in real-world tests on eleven novel objects it raises success from 22% with the baseline to 80%, while also working on shelves and at different table heights.

What carries the argument

The mechanism that carries the argument is the vectorized motion planner, a planner that can solve many trajectory optimizations in parallel on a GPU; the paper uses Curobo and geometric fabrics for this role, and it is what makes planning to every proposed target cheap rather than sequential. The second component is the learned evaluator, a network trained on 28.9 million full-robot simulated grasp attempts, including hard negatives, that takes the object point cloud and a grasp and outputs a success likelihood; in FPTE the grasp fed to it is extracted from the last timestep of each planned trajectory, not from the generator's output. The paper deliberately drops the distance-to-target criterion, which is the load-bearing design choice: terminal poses that miss their targets are ranked and can be executed if the evaluator judges them likely to succeed, so planned trajectories are never discarded for failing to reach an arbitrary goal threshold.

What would settle it

Run FPTE in simulation with the goal tolerance deliberately widened so many planned trajectories terminate far from their targets, then compare evaluator scores against actual lift success separately for the off-target subset, without filtering out approach collisions; if the evaluator is no better than chance on that subset, the claimed advantage of terminal-configuration scoring collapses.

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Extended reading notes

Core claim

The paper's central discovery is that the traditional generate-evaluate-plan order is the source of a large, systematic grasp failure: generated targets are grounded in the object frame and often unreachable or colliding in the robot frame, so planning to the top-ranked target frequently fails, the pipeline replans to lower-ranked targets, and even when a relaxed planner reaches something close, it evaluates proximity to an idealized target rather than the quality of the actual end pose. FPTE, First Plan Then Evaluate, inverts this: a vectorized motion planner plans trajectories to all generated grasp targets simultaneously, and a learned evaluator scores the success likelihood of the terminal hand pose and configuration of each trajectory, ignoring distance to the target. The paper reports that this substitution yields higher predicted and actual success across all tested generator architectures (cVAE, diffusion, and MDN), both tested motion planners (Curobo and geometric fabrics), and 20 object shapes in simulation; in the real world, the predicted success of executed grasps averaged 90% versus 26% for the baseline, and actual success was 80% versus 22%, with the difference driven both by the baseline failing to find trajectories and by executing lower-quality grasps when it did.

Load-bearing premise

The load-bearing premise is that the learned success evaluator, trained only in simulation on single isolated objects on a fixed-height table, remains accurate when it scores real terminal configurations reached at different table heights, in shelves, under clutter, and at positions far from the originally proposed grasps.

Editorial extensions

If this is right

  • Grasp success no longer depends on the generator proposing reachable grasps, since every proposed target is planned to and the terminal pose is what gets scored; this relaxes the burden on grasp generation.
  • The pipeline avoids sequential replanning through a ranked list; in the real-world baseline 23 of 55 attempts found no trajectory at all, whereas the parallel batch always provided an executable candidate.
  • The evaluator's calibration on terminal configurations becomes the central bottleneck, so any improvement in full-robot grasp-attempt data or evaluator training should translate directly into higher success.
  • Execution can be gated on the evaluator's confidence: the reported precision-recall analysis indicates that refusing grasps below a high predicted-likelihood threshold would raise success further.
  • The ordering benefit transfers across planner choices, generator architectures, and unseen environments without retraining the learned models, including novel objects, tables, shelves, and cluttered scenes.

Reading between the lines

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

  • The same plan-then-evaluate principle could apply to other robot manipulation skills where the planner's terminal state, not the commanded goal, determines outcome quality; for example placing, pushing, or tool use with loose goal tolerance.
  • Because the evaluator is the only judge of off-target terminal poses, the paper's reported real-world numbers are an upper bound on what the current evaluator supports; training the evaluator on varied table heights and clutter, rather than single isolated objects, would test whether the transferred success is limited by the evaluator's distribution.
  • An extension the paper does not run is FPTE with a purely geometric or analytical success score instead of the learned evaluator; if the ordering benefit persists, that would show the advantage comes from the plan-first order itself, not from the evaluator's learning.
  • The 90% average predicted likelihood for executed grasps suggests a practical intervention the paper mentions but does not deploy: a high-confidence execution gate with human handoff for low-confidence grasps could push system-level reliability well above the reported 80%.
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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

3 major / 5 minor

Summary. The paper introduces FPTE (First Plan, Then Evaluate), a dexterous grasping pipeline that first uses a generator to propose a batch of target grasps, plans trajectories to all targets in parallel with a vectorized motion planner, and then uses a learned evaluator to score the grasp at each trajectory's terminal configuration, executing the highest-scoring trajectory. This reverses the standard generator-evaluator-planner order and explicitly disregards distance to the planned target. The authors report consistent simulation improvements across three generators and two motion planners, and a real-world success rate of 80% versus 22% for the baseline on 11 novel objects, with demonstrations in cluttered scenes and varied table/shelf heights.

Significance. If the claimed results hold, FPTE is a practically valuable reordering of a common grasping pipeline and a compelling application of vectorized planning, with a plausible explanation for why target-distance rejection discards successful grasps. The paper's strengths include a large simulated dataset (28.9M grasp attempts), systematic variation of generators and planners, and real-world generalization to novel objects and environments. However, the central evidence depends on unquantified out-of-distribution evaluator inputs and on a real-world comparison with asymmetric planning budgets; these issues need to be addressed before the quantitative claims are fully supported.

major comments (3)
  1. [Section III.A and III.B] At runtime, the evaluator receives grasp triples (T_OH, theta_p, theta_g) in which the terminal pose and pre-grasp configuration come from the planned trajectory, but theta_g is taken from the original target grasp. In training, theta_g is planned for the actual terminal pre-grasp configuration via contact optimization, as described in Section III.B. The exact inputs that FPTE ranks, especially for off-target trajectories, are therefore out of the training distribution. Because FPTE's core claim is that the evaluator can grade these terminal states without the target-distance criterion, the paper should provide direct evidence of this transfer, such as a distributional comparison, an unfiltered evaluation set, or retraining/conditioning the evaluator with candidate-specific theta_g values.
  2. [Section IV.B, Figure 8] The real-world precision-recall curve is computed after filtering out samples where the robot hit the object during the approach to the pre-grasp configuration. Collisions during approach are a natural failure mode for off-target trajectories, so filtering them can materially inflate the apparent quality of the evaluator's ranking on the full candidate set. Please report the PR curve on unfiltered data and, if possible, separately for trajectories that reach the target versus those that do not.
  3. [Section IV.B] The headline real-world comparison of 80% versus 22% is confounded: the baseline is capped at three planning attempts, while FPTE plans to 512 targets in parallel and then executes the best one. The 23 out of 55 baseline failures to find a trajectory may be partly an artifact of this budget. Even the executed-only 38% success rate lacks confidence intervals or a per-object/per-location breakdown, so the magnitude of the improvement is not established. Please report per-condition counts with binomial confidence intervals, or run a baseline with a comparable planning budget.
minor comments (5)
  1. [Section IV.A, Figure 4] The simulation results in Figure 4 are reported without error bars or the number of repeated trials per condition; adding trial counts and variance estimates would make the cross-generator and cross-planner claims easier to assess.
  2. [Section IV.B] The sentence "We model all the detected objects and the environment (hand-coded) as collisions for the motion planner" appears verbatim twice in Section IV.B; one occurrence should be removed.
  3. [Section IV.B] The phrase "with the promptobject that can be picked up with one hand" appears to contain a missing-space typo; please correct it to "with the prompt 'object that can be picked up with one hand'."
  4. [Section III.A] The notation for resulting grasps G_R_OH and target grasps G_T_OH is dense; a short symbol table or a clarifying sentence would improve readability.
  5. [Section V] The limitations paragraph names object-shape diversity and segmentation inaccuracy but does not acknowledge the evaluator distribution shift discussed above; this should be added to the limitations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FPTE's success labels come from physics simulation and real execution, and the plan-then-evaluate ordering is tested against an external baseline.

full rationale

I examined the claimed derivation chain: FPTE proposes planning to all generator targets in parallel, then scoring terminal configurations with a learned evaluator, and executing the highest-scoring trajectory. The success labels used to train the evaluator come from physical simulation (Isaac Sim) full-robot grasp attempts and from real executed grasps, not from the evaluator's own outputs or from the FPTE selection rule. The baseline is the standard generator-evaluator-planner loop with Curobo thresholds, an external comparison. The vectorized planners (Curobo, Fabrics) are code-reproduced external tools despite author overlap, so citing them is shared engineering, not circular. The evaluator's predictions are validated against measured success rates in Figure 8 and in the real-world experiment, making the central result externally falsifiable. The paper openly states limitations such as limited object shape diversity and segmentation inaccuracy, and it discloses the Figure 8 filtering of approach collisions; these are correctness or statistical-reporting concerns, not circular reductions. No equation or parameter is defined in terms of the target result, and no self-citation is load-bearing in the derivation. Therefore the paper exhibits no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities or conserved quantities. Its central load-bearing assumptions are about evaluator transfer, the validity of scoring terminal configurations, the existence of at least one good planned trajectory, and the simulation success criterion. The two free parameters listed affect the baseline comparison and the evaluator training, respectively.

free parameters (2)
  • Baseline trajectory acceptance thresholds = 0.005 m position; 14 degrees per axis rotation
    Set to Curobo defaults in Section IV.A. These thresholds define which planned trajectories count as successful in the traditional baseline, and the paper's critique that useful trajectories are discarded depends on this choice; a looser threshold would change the comparison.
  • Hard-negative perturbation magnitudes = ±5 cm and ±60 degrees per axis
    Used in Section III.A to create negative training samples for the evaluator. The evaluator's ability to discriminate near-successful grasps depends on this augmentation choice.
assumptions (4)
  • domain assumption The evaluator, trained on simulated full-robot grasp attempts on a single isolated object on a fixed table, generalizes to real-world terminal configurations including shelves, clutter, and different table heights.
    Invoked in Section IV.B. The real-world PR curve and the reported 80% success rate depend on this transfer, and the paper provides empirical evidence but no formal guarantee.
  • domain assumption The grasp at a trajectory's terminal configuration, assembled from the terminal pre-grasp pose and the target in-grasp joint configuration, adequately represents the grasp the robot will execute.
    Section III.A constructs resulting grasps by combining the terminal pose and pre-grasp configuration with the target in-grasp configuration. If the trajectory stops far from the target, the target in-grasp configuration may be inconsistent with the terminal pose.
  • domain assumption For each tested object, the generated grasp batch contains at least one target for which the vectorized planner produces a trajectory whose terminal configuration is graspable.
    The method selects only among planned trajectories, so if no planned trajectory ends in a good grasp, FPTE cannot improve over the baseline. This is an existence condition not proven for arbitrary objects.
  • domain assumption The simulation labels of grasp success, requiring at least two fingertips in contact with the thumb among them, plus lifting, are a valid proxy for real-world grasp success.
    This success criterion is used throughout data collection in Section III.B and is the ground truth for training the evaluator, so any mismatch between this criterion and physical task success propagates through the pipeline.

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Cite this review

Pith. "Pith review of First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines." pith.science (2026). https://pith.science/paper/545E2ZPZ

@misc{pith2026250907162,
  author       = {Pith},
  title        = {Pith review of: First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/545E2ZPZ}},
  note         = {Machine review of arXiv:2509.07162}
}
read the original abstract

Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initialization and are potentially time-consuming. As an alternative, the generator-evaluator-planner framework has been proposed. A generator generates grasp candidates, an evaluator ranks the proposed grasps, and a motion planner plans a trajectory to the highest-ranked grasp. If the planner doesn't find a trajectory, a new trajectory optimization is started with the next-best grasp as the target and so on. However, executing lower-ranked grasps means a lower chance of grasp success, and multiple trajectory optimizations are time-consuming. Alternatively, relaxing the threshold for motion planning accuracy allows for easier computation of a successful trajectory but implies lower accuracy in estimating grasp success likelihood. It's a lose-lose proposition: either spend more time finding a successful trajectory or have a worse estimate of grasp success. We propose a framework that plans trajectories to a set of generated grasp targets, the evaluator estimates the grasp success likelihood at the terminal configuration of the planned trajectories, and the robot executes the trajectory most likely to succeed. Our experiments show our approach improves over the traditional generator-evaluator-planner framework across different objects, generators, and motion planners, and successfully generalizes to novel environments in the real world, including different shelves and table heights. Project Website: https://martinmatak.github.io/fpte/

Figures

Figures reproduced from arXiv: 2509.07162 by the authors.

Figure 1
Figure 1. Grasps from different shelves and table heights are shown. The robot successfully grasps the target object while avoiding collisions with other objects and the environment. Our approach generalizes to novel environments containing multiple objects. regression has been proposed [9], [19] resulting in the pop￾ular generator-evaluator-planner paradigm [17], [20]–[23]. The generator-evaluator-planner approach consists o… view at source ↗
Figure 2
Figure 2. We use a vectorized motion planner to plan trajectories to different grasp targets in parallel. Then, the grasps resulting from the terminal configuration of all the planned trajectories are evaluated, despite some trajectories not reaching the target grasps. Finally, the trajectory with the highest grasp likelihood is executed. Initial grasp targets are only used for the motion planner, never for grasp success like… view at source ↗
Figure 3
Figure 3. Our platform and the objects used to assess our system. The objects vary in shape, size, mass, rigidity, and opaqueness. Grasp Success 0% 25% 50% 75% 100% MDN Diffusion cVAE trad-curobo FPTE-curobo trad-fabric FPTE-fabric [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Comparison of grasp success rates for different generators, motion planners, and approaches. Our approach, First Plan Then Evaluate, consistently achieves a higher success rate compared to the “trad” method, irrespective of the motion planner choice or the generator ar…
Figure 6
Figure 6. Figure 6: Our generator successfully captures a diverse grasp distri￾bution and generalizes to novel objects in the real world. We use Grounded SAM [62], on the RGB image from the depth observation with the prompt object that can be picked up with one hand, followed by matching …
Figure 7
Figure 7. Figure 7: Some examples of resulting grasps. if there are multiple objects on the table. Furthermore, it enables our approach to grasp objects in novel environments such as different table heights and shelves. We show different environments and some grasps in [PITH_FULL_IMAGE:f…
Figure 8
Figure 8. Figure 8: We report (top) the precision-recall (PR) curve [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 8
Figure 8. Figure 8: Top Precision-Recall curve of the learned grasp likelihood evaluator in the simulator and the real world. This shows that the evaluator’s performance in the real world is similar to its performance in the simulator, and is much better than a random guess. Bottom Estima…

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.