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REVIEW 4 major objections 5 minor 1 cited by

Online Trajectory Replanner for Dynamically Grasping Irregular Objects

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A two-stage trajectory replanner lets robots dynamically grasp irregular objects despite vision pose errors.

desk verdict Solid engineering paper on reactive trajectory replanning for dynamic grasping, but the robustness claims rest on thin statistics and an untested linearization regime. read the letter →

arxiv 2501.17968 v1 pith:O3ZADQBS submitted 2025-01-29 cs.RO

Minh Nhat Vu , Florian Grander , Anh Nguyen This is my paper · ORCID
classification cs.RO
keywords dynamicgraspingtrajectoryreplanningirregularobjectmanipulationonlineoptimizationlinearconstrainedquadraticprogramposeestimationerrorcompensationmovingrobot
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

The paper aims to show that dynamic grasping—catching or picking an object while the robot keeps moving—can work for irregular, non-symmetric objects, not just blocks or boxes. The proposed framework splits planning into an offline stage that produces three smooth motion segments (approach, grasp, carry-away) in under 10 seconds, and an online replanner that reacts to camera pose updates at 10 Hz by solving a small quadratic program in about 53 milliseconds. The online update is built by linearizing the robot dynamics around the offline trajectory, so it nudges the arm and gripper to the latest object pose without stopping the motion. According to the paper, 988 of 1000 randomized simulated poses were grasped successfully, and in real moving-object trials the replanner caught the object 19 out of 20 times, compared with 15 out of 20 for an RRTConnect baseline. If this holds up, the method would make production lines more flexible by letting robots grasp randomly placed or moving irregular parts while in motion.

What carries the argument

The load-bearing object is the deviation trajectory $\delta\xi = [\delta T, \delta x_0^T, \ldots, \delta x_N^T, \delta v_0^T, \ldots, \delta v_N^T]$ and the linearized discrete-time map that relates deviations at neighboring grid points. Starting from the offline optimal trajectory $\xi^*$, the replanner computes Jacobians $\Gamma^x_k$ and $\Gamma^v_k$ of the dynamics, neglects second-order terms, and obtains the compact linear map $C_{k+1}s_{k+1}=A_k s_k$, where $s_k=[\delta x_k^T,\delta v_k^T,\delta T_k]^T$. The quadratic program (13) then minimizes a weighted sum of squared deviations subject to that map, box constraints on deviations, and boundary conditions set by the updated object pose. In the offline phase, an inverted-Gaussian potential function restricts the gripper's contact point inside a funnel near the object, and torque limits are respected by bounding the Coriolis term rather than neglecting it.

What would settle it

Run the online replanner on the same irregular-object grasp while injecting controlled position biases into the vision module (for example, 1, 5, 10, and 20 mm) and record both grasp success and QP solver feasibility; a sharp drop in success or repeated solver failures at moderate bias would show that the linearization, rather than the tracking controller, is the limiting factor.

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Extended reading notes

Core claim

The paper's central claim is that the combination of a fast offline trajectory optimizer and an online deviation-minimizing replanner makes dynamic grasping of irregular objects robust to pose-estimation error. The offline stage solves a constrained trajectory optimization for three connected phases, with the final state of each phase serving as the initial state of the next, and uses an inverted-Gaussian potential function to keep the gripper approaching the object along a safe funnel. The online stage updates the whole trajectory whenever a new object pose arrives: it introduces a small deviation vector $\delta\xi$ around the current optimal trajectory, linearizes the discretized dynamics at the reference trajectory, drops products of deviation variables, and solves a linear constrained quadratic program (Eq. 13) whose solution is added back to the reference. Reported running times are below 10 s for all three offline phases and $53.5 \pm 7.9$ ms on average for the online replanner; success rates are 988/1000 in simulation and 19/20 for real moving objects, versus 15/20 for RRTConnect.

Load-bearing premise

The load-bearing premise is that pose errors and disturbances are small enough that the linearized dynamics with products of deviation variables dropped remain valid; if a vision error or sudden object relocation pushes the system out of that small-deviation regime, the quadratic program can fail or steer the gripper past the object.

Editorial extensions

If this is right

  • A robot can grasp randomly placed irregular parts without stopping: the online replanner absorbs 10 Hz pose updates within a 100 ms budget, so the arm can keep moving through approach and grasp.
  • Dynamic grasping success in the paper's tests is 98.8% in 1000 randomized simulated poses and 95% over 20 real moving-object trials.
  • Compared with an RRTConnect baseline, the replanner avoids computation-time limit violations (0 versus 2 trials) and misses the object less often (1 versus 3 trials).
  • The same two-stage framework transfers to a different gripper, indicating the method is not tied to one hand's kinematics.
  • Because the online update is a convex quadratic program, it can run on a standard PC, making the approach practical for industrial controllers.

Reading between the lines

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

  • The linearization assumption implies there is a basin of attraction: success should degrade continuously as pose-estimation bias grows, so a natural extension not tested in the paper is a sensitivity sweep over injected vision errors to find the breakpoint.
  • The same online-replanner structure could apply beyond grasping, e.g., to assembly insertion or handover tasks where a goal pose stream arrives at low rate and the robot must correct while moving.
  • Because the replanner only produces local corrections around the offline trajectory, completely unexpected object relocations may still require a global planner; the generalization experiment only covers one sudden relocation.
  • The authors' stated limitation (the 10 s offline computation) suggests a fully online version with shorter planning horizons is the immediate next step; if learned motion primitives replace the offline stage, the two-phase split could disappear entirely.
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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 / 5 minor

Summary. The paper proposes a two-stage trajectory optimization framework for dynamically grasping irregular objects with a KUKA LBR iiwa and SDH2 gripper. An offline stage computes three-phase trajectories (approach, grasp, transfer) in under 10 s, and an online replanner solves a small quadratic program at 10 Hz to adjust the trajectory when the vision system updates the object pose. The authors report a 98.8% success rate over 1000 simulated random object poses, an average online solve time of 53.5 ± 7.9 ms, and real-robot experiments including a single-trial with/without comparison and a 20-trial moving-object comparison against RRTConnect (95% vs. 75% success). The central claim is that the online replanner makes dynamic grasping robust to pose-estimation errors while the robot remains in motion.

Significance. If the results hold, the paper offers a practically fast replanning scheme with a clear timing budget, a principled offline warm-start, and an interesting two-stage use of trajectory optimization. The strengths are explicit: offline computation is measured against VP-STO, online computation is well below the 100 ms budget, the simulation campaign is large (1000 trials), and the method is demonstrated on real hardware with two different grippers. The derivation of the online QP is internally consistent, and the use of torque bounds via previous work is a reasonable modeling choice. However, the evidence for the central robustness claim is not yet conclusive: the linearization in the online replanner is tested only indirectly, the key with/without online comparison has n=1 per condition, and the moving-object comparison is a 20-trial experiment reported without confidence intervals. These gaps are fixable and do not, in my reading, invalidate the proposed approach.

major comments (4)
  1. [III-B, Eqs. (10)-(13)] The linearized constraint (11) drops products of δT with δx_k and δv_k, so it is reliable only when deviations from the nominal trajectory are small. However, the box constraints in (13c) do not encode any validity radius for this linearization, and no experiment or simulation varies the magnitude of the pose-estimation error. I request a sensitivity study: apply synthetic pose offsets of increasing magnitude at representative object poses, solve (13), and report the true trapezoidal residual of the replanned trajectory, the QP solve success rate, and the resulting grasp success rate. Without such a study, the paper's motivating claim that the replanner handles vision pose errors is unverified precisely in the regime where the dropped product terms become large.
  2. [IV-B, Fig. 7-8] The comparison of the proposed framework with and without the online replanner is based on one trial per condition. The conclusion that the replanner is responsible for the successful grasp cannot be drawn from n=1; the two trajectories in Fig. 7 and snapshots in Fig. 8 are illustrative only. I recommend repeating the experiment over at least 10-20 random object placements with the same initial and target configurations and reporting success rates, per-trial deviation magnitudes, and, where possible, grasp error or minimum clearance.
  3. [IV-B, Table II] The 20-trial moving-object comparison reports 95% versus 75% success without confidence intervals or a statistical test. With 19/20 successes, the one-sided 95% Wilson lower bound is approximately 76%, so the data do not strongly separate the proposed method from RRTConnect, especially since two RRTConnect trials violated the time limit, which may reflect the TOTG time parametrization rather than planning quality. Please report exact per-trial outcomes, confidence intervals, and either a paired test or a larger trial count.
  4. [IV-A (Simulation results)] The paper states that in 12 failed simulation trials 'the optimization problems reach the iteration limits set to 100,' but it is not specified whether these failures occur in the offline phase, the online QP, or both, and no recovery mechanism is described. Because the 98.8% success rate is a headline quantitative result, the failure stage and subsequent behavior matter. Please clarify where the failures occur, report the distribution of the resulting deviations, and discuss any fallback or re-initialization if the online QP becomes infeasible or hits its iteration limit.
minor comments (5)
  1. [Table II] The caption says 'RRTConnect [25]', but reference [25] is the MoveIt paper while the RRT-Connect algorithm is reference [26]; please correct the citation.
  2. [Eq. (13c)] The inequality (13c) uses the same symbol s_k for the optimization variable and its lower bound; please introduce distinct notation such as \underline{s}_k and \overline{s}_k to make the box constraints unambiguous.
  3. [III-B, Eq. (14), and III-A] The numerical values of the weighting matrices Q_k in Eq. (14), the admissible bound vectors in (13c), and the potential-function parameters (z_th, μ, Σ) in Eq. (5) are not reported; providing these values would improve reproducibility.
  4. [Fig. 5] The caption states σ_x² = 5 mm and σ_y² = 2 mm, which are variances with units of mm²; please clarify whether these are variances or standard deviations and use consistent units.
  5. [I.A] The text says 'AruCo marker'; the standard spelling is 'ArUco marker'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the framework's success metrics are independent physical grasping outcomes, and the self-citations used are not load-bearing for the central claim.

full rationale

The central claim is that the two-stage replanner enables dynamic grasping, measured by grasp success in simulation (988/1000) and physical trials (19/20) against baselines (VP-STO and RRTConnect). These outcomes are not defined in terms of the replanner's internal objective: the QP (13) minimizes deviation from the offline trajectory, but success is independently determined by whether the gripper catches the object. The paper's derivation of the online dynamics from Eqs. (10)-(11) uses a first-order expansion and neglects products of deviation variables; this is an approximation assumption and a correctness risk, not a circular step, because the predicted quantity (grasp success) is not encoded in the linearization. The only self-citation used in the construction is [10] for Coriolis bounds c and c in constraint (3e); these bounds are auxiliary inequality parameters, and the cited work is an independently published ICRA paper, so this citation is not load-bearing for the reported grasping performance. No equation in the paper reduces by construction to a fitted parameter renamed as a prediction, and no uniqueness theorem or ansatz is imported from the authors' own prior work to force the framework's choice. The comparison with VP-STO and RRTConnect and the static/dynamic real-robot experiments provide external falsifiability. Therefore the derivation chain is self-contained with respect to the paper's central claim, and no significant circularity is present.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests mainly on hand-tuned design parameters such as the potential function, weighting matrices, and deviation bounds, and on assumptions that vision errors are small and that the linearized replanner is a sufficient correction. No physical entities are invented. The dependence on the authors' previous work for Coriolis bounds is mild and not circular, because it is not used to define or enforce the success metric.

free parameters (3)
  • Potential function parameters (zth, Sigma, mu) = zth unspecified; sigma_x^2=5 mm^2, sigma_y^2=2 mm^2, mu=(0,0) from Fig. 5
    Chosen by hand to shape the approach corridor in Eq. (5)-(6); no tuning procedure or sensitivity analysis is provided.
  • Online replanner weighting matrices Q_k = unspecified
    Eq. (14) sets Q_k = diag(Q_xk, Q_vk, Q_deltaT); the values are not given and directly control how strongly the replanner stays near the offline trajectory.
  • Admissible deviation bounds s_k and s_k, and deltaT bounds = unspecified
    Inequality (13c) uses hand-chosen tolerances for state, control, and time deviations; no numerical values or justification are provided.
assumptions (5)
  • domain assumption Rigid-body dynamics with trapezoidal discretization (Eq. 3b) is an adequate model for the KUKA robot.
    The optimizer relies on this discretization; Coriolis effects are only bounded via values from prior work, not fully modeled in this paper.
  • domain assumption Vision pose updates at 10 Hz are accurate enough that deviations from the offline trajectory remain small.
    The online replanner linearizes around the offline path (Eqs. 10-11) and ignores second-order terms; no pose-error statistics are reported.
  • domain assumption The grasping point G, defined as the midpoint of the line between C1 and C3, yields balanced contact forces.
    Section II-B states this choice is crucial for steady movement of the object, but it is not verified with force measurements.
  • standard math IPOPT with MA57 converges to a good local solution within the stated time limits.
    The paper relies on a standard NLP solver; only local optimality is expected, and 12 of 1000 simulation trials hit the iteration limit.
  • domain assumption Coriolis bound values c and c from the authors' prior work [10] remain valid for this robot and task.
    These bounds are used in the torque constraint (3e) and are imported without derivation or re-validation here.

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Pith. "Pith review of Online Trajectory Replanner for Dynamically Grasping Irregular Objects." pith.science (2026). https://pith.science/paper/O3ZADQBS

@misc{pith2026250117968,
  author       = {Pith},
  title        = {Pith review of: Online Trajectory Replanner for Dynamically Grasping Irregular Objects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O3ZADQBS}},
  note         = {Machine review of arXiv:2501.17968}
}
read the original abstract

This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a "dynamic grasp" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular objects, we propose a trajectory optimization framework that comprises two phases. Firstly, in a specified time limit of 10s, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-defined target location. Secondly, fast online trajectory optimization is implemented to update robot trajectories in real-time within 100 ms. This helps to mitigate pose estimation errors from the vision system. To account for model inaccuracies, disturbances, and other non-modeled effects, trajectory tracking controllers for both the robot and the gripper are implemented to execute the optimal trajectories from the proposed framework. The intensive experimental results effectively demonstrate the performance of our trajectory planning framework in both simulation and real-world scenarios.

Figures

Figures reproduced from arXiv: 2501.17968 by the authors.

Figure 1
Figure 1. An example setup of grasping irregular objects. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Schematic drawing of (a) the KUKA LBR iiwa [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. (a) Side view of the SDH2 without the finger 2 with [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Timeline III. TRAJECTORY OPTIMIZATION This section presents the overall optimization framework, which consists of two stages. In the offline stage, we consider three trajectory optimization phases; see [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 6
Figure 6. Figure 6: Time evolution of the scaled offline and the online [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Time evolution of the experiment on the comparison [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 8
Figure 8. Figure 8: Snapshots of experiment on the comparison of the [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: A demonstration of grasping the moving object. [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

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Forward citations

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