Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution

Chen Chen; Mingrui Yu; Xiang Li; Yongpeng Jiang; Yongyi Jia

REVIEW 4 major objections 5 minor 33 references

Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution

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

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Pith's one-line read This paper claims that precise, large-range in-grasp object movement can be achieved by a kinematic optimizer that fixes fingertip positions, allows full rolling, and closes the loop with replanning—with no object geometry or training.

desk verdict A useful, honest systems paper that extends relaxed-rigidity optimization by allowing all-fingertip rolling; the simplified contact model is a real limitation but the closed-loop empirical results carry the claim. read the letter →

arxiv 2502.07472 v2 pith:KFJKHWGK submitted 2025-02-11 cs.RO

Mingrui Yu , Yongpeng Jiang , Chen Chen , Yongyi Jia , Xiang Li This is my paper · ORCID

classification cs.RO

keywords multi-fingeredin-handmanipulationin-graspobjectmovementtrajectoryoptimizationrollingcontactclosed-loopreplanningkinematicplanningdexterousRGMC

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 claims that a simple kinematic trajectory optimizer can move an object grasped in a multi-fingered hand through a large range of positions with millimeter-level precision, without any object geometry, pretraining, or contact simulation. The trick is to enforce the stable grasp only by fixing each fingertip's position in the object frame, leaving fingertip orientation free so all fingertips may roll. Closed-loop replanning from the measured current pose absorbs the errors this simplification introduces. If right, this makes precise in-grasp manipulation practical for novel everyday objects on generic dexterous hands.

What carries the argument

The load-bearing object is the cost $J = J_{\text{object}} + J_{\text{finger}} + J_{\text{joint}}$, where $J_{\text{object}}$ penalizes the terminal pose error via the weighted SE(3) distance $d(T_1,T_2,W)$ with error $e=[p_e; r_e]$, $J_{\text{finger}}$ penalizes changes in each fingertip's pose relative to the object frame with orientation weights near zero, and $J_{\text{joint}}$ regularizes the joint trajectory. The constant-stable-grasp requirement is replaced by the surrogate 'fingertip positions fixed in the object frame', which ignores rolling-induced contact migration and is justified by an assumption of smooth, low-curvature surfaces and quasi-static motion. Analytic gradients come from perturbing the SE(3) pose, using the left Jacobian of SO(3) and a relative Jacobian between fingertip and object frames; the non-convex problem is solved sequentially with SLSQP. A closed-loop scheme re-plans and re-executes until planned error, replanning count, or time budget is met.

What would settle it

Measure fingertip contact migration during a 40-waypoint closed-loop run, for example with a tactile array or a high-speed camera; if the accumulated contact migration exceeds the hemispherical fingertip radius, the fixed-position surrogate no longer predicts the actual motion. Alternatively, run the identical pipeline on a small-radius sphere (about 2 cm radius) with the same replanning cap: if the object drops or average execution error grows well beyond the reported 5 mm, the low-curvature premise is falsified.

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Extended reading notes

Core claim

The paper's central claim is that in-grasp object movement—moving an object's pose using only finger motion under a constant stable grasp—can be solved by a geometry-free kinematic trajectory optimizer. The constant-grasp requirement is reduced to a surrogate: keep each fingertip's position fixed in the object frame, but allow all fingertips to roll. Assigning zero orientation weight in the fingertip cost makes rolling free, and optimizing the object pose as a decision variable frees the thumb from the rigid-contact assumption that limited prior relaxed-rigidity approaches. Closed-loop replanning from the current measured state to the goal compensates for the rolling simplification, and returning the fingers to the initial grasp between waypoints preserves long-horizon robustness. On the RGMC task this yields average execution errors near 5 mm over 40 continuous waypoints inside a 5×5×5 cm cube, on both the known cylinder and novel objects, without using object geometry.

Load-bearing premise

The approach assumes that ignoring fingertip rolling and contact migration—treating a stable grasp as fixed fingertip positions in the object frame—still produces the planned motion, which holds only if the object surface is smooth and low-curvature and the motion is quasi-static.

Editorial extensions

If this is right

Allowing rolling at every fingertip, rather than keeping the thumb contact rigid, enlarges the object's reachable space and improves both planned and executed accuracy compared with the rigid-thumb baseline.
Closed-loop replanning roughly halves execution error, from about 10 mm open-loop to about 5 mm, and keeps errors stable across 40 consecutive waypoints when the replanning cap is moderate.
The same pipeline, with no object model, transfers to novel everyday objects—thick cylinder lid, box, presenter remote, and screwdriver—with average errors around 5 mm, even though high curvature and weight increase error.
Accuracy degrades gracefully with distance: sub-millimeter errors at 1 cm cube corners, and roughly 2 cm average error at the extremes of a 9×9×9 cm space, showing the planner exploits the fingers' workspace.
Excessive replanning degrades contact quality, so the competition strategy used a conservative replanning cap in one run and an aggressive cap in the other to balance robustness and precision.

Reading between the lines

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

Editorial extension: the fixed-fingertip-position surrogate could serve as a cheap warm-start or feasibility filter for contact-implicit or learning-based planners, because it produces joint-limit- and collision-respecting trajectories in seconds rather than the per-step cost of rigorous rolling models.
Editorial extension: since the object cost can carry orientation weights, the formulation implies reachable pose goals with rotation; the paper demonstrates only three such goals, leaving full six-dimensional waypoint sequences as a natural open test.
Editorial extension: the documented failure mode under excessive replanning suggests a closed-loop regulator that monitors contact quality (for instance by tactile sensing) could outperform the fixed replanning cap, an option the authors do not explore.
Editorial extension: the screwdriver results, which required occasional manual grasp resets, hint that the low-curvature assumption is the method's boundary; a systematic sweep over surface curvature would quantify where the surrogate breaks.

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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

This paper is worth a look if you care about practical in-hand manipulation. What is new: it takes the relaxed-rigidity formulation from Sundaralingam and Hermans and loosens it by fixing only fingertip positions in the object frame rather than rigid thumb-object contact, letting all fingertips roll. That does enlarge the reachable space in the experiments, and the closed-loop replanning scheme is a sensible practical addition. The real-robot results are the core: 40 waypoints in a 5 cm cube with about 5 mm average closed-loop error, repeated across runs and on four novel objects, with code and video released. The competition win is supporting external evidence, and the appendix gives a genuinely honest account of failure modes; the excessive-replanning contact degradation in C.3 is exactly the risk you would worry about.

The stress-test concern about the contact simplification is fair, and the paper is candid about it. The optimization ignores rolling-induced contact migration, justified by low-curvature surfaces and quasi-static motion, and the custom fingertips are only hemispherical over a 3D-printed base. Appendix C.3 confirms that when replanning pushes the contact past the spherical cap, unmodeled contact with non-spherical finger parts causes slippage. So the 5 mm accuracy is not a property of the kinematic model alone; it is the closed-loop system, including PD compliance and soft fingertips, that makes it work. That does not break the main claim, because the paper claims the approach achieves the accuracy, not that the simplified model predicts it perfectly. But the limitation is load-bearing and should be stated more prominently than a brief assumption in Section IV-B.

Minor soft spots: the main comparison figures lack error bars, though Appendix C.1 partially fixes that; the baseline in Section V-C is a modified re-implementation, disclosed in Appendix F; hyperparameters were tuned on the same benchmark used for the headline numbers; and the 7/9 cm reachable-space results involve manual grasp adjustments and per-goal replanning choices, making those numbers less clean. None of these are fatal, but they should be disclosed in the main text.

Bottom line: a solid systems contribution, honestly reported, with reproducible artifacts. It is an extension of an existing framework rather than a new paradigm, and its practical value is the main asset. A serious referee should engage with it. I would send it to review, with a request to move the contact-simplification caveat and the hyperparameter-tuning disclosure into the main text. I would cite it if I were working on geometry-free in-hand manipulation.

Referee Report

4 major / 5 minor

Summary. This paper presents a trajectory-optimization approach to in-grasp object movement for a multi-fingered hand. The method fixes only the fingertip positions in the object frame while allowing rolling contacts, adds a joint-space smoothness cost, and uses SLSQP with analytical gradients to plan full trajectories. A closed-loop scheme re-plans and re-executes until a target error, a replanning budget, or a time limit is reached, and the pipeline returns the fingers to the initial state between waypoints. The authors report winning the RGMC in-hand manipulation track, with average execution errors around 5 mm over 40 waypoints in a 5x5x5 cm cube on a known cylinder and on novel everyday objects, and they compare their approach with a modified re-implementation of a relaxed-rigidity baseline.

Significance. If the reported results hold, the paper shows that a simple, geometry-free kinematic optimizer can achieve practically useful precision and range for in-hand object movement, without pretraining or object models. The strengths include real-world validation on a physical hand, a competition win with externally recorded waypoints, open-source code, and detailed appendices on analytic gradients and variance. The main contribution over prior kinematic in-grasp work is the relaxation of the rigid-thumb-contact assumption, which plausibly enlarges the reachable space while keeping the optimization tractable. The empirical claims are valuable, but the current evidence for the comparative advantage over existing work is weakened by the modified baseline and by missing statistical detail in the central comparison figures.

major comments (4)

[IV-D and Fig. 4] The pipeline described in Section IV-D returns the fingers to the initial state along the forward trajectory after each waypoint, and Appendix C.2 confirms that the object falls in the second iteration when this return is omitted. This means the reported 40-waypoint experiments are likely 40 out-and-back trials from the center of the cube rather than a continuous traversal from one waypoint directly to the next. The paper should state explicitly whether the object returns to the initial position between waypoints and, if so, reword claims such as 'continuously reach' and 'long-term robustness' so that they describe repeated departures from and returns to the center. This distinction is important for interpreting the difficulty and the validity of the large-range continuous movement claim.
[V-C and Appendix F] The baseline implemented for comparison in Section V-C is not the original algorithm of Sundaralingam and Hermans but a modified re-implementation with three deliberate differences: no in-trajectory object-pose cost, joint velocity/movement limits treated as a soft penalty, and the same hyperparameters as the proposed method. These modifications could disadvantage the baseline, so the comparison in Fig. 8 does not by itself support the contribution claim that the proposed approach achieves a larger object reachable space while ensuring task accuracy compared with existing works. The authors should either use the original baseline implementation, justify that each modification is neutral or favors the baseline, or temper the comparative claim to be against this particular re-implementation.
[V-C, Fig. 8] The central comparison between the proposed approach and the baseline in Fig. 8 reports only average errors over 40 waypoints without error bars or repeated-trial statistics. Appendix C.1 provides variance data for the proposed method, but no equivalent variance data are given for the baseline. Since the comparative improvement is a stated contribution, the paper should report repeated trials and error bars for both methods, or at least state clearly that the comparison is based on a single continuous run per condition and therefore should be interpreted as anecdotal.
[IV-B and Appendix C.3] The contact simplification in Section IV-B fixes each fingertip center in the object frame and fully allows rolling, but it places no constraint that the actual contact point remains on the hemispherical fingertip cap. Appendix C.3 documents that with excessive replanning, unmodeled contact occurs between the object and non-spherical parts of the fingers, leading to significant slippage. Because maintaining a constant stable grasp is a central claim, the paper should provide quantitative evidence that for the reported 5 cm motions with the chosen replanning budgets, the contact points remain on the spherical caps (for example, from tracked fingertip-object contact positions or rolling-angle estimates), or explicitly qualify the constant-grasp guarantee as an empirical property of the closed-loop hardware system rather than a property guaranteed by the optimization formulation.

minor comments (5)

[Section I] The introduction states that in-grasp manipulation can be achieved 'without altering the contact locations,' but Section IV-B explicitly allows rolling contacts that alter contact locations; the terminology should be made consistent, for example by referring to maintaining contact without breaking and re-making contacts.
[Figs. 6, 7, 8, 10] The 'planned error' metric is the terminal pose error of the trajectory that the optimizer minimizes; presenting it as a task-performance benchmark is somewhat circular. Consider relabeling it as 'planned terminal error' and clarifying that it is an optimizer property, with closed-loop execution error being the actual task metric.
[Fig. 7] The comparison of different Nreplan values in Fig. 7 shows averages over eight corners per iteration without error bars; the variability is visible in the scatter points but should be summarized, especially since the conclusion that Nreplan = 8 degrades accuracy in later iterations is based on these averages alone.
[Eq. (5)] The collision constraint Fcollision(Qt) is described only as constraining distances between four critical points on the index and ring fingers; the exact definition of Fcollision and the critical points should be stated in the main text or referenced to a specific appendix location for reproducibility.
[Table III] The pose-goal experiments in Appendix D report a single run for each of the three goals; the paper should state how many trials were performed and whether the reported position and orientation errors are representative, since the appendix elsewhere emphasizes trial-to-trial variability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: accuracy claims are execution measurements against external waypoints, and the planned-error metric is transparently the optimized objective rather than an empirical prediction.

full rationale

The derivation chain is self-contained. The trajectory optimization in Eqs. (2)-(5) directly minimizes the terminal pose distance d(W To,T, W To,d, Wo), and the paper reports this quantity only under the transparent label 'planned error' (Section V), not as an empirical prediction. The headline accuracy claims (average closed-loop error of about 5 mm on the cylinder and novel objects; Table II competition errors of 0.054 cm and 0.063 cm) are measured against externally supplied waypoints by an AprilTag tracker, so they do not reduce to the optimized cost. Open-loop and closed-loop execution errors are real-world measurements independent of the planning objective. No parameter is fitted to a subset of the reported waypoints and then renamed a prediction; the hand-tuned hyperparameters (Wo, Wf, lambda, Nreplan, T) are not the quantities claimed as results. The two self-citations ([20] and [31]) are used for related-work context and for an IK solver, respectively, and neither carries the central claim. The main limitation, that rolling is simplified to fixed fingertip positions in the object frame (Section IV-B) with Appendix C.3 documenting unmodeled contact under excessive replanning, is an acknowledged modeling gap rather than a circular reduction. The larger reachable space compared with the baseline is partly a mathematical consequence of relaxing the rigid-thumb constraint, but the paper supports it with open-loop and closed-loop execution errors, which are external benchmarks. No circular step meeting the quoted-evidence bar was found.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on four explicit modeling assumptions in Section IV-B (rigid object, quasi-static motion, smooth low-curvature surfaces, stable manipulable initial grasp) and on the paper-specific simplification that constant stable grasp is encoded only by holding fingertip centers fixed in the object frame (Jfinger in Eq. (3)), with no force-closure or contact-quality constraint. Hyperparameters (cost weights, T, Nreplan, grasp inset offset) are hand-selected; they are not fitted to predict a constant, but they do shape the reported performance. No new physical entities are introduced.

free parameters (4)

Cost weights W_o, W_f, lambda = W_o = diag(10,10,10,0.01,0.01,0.0); W_f = diag(10,10,10,0.001,0.001,0.001); lambda = 4e-4 (full trajectory), 5e-3…
Hand-selected in Appendices B.A and D; orientation weights trade reachable accuracy against robustness and were chosen empirically after observing slippage.
Trajectory steps T = T=3 for first plan, T=1 for replanning
Chosen in Section V-A as a resolution and efficiency trade-off; the paper reports T has little effect on execution error, so this is not a sensitive fit.
Replanning budget Nreplan = 4 (first competition run), 8 (second run)
Chosen from the observed accuracy versus contact-quality trade-off in Section V-B; the paper reports that Nreplan=8 improves early iterations but degrades later ones.
Initial grasp inset offset = Not quantified in text
Section IV-E sets target fingertip positions further inside the object surface with a predefined offset; this manual choice affects grasp quality but is not reported as a value.

assumptions (5)

domain assumption The object can be treated as rigid.
Assumption 1 in Section IV-B.
domain assumption Motion is quasi-static with negligible inertial effects.
Assumption 4 in Section IV-B; used to justify a kinematic optimization without dynamics.
domain assumption Object surfaces near contacts are smooth and low-curvature.
Assumption 3 in Section IV-B; this is the premise that ignoring rolling-induced contact migration does not significantly change results.
ad hoc to paper A constant stable grasp can be approximated by holding each fingertip center fixed in the object frame.
Core simplification in Section IV-B defining Jfinger in Eq. (3); no force closure or contact-quality constraint is enforced.
domain assumption The human-supplied initial grasp is stable and manipulable.
Assumption 2 in Section IV-B and human-dragging initialization in Section IV-E; all subsequent planning starts from this grasp.

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Cite this review

Pith. "Pith review of Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution." pith.science (2026). https://pith.science/paper/KFJKHWGK

@misc{pith2026250207472,
  author       = {Pith},
  title        = {Pith review of: Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KFJKHWGK}},
  note         = {Machine review of arXiv:2502.07472}
}

read the original abstract

In-hand manipulation using multiple dexterous fingers is a critical robotic skill that can reduce the reliance on large arm motions, thereby saving space and energy. This letter focuses on in-grasp object movement, which refers to manipulating an object to a desired pose through only finger motions within a stable grasp. The key challenge lies in simultaneously achieving high precision and large-range movements while maintaining a constant stable grasp. To address this problem, we propose a simple and practical approach based on kinematic trajectory optimization with no need for pretraining or object geometries, which can be easily applied to novel objects in real-world scenarios. Adopting this approach, we won the championship for the in-hand manipulation track at the 9th Robotic Grasping and Manipulation Competition (RGMC) held at ICRA 2024. Implementation details, discussion, and further quantitative experimental results are presented in this letter, which aims to comprehensively evaluate our approach and share our key takeaways from the competition. Supplementary materials including video and code are available at https://rgmc-xl-team.github.io/ingrasp_manipulation .

Figures

Figures reproduced from arXiv: 2502.07472 by the authors.

**Figure 2.** Our hardware setup for the competition, comprising a Leap Hand with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Formulation of the in-grasp object movement. The objective is to find [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: Our pipeline for the RGMC. After the initial grasping, our solution [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Experiments of in-grasp object movement with various objects, in which the objects continuously reach the eight corners of a [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Evaluation of the trajectory optimization with different numbers of [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 8.** Figure 8: Comparison between the proposed approach and the baseline [ [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 10.** Figure 10: Evaluation of the generalization of our approach to novel everyday [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]

**Figure 11.** Figure 11: Variance of task errors. (a) Variance w.r.t. different runs, where each bar represents the average error over 40 waypoints (five iterations of eight [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]

**Figure 12.** Figure 12: Example of applying excessive re-planning. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

**Figure 14.** Figure 14: Snapshots of reaching goal object poses. The figures in the second [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]

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we do not include the cost of in-trajectory object poses, JOURNAL OF LATEX CLASS FILES 13 whose references are obtained by linear interpolation between the start and goal object poses in theirs; 2) we treat the joint velocity/movement limits as a soft constraint (penalty) inst...

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

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Trends and challenges in robot manipulation,

A. Billard and D. Kragic, “Trends and challenges in robot manipulation,” Science, vol. 364, no. 6446, p. eaat8414, 2019

2019

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Manipulation for self-identification, and self-identification for better manipulation,

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B. Sundaralingam and T. Hermans, “Relaxed-rigidity constraints: In- grasp manipulation using purely kinematic trajectory optimization,” in Robotics: Science and Systems XIII , 2017

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Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation,

——, “Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation,” Autonomous Robots, vol. 43, pp. 469–483, 2019

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9th Robotic Grasping and Manipulation Competition

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Learning dexterous in-hand manipulation,

O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al., “Learning dexterous in-hand manipulation,” Int. J. Robot. Res. , vol. 39, no. 1, pp. 3–20, 2020

work page 2020