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REVIEW 4 major objections 5 minor 30 references

Soft Robotic Dynamic In-Hand Pen Spinning

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The SWIFT system shows that a soft three-finger hand can learn to spin a pen dynamically by optimizing eight interaction primitives through real-world trial and error, reaching 100% success on three pens and generalizing to a brush and a…

desk verdict A genuinely new soft-hand dynamic manipulation result whose headline success rates are not as clean as the abstract implies; the evaluation protocol needs scrutiny before you trust the 10/10 numbers. read the letter →

arxiv 2411.12734 v1 pith:R6DUIMUY submitted 2024-11-19 cs.RO

Yunchao Yao , Uksang Yoo , Jean Oh , Christopher G. Atkeson , Jeffrey Ichnowski This is my paper · ORCID
classification cs.RO
keywords softroboticsin-handmanipulationdynamicpenspinningCMA-ESreal-worldlearningcomplianthandstrial-and-erroroptimization
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 establish that a soft, compliant robotic hand can perform fast dynamic in-hand manipulation—specifically spinning a pen around a finger like the human 'Thumbaround' trick—by learning from real-world trial and error. The proposed system, SWIFT, searches over only eight action parameters (six servo targets, a catch delay, and a grasp location) with CMA-ES, using a camera-based reward that rewards rotation and penalizes drops. No simulator, object model, or prior knowledge of the pen's mass or center of mass is required. After 130 sampled actions per object, the optimized parameters spin three differently weighted pens successfully in 10 out of 10 trials each, and the same approach spins a brush 10/10 and a screwdriver 5/10. If correct, this shows that compliance need not be a barrier to high-speed manipulation, and that low-dimensional primitive search can replace accurate modeling in contact-rich tasks.

What carries the argument

The carrying mechanism is the reduction of pen spinning to an eight-parameter primitive space: six servo-target changes $s \in \mathbb{R}^6$ (two per finger on the tendon-driven MOE soft hand, each finger actuated by two servos pulling four tendons), a catch delay $d$, and a grasp displacement $g$ along the pen's length. These parameters are executed open-loop and scored by $r = r_{\mathrm{rot}} - \lambda p_{\mathrm{fall}}$, where the reward accumulates the pen's rotation around the camera's $z$-axis while the pen is tracked, and the penalty counts frames where the pen is displaced from the fingers. CMA-ES, a gradient-free evolution strategy, updates the parameter distribution from each batch of real-world trials, so the search itself carries the reasoning that previous systems put into simulation or analytic models. The result is a small search space that still includes the grasp point, which the ablations show is necessary for reliable spinning across different weight distributions.

What would settle it

Run the same 130-sample optimization on visually identical pens while shifting the manual reset slot by 5–10 mm along the pen's length, and check whether the 10/10 success rate collapses; if it does, the learned skill depends on the fixed reset geometry rather than on a general dynamic manipulation capability.

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Extended reading notes

Core claim

The central claim is that dynamic pen spinning, usually reserved for high-speed rigid hands with analytic models or simulation-trained policies, can be learned by a soft three-finger hand from scratch in the real world. The paper demonstrates this with the SWIFT system: it optimizes an eight-dimensional action $(s,d,g)$, where $s$ are six servo angle changes, $d$ is a catch delay, and $g$ is a horizontal grasp offset along the pen. Trials are self-labeled through a camera-based objective that rewards rotation of the pen's major axis around the $z$-axis and penalizes frames where the pen is not near the fingers. After about 130 sampled actions per object, the optimized parameters succeed on all three pens (10/10 each), and the same procedure produces 10/10 on a brush and 5/10 on a screwdriver. The author's interpretation is that compliance, rather than being a limitation for fast manipulation, lets the system interact safely and repeatably while the search finds the timing and contact that make the spin work.

Load-bearing premise

The whole loop assumes every trial starts from a repeatable grasp, which is achieved by a human manually placing the pen in a fixed slot and the arm following a fixed motion sequence; if that setup variation is larger than the differences the optimizer is meant to find, the learned parameters will not transfer.

Editorial extensions

If this is right

  • With the full eight-parameter optimization, the system reaches 10/10 success on three visually identical pens that differ in total mass and center-of-mass offset, after about 130 sampled actions per pen.
  • Removing the grasp-location parameter from the search reduces success to at most 7/10 on one pen and 0/10 on the other two, so searching over where to grasp is a necessary part of the discovered solution.
  • Action parameters found for one pen transfer poorly to another (pen 1's solution gives 7/10 on pen 3 and 0/10 on pen 2), so each object needs its own short optimization run.
  • The same method spins a brush successfully in 10/10 trials and a screwdriver in 5/10 trials, indicating the approach is not limited to symmetric, uniformly weighted cylinders.
  • Because everything is learned from real-world interactions with a camera-based reward, the approach sidesteps the sim-to-real gap that prior pen-spinning systems had to overcome.

Reading between the lines

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

  • Inference: the optimized grasp displacement $g$ likely tracks each pen's center-of-mass offset; a direct test would be to check whether $g$ changes in the same direction as the COM positions shown for the three pens.
  • Inference: the same eight-parameter recipe could be applied to other fast in-hand tricks (coin flip, pencil twirl, finger pass) by changing the rotation axis in the reward and reusing CMA-ES, provided the task can be captured by a similarly small primitive family.
  • Inference: the manual reset and human success labels are currently hidden supervision; replacing the fixed slot with visual servoing and deriving a binary success signal from the drop penalty would make the loop fully autonomous and reveal how much of the 130-sample efficiency depends on the reset procedure.
  • Inference: if this scaling holds, precise dynamic models and high-speed closed-loop control are not prerequisites for fast soft manipulation; the hand's compliance can absorb the errors of open-loop primitives.
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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 presents SWIFT, a system that uses a three-finger soft robotic hand (MOE) attached to a 6-DOF arm to learn a "Thumbaround"-style pen spinning skill entirely from real-world trials. The action space is compressed to eight parameters: six servo-target changes, a catch delay, and a horizontal grasp offset. CMA-ES optimizes these parameters using a camera-based proxy reward that combines accumulated rotation around the hand's m3 finger with a fall penalty. The authors report 10/10 success on three visually identical pens with different mass distributions after 130 sampled actions per object, and 10/10 and 5/10 on a brush and a screwdriver respectively. They also report ablations showing that optimizing the grasp offset is important and that parameters tuned for one pen do not transfer to another. The claimed novelty is that a soft hand can learn a fast, dynamic in-hand manipulation skill without simulation, precise object models, or closed-loop high-speed control.

Significance. If the claims hold, this would be a meaningful step for soft robotic manipulation, which has mostly been limited to quasi-static tasks. The paper's strengths are its real-world, trial-and-error optimization approach; a deliberately compact task-specific parameterization; and the public release of videos, data, and code. The system also demonstrates a plausible use of CMA-ES on a physical platform where simulation would be difficult due to soft-body contact dynamics. However, the evaluation protocol currently undermines the headline claims: the paper describes trials as "self-labeled" while later stating that a human observer labels success, and the generalization results are based on a manually selected successful sample rather than the optimized policy. These issues are load-bearing for the central contribution of autonomous, self-supervised learning, and they must be resolved before the reported success rates can be interpreted as policy performance.

major comments (4)
  1. [Abstract, §V-B] The abstract and contribution (2) describe the trials as "self-labeled" and the process as "self-supervised autonomous." In §V-B, however, the authors state: "a human observer labels trials a success or failure." These statements are in direct tension. If the human labels are used only for the reported success rates and not for optimization, the text must say so explicitly and avoid "self-labeled." If human labels are used to select or update parameters, the process is not autonomous and the contribution claim should be revised. This distinction is load-bearing for the paper's central claim.
  2. [§V-A] For the brush and screwdriver, the authors "chose the first manually observed success for evaluation, rather than directly using the stored value of CMA-ES." Selecting a parameter sample because a human observed a success, and then scoring that same sample 10/10 or 5/10, is a selection-biased estimate of the learned policy. It does not measure the performance of the CMA-ES incumbent, the final mean, or the best-by-objective parameter. The paper should report the success rates of the final CMA-ES policy (e.g., the incumbent after the last generation), and clearly separate optimization samples from evaluation trials.
  3. [Table I, §V-A] It is not specified whether the 10-trial success rates for the three pens are obtained from the CMA-ES incumbent, from the best-by-objective sample, or from a manually selected successful sample as in the generalization experiments. If the pen rows also use a manually selected favorable trial, the 10/10 claims overstate the reliability of the learned policy. The authors should state the exact parameter selection rule for every row and, ideally, report success rates for both the objective-best and the final mean parameter sets.
  4. [§IV-D, §V-B] The objective in Eq. (1) is an indirect proxy: it accumulates z-axis rotation while the filtered point cloud exceeds a threshold, and the penalty counts frames with insufficient points. This proxy may score a trial as good even when the pen does not complete a full rotation around the finger or is caught in a way the human would not call a success. Since "success" is defined in §III as a full rotation without dropping, the mismatch between the proxy and the human label should be quantified (e.g., agreement rate between high reward and human success), or the objective should be redesigned to directly encode the success criterion. At minimum, the paper should clarify whether the human labels influenced the optimization or only the final evaluation.
minor comments (5)
  1. [§IV-A] There is a typo: "The sot robot hand consists..." should be "The soft robot hand consists...".
  2. [§IV-D] The sentence "The camera has setup to have its z-axis pointing..." is ungrammatical; it should be "The camera was set up with its z-axis pointing...".
  3. [§IV-D] The definition of the rotation angle θt_z is not precise: the text says the first principal component is projected onto the x, y, and z planes to compute Euler angles, but it is unclear how the sign and branch of the z-axis rotation are resolved. Please specify the exact convention used.
  4. [§V-A] The phrase "130 sampled actions per object" in the abstract is inconsistent with the generalization protocol, where optimization stops at the first generation with observed successes (reported as generation 4, i.e., about 52 samples). The per-object sample counts should be stated explicitly for the pen experiments and for the brush/screwdriver experiments separately.
  5. [Table I] The row label "Initialization ∅" is confusing: the row reports the success rate of the fixed hand-crafted initialization, not of an empty parameter set. Renaming it to "Hand-crafted initialization (no optimization)" would make the table easier to interpret.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the learned spinning parameters are evaluated against an external human-labeled success criterion, and no equation or claimed result reduces to its inputs.

full rationale

SWIFT is an empirical learning system, not a derivation. The 8-D action parameterization (s,d,g) is hypothesized, CMA-ES optimizes the camera-based reward r = r_rot - lambda p_fall in Eq. (1), and success is defined externally as a full rotation without dropping and labeled by a human observer in Section V-B. The reward is an approximate proxy for success, but the paper does not define success as the reward, and the final Table I success rates are measured from repeated trials, not read off the optimization objective. The 'first manually observed success' selection for the brush and screwdriver is a statistical/selection concern about how representative the reported rates are, but it is not a circularity: the selected parameter is still executed and scored independently against the human-defined success criterion. Self-citations to the authors' MOE hand ([7], [28]) supply hardware and prior proprioception work, but the spinning result is not derived from those citations. No uniqueness theorem, ansatz-by-citation, or renaming pattern is present. The paper's claims are therefore self-contained empirical claims with no by-construction equivalence between inputs and outputs.

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

The central claim rests on a controlled physical setup, a hand-tuned reward, and the assumption that RGB-D state estimation and human labeling accurately capture success. The optimized action parameters (s, d, g) are fitted per object via CMA-ES, and several thresholds and weights are unreported.

free parameters (5)
  • Spinning servo targets s (6 values) = Not reported; optimized by CMA-ES
    Section IV-C: s is the vector of servo angle changes for the six servos that execute the spin; learned per object.
  • Catch delay d = Not reported; optimized by CMA-ES
    Section IV-C: delay between the spinning action and catching action; learned per object.
  • Grasp location offset g = Not reported; optimized by CMA-ES
    Section IV-C: horizontal displacement of the grasp from the pen center; learned per object.
  • Reward penalty weight lambda = Not stated in the paper
    Equation (1) combines rotation reward and fall penalty with weight lambda; the value is chosen by the authors but not reported.
  • Point-cloud drop threshold n = Not stated in the paper
    Used in the indicator function 1_{||p_t||>n}; n is a hand-chosen threshold for detecting when the pen is dropped or too far from the fingers, and its value is not specified.
assumptions (5)
  • domain assumption The object is long, cylindrically symmetric, has a well-defined major axis, and its mass and size are within the hand's manipulation capabilities.
    Section III: bounds the problem to pen-like objects, excluding irregular objects from the formal problem statement.
  • domain assumption The manual fixturing and pre-programmed arm motions produce repeatable grasps.
    Section IV-B defines the reset procedure that the optimization depends on; repeatability is asserted, not measured.
  • domain assumption The reward computed from RGB-D observations (SAM v2 segmentation, PCA on point cloud) is a sufficient proxy for the spinning behavior.
    Section IV-D: the objective uses PCA-derived Euler angles and point counts; no validation of state estimation accuracy is provided.
  • domain assumption CMA-ES with population size 13 and 10 generations is sufficient to find good parameters.
    Section V-A: population size follows Hansen and Ostermeier heuristics; no ablation on generations or population size is performed.
  • domain assumption The RGB-D camera's z-axis is roughly aligned with finger m3, so the reward's z-rotation about the camera axis approximates rotation about the finger.
    Section IV-D and IV-B: the camera is set up with z-axis pointing roughly towards finger m3, but the alignment is not precisely calibrated or reported.

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Cite this review

Pith. "Pith review of Soft Robotic Dynamic In-Hand Pen Spinning." pith.science (2026). https://pith.science/paper/R6DUIMUY

@misc{pith2026241112734,
  author       = {Pith},
  title        = {Pith review of: Soft Robotic Dynamic In-Hand Pen Spinning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R6DUIMUY}},
  note         = {Machine review of arXiv:2411.12734}
}
read the original abstract

Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynamic tasks. In this work, we present SWIFT, a system for learning dynamic tasks using a soft and compliant robotic hand. Unlike previous works that rely on simulation, quasi-static actions and precise object models, the proposed system learns to spin a pen through trial-and-error using only real-world data without requiring explicit prior knowledge of the pen's physical attributes. With self-labeled trials sampled from the real world, the system discovers the set of pen grasping and spinning primitive parameters that enables a soft hand to spin a pen robustly and reliably. After 130 sampled actions per object, SWIFT achieves 100% success rate across three pens with different weights and weight distributions, demonstrating the system's generalizability and robustness to changes in object properties. The results highlight the potential for soft robotic end-effectors to perform dynamic tasks including rapid in-hand manipulation. We also demonstrate that SWIFT generalizes to spinning items with different shapes and weights such as a brush and a screwdriver which we spin with 10/10 and 5/10 success rates respectively. Videos, data, and code are available at https://soft-spin.github.io.

Figures

Figures reproduced from arXiv: 2411.12734 by the authors.

Figure 1
Figure 1. SWIFT tackles the problem of high-speed dynamic in-hand partially non-prehensile manipulation with soft robotic hands. Using a soft multi￾finger gripper, the robot grasps a pen. Then, using a learned action sequence, rapidly rotates the pen around a finger and catches it. in practice, we may not know such parameters a priori. For example, in the case of pen spinning, visual observation may be inadequate to estimate … view at source ↗
Figure 2
Figure 2. Multi-finger Omnidirectional End-effector (MOE) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Task progression over time. There are three main stages for each pen-spinning trajectory. We place the pen according to the blue slots fixed on the table, and the robot moves to grasp and move the pen to reach the pre-spin pose with g or pre-defined constant. The MOE fingers then execute s to attempt to spin the pen, and finger m1 waits for d seconds before closing to catch the pen. Finally, the robot arm moves to t… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: SWIFT optimization pipeline. There are 4 main stages for each iteration k: 1) During grasping and resetting, the robot arm moves the MOE hand to a target grasp location following a specific grasping location gk. 2) The robot arm then moves the MOE hand to the pre-spin …
Figure 4
Figure 4. Figure 4: Our setup for pen spinning. Top: A 3-finger MOE soft robotic hand is attached to a 6 degree-of-freedom robot arm to develop a system that can safely interact with the pen and learn to spin it. An RGB-D camera is used to evaluate the performance of the sampled action ba…
Figure 6
Figure 6. Figure 6: Spinning visualization after optimization [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Generalization to other objects. We applied SWIFT to other objects with more irregular shapes, such as a brush or a screwdriver. The circle in the initial frame indicates the approximated center of masses. We demonstrated the system’s robustness across pens of differen…

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