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REVIEW 4 major objections 6 minor 1 cited by

Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A one-degree-of-freedom gripper with three tapered fingers can hold objects more securely than the standard two flat fingers used across robotics.

desk verdict A clever, well-documented 1-DoF gripper design with a plausible torque advantage, but the empirical 'consistent outperformance' claim needs more statistical support before I'd trust it. read the letter →

arxiv 2505.09771 v3 pith:XJDX3MV5 submitted 2025-05-14 cs.RO

Michael Burgess , Edward H. Adelson This is my paper · ORCID
classification cs.RO
keywords parallel-jawgripperthree-fingergraspingroboticmanipulationtactilesensingforceestimationteleoperationgraspstabilityself-similardesign
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 aims to show that a simple hardware change—three narrow, tapered fingers on a standard one-degree-of-freedom parallel jaw—makes robotic grasps more secure than the usual two flat fingers. The design is a two-against-one layout: two fingers converge into a V, and a third opposes them, so grasped objects meet three contact patches instead of two. From a 2D statics argument, the third contact gives a lever arm that resists twisting forces about the grasping axis and along the finger direction, something flat jaws cannot do without very wide fingers. The authors support the claim with teleoperated trials on 15 objects and three tool-use tasks, plus a camera-based tactile finger that estimates normal force to about 1.3 N validation error. If the results hold, any parallel-jaw robot could gain more reliable grasping at low cost and without changing its actuator.

What carries the argument

The load-bearing mechanism is the two-against-one, V-shaped finger geometry. The two V fingers are pitched inward by a small angle so they can flatten into interdigitation and still grasp very thin objects, while the varying separation $L$ along their length lets objects of different sizes contact at different positions—small objects near the tip, large objects near the base where the lever arm is longest. Soft silicone gel pads deform to the object's local shape, enlarging contact patches and adding elastic resistance to slip; rigid fingernails at the tips handle prying small objects. For tactile sensing, the single finger's backing is left optically clear, so an external high-dynamic-range camera sees both the gel deformation and nearby objects in the same image, and a neural network maps difference images to normal force.

What would settle it

Recompute the grasp results per operator and per object with binomial confidence intervals; if the flat-finger and GET success intervals overlap for a majority of the 15 objects, the claimed consistent advantage is not supported. A sharper test would instrument a fixed grasp with a six-axis force-torque sensor and measure the maximum disturbance torque about the grasping axis before slip for GET and flat fingers on identical cylindrical handles; the geometry predicts GET's limit should grow with finger separation $L$, so a failure to find that scaling would refute the mechanism.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that replacing the two flat fingers of a parallel jaw gripper with a three-finger, V-shaped pair-plus-opposing configuration turns a 1-DoF gripper into a device that can form three-point, geometry-conforming grasps. The authors derive that the maximum disturbance torque a two-finger grasp can resist scales with finger width $w$, while the three-finger grasp resists torque approximately in proportion to the lever arm $L$ between fingers, giving an advantage that grows with object size and with distance between contacts. They also find that rigid fingernails at the fingertips let the gripper pry small flat objects off a table, and that a camera mounted behind a soft gel pad, with colored LED lighting, yields tactile images from which a convolutional network estimates normal contact force with roughly 1.3 N validation error across varied geometries. In experiments, the GET fingers grasped small objects and securely held large ones more often than the flat-finger baselines, and completed three teleoperated tasks—hammering, spreading, and small-part assembly—with higher success and shorter completion times.

Load-bearing premise

The load-bearing premise is that 30 trials per finger design across three teleoperators, with 'secure' judged by visually checking for slip under hand-applied probes of up to 3 N and no confidence intervals or significance tests, is enough evidence that the three-finger design reliably beats flat fingers.

Editorial extensions

If this is right

  • If the central claim is correct, any existing parallel-jaw robot can adopt the finger geometry without changing its actuator, motor, or control interface, so the grasp improvements transfer to systems already deployed for teleoperation and data collection.
  • The parametric sizing means the same design can be scaled for arms of different payloads and workspaces; the paper demonstrates versions for three different robot platforms.
  • The tactile finger adds a force-sensing channel at roughly 1.3 N validation error without requiring a wrist force sensor, so learning-based policies could use force feedback from the same image stream that already records contact and proximity.
  • Rigid fingernails extend the range of graspable objects to small, flat, or clustered items like coins, paper clips, and rubber parts, which flat fingers typically cannot pick from a table or from clutter.

Reading between the lines

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

  • A natural extension the paper does not pursue is an autonomous grasp planner that uses the varying lever arm $L$ explicitly—for example, adjusting grasp position along the finger to optimize torque resistance for a given object size; the geometry suggests this is a tunable quantity rather than a fixed property.
  • The statics comparison predicts the three-finger advantage grows with the lever arm $L$, so one could expect diminishing returns once $L$ exceeds the object width; this is testable by measuring slip torque on scaled finger versions.
  • Because the experiments used human teleoperators, the consistent-outperformance claim is about what a skilled operator can achieve; whether learned policies trained on GET data retain the same advantage is an open consequence, not demonstrated here.
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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 / 6 minor

Summary. The manuscript introduces GET, a 1-DoF, three-fingered gripper attachment for standard parallel-jaw actuators. The design pairs two V-shaped fingers against one opposing finger, adds rigid fingernails for small-object acquisition, and is parametrically scaled for ALOHA, Franka Panda, and UMI embodiments. A camera-based tactile sensor in the single finger estimates normal force from difference images via a ResNet-18 network. The authors present a geometric/torque argument for why three contacts improve grasp security, then report experiments: force-estimation validation (Table I), grasp-success comparisons on 15 objects (Fig. 9), and three teleoperated manipulation tasks (Fig. 10), with GET compared against ALOHA ViperX fingers and a custom 'traditional' flat-finger design. The central claim is that GET 'consistently outperformed standard flat fingers' in these experiments.

Significance. If the empirical claim is substantiated, this is a useful, low-cost, retrofittable gripper design that improves robustness without adding actuated degrees of freedom, which matters for learning-from-demonstration pipelines and in-the-wild data collection. The open-source release of CAD files and the integration of proximity-plus-tactile imaging are strong practical contributions. The force-estimation network is trained on held-out validation and unseen-object data rather than fitted to the reported experiments, which is a strength. The main unresolved issue is the statistical support for the central comparative claim, which currently rests on aggregated Bernoulli trials without uncertainty quantification.

major comments (4)
  1. [Section IV-B, Fig. 9] The object-level comparison rests on 30 trials per finger per object (10 trials x 3 operators) aggregated into bar heights with no confidence intervals, significance tests, or per-operator breakdown. At n = 30, a 20-percentage-point difference (e.g., 80% vs 60%) is not statistically significant (two-proportion z about 1.7, p about 0.08), so many of the visually large differences in Fig. 9 are within sampling noise. Additionally, the 'secure' criterion for large objects is operator-judged (no visual slip under manually applied probes 'up to 3 N') with no blinding or inter-rater reliability measure. Please report raw success counts per operator, exact confidence intervals (e.g., Clopper-Pearson), and appropriate significance tests (e.g., Fisher exact or McNemar for paired trials), and either blind the slip assessment or demonstrate inter-rater agreement. Without these, the central claim of consistent superiority is not supported.
  2. [Section IV-C, Fig. 10] The task-level claim that 'GET fingers more successfully completed all tasks' is based on 20 attempts per gripper per task, again with no confidence intervals, significance testing, or per-operator variability. The completion-time metric is the average over successful trials, but the number of successful trials is very small for some baselines (the figure labels suggest as few as 1-2 successful trials for some conditions), making the reported means unstable. Please provide the number of successes per condition, confidence intervals for success rates, per-operator results, and a clearly defined pass/fail criterion for each of the three tasks.
  3. [Section III-A1, Fig. 3b] The torque-capacity formulas |tau_x| <= Fmax(w + L/2) and |tau_z| <= mu Fmax(w + L/2) assume that each of the three contacts can independently exert the full actuation force Fmax and that L is a fixed geometric parameter. In a 1-DoF parallel jaw, the actuation force is shared among the contacts, and the normal-force distribution depends on object geometry and compliance; the formulas should be derived from an actuator-level force balance or explicitly qualified as an upper bound. Also, because the V-shape makes L vary along the finger, the text should specify where L is evaluated for a given grasp. The qualitative point that a third contact can increase torque resistance is plausible, but the specific expressions are not justified by the text as written.
  4. [Section IV-A, Table I and abstract] The abstract states that the network was trained 'with an average validation error of 1.3 N', but Table I reports a combined validation RMSE of 1.34 N and unseen-object errors of 1.40, 2.65, and 2.83 N (average 2.29 N). Please state the metric explicitly (RMSE versus MAE) and clearly distinguish validation-set performance from unseen-object generalization; the current wording overstates generalization accuracy. In addition, clarify whether the 20% validation split was object-disjoint or a random frame split, since random splits of repeated indentations can optimistically bias the validation RMSE.
minor comments (6)
  1. [Section III-A2] The term 'self-similarity' is used as an analogy, but no actual self-similar structure or scaling law is defined; consider renaming this 'parametric scaling' or providing a concrete scaling argument.
  2. [General] There are several typos, including 'desgins' in Fig. 6 and 'compatability' in Section III-A2; please correct them.
  3. [Section IV-C] Fig. 10 reports average completion time over successful trials without any measure of spread or the number of successes; please add error bars or a table with per-condition success counts and completion times.
  4. [Section IV-A] The paper states that designs are 'available on GitHub' but provides no repository URL; please include the link in the final version.
  5. [Section I, contributions] The claim of 'low cost (less than $100)' would be more credible with a brief bill of materials or component cost breakdown.
  6. [Table I] For the unseen-object rows, please state explicitly that these 800 images per object were not used in training; as written, the reader cannot determine whether these entries are part of the training set.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the gripper's claimed advantages are supported by independent mechanical analysis and empirical comparisons, not by construction or self-citation.

full rationale

The paper's central claims are empirical and mechanical rather than definitional. The torque-resistance analysis in Section III-A1 derives bounds from independently stated parameters (w, L, mu, Fmax) and does not assume the grasping results it later reports; the bounds are model predictions, not fits to the object-grasping experiments. The tactile force-sensing network is trained on a held-out validation split and separately evaluated on three unseen objects, so the reported 1.3 N average validation error and unseen-object errors are genuine generalization results, not re-labeled training performance. The grasping and manipulation evaluations compare three physical finger designs under teleoperation, with no fitted parameter being reused as a 'prediction' of the same experiments. Self-citations to prior Adelson-group work (e.g., GelSight, GelSight Fin Ray, and tactile policy papers) are used as background technology and inspiration, not as the load-bearing justification for the gripper's claimed outperformance. No step in the derivation chain reduces by construction to its own inputs, and no uniqueness theorem or ansatz is smuggled in via citation. Therefore, the appropriate finding is no significant circularity.

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

The grasping claim rests on a standard contact-mechanics model with hand-chosen geometry, none of which is fitted to the outcome data. The tactile force model is an empirical neural network with measured validation error; its weights are fitted to collected data but the central grasping comparison does not depend on it.

free parameters (3)
  • Finger pitch angle theta = not reported
    The inward pitch angle (Fig. 4) is a hand-chosen design parameter intended to prevent outward finger bending; no value or tuning study is given.
  • Finger length dimensions = 75 mm (ALOHA), 112.5 mm (Panda), 60 mm (UMI)
    Lengths are chosen per embodiment based on a self-similarity argument; the scaling is not experimentally validated across sizes.
  • Gel pad thickness at base = 4.5 mm
    Gel thickness profile is a design choice affecting compliance and contact area; no optimization or ablation is reported.
assumptions (5)
  • domain assumption Coulomb friction model at each contact with coefficient mu
    The torque limits in Fig. 3b are derived assuming rigid contacts with Coulomb friction; the actual soft gel pads are viscoelastic and may have different slip behavior.
  • domain assumption Rigid finger assumption for the 2D geometric analysis
    Fig. 3a analyzes grasps assuming rigid fingers; the fabricated fingers have compliant gel that deforms, which changes contact geometry.
  • domain assumption The 3-finger contact set is force-closed in two-against-one configuration
    The paper assumes three contacts in a two-against-one arrangement resist rotation about the grasp axis better than two antipodal contacts; this is plausible but not formally proven beyond the 2D examples.
  • ad hoc to paper Self-similarity justifies parametric rescaling
    Section III-A2 uses self-similarity as the motivation for resizing fingers across embodiments, but no measurement verifies that grasp performance is preserved after rescaling.
  • domain assumption Interdigitation allows zero-pitch contact for thin objects
    The design claim that fingers bend flat to 0 degrees without interference, enabling infinitesimally thin objects, is stated but not geometrically verified.

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Cite this review

Pith. "Pith review of Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping." pith.science (2026). https://pith.science/paper/XJDX3MV5

@misc{pith2026250509771,
  author       = {Pith},
  title        = {Pith review of: Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XJDX3MV5}},
  note         = {Machine review of arXiv:2505.09771}
}
read the original abstract

We introduce the Grasp EveryThing (GET) gripper, a novel 1-DoF, 3-finger design for securely grasping objects of many shapes and sizes. Mounted on a standard parallel jaw actuator, the design features three narrow, tapered fingers arranged in a two-against-one configuration, where the two fingers converge into a V-shape. The GET gripper is more capable of conforming to object geometries and forming secure grasps than traditional designs with two flat fingers. Inspired by the principle of self-similarity, these V-shaped fingers enable secure grasping across a wide range of object sizes. Further to this end, fingers are parametrically designed for convenient resizing and interchangeability across robotic embodiments with a parallel jaw gripper. Additionally, we incorporate a rigid fingernail for ease in manipulating small objects. Tactile sensing can be integrated into the standalone finger via an externally-mounted camera. A neural network was trained to estimate normal force from tactile images with an average validation error of 1.3 N across a diverse set of geometries. In grasping 15 objects and performing 3 tasks via teleoperation, the GET fingers consistently outperformed standard flat fingers. All finger designs, compatible with multiple robotic embodiments, both incorporating and lacking tactile sensing, are available on GitHub.

Figures

Figures reproduced from arXiv: 2505.09771 by the authors.

Figure 1
Figure 1. Grasp EveryThing (GET) Gripper. (a) Holding a toy hammer with simultaneous tactile image recorded from HDR camera at the back of the single finger side. (b) The GET gripper is designed to be easily integrated onto parallel grippers commonly found on existing in-the-wild data collection systems with added torque constraint. We demonstrate its capability in completing a variety of household tasks through teleoperation… view at source ↗
Figure 2
Figure 2. Assembly of GET components. Left: Rendered exploded view of mechanical and electronic components in each finger, arranged in order of assembly. Parts that are made of Onyx are 3D printed on a Markforged printer. Right: Fabricated set of GET fingers with rigid fingernails highlighted. II. RELATED WORKS A. Robotic Gripper Design Rigid, parallel jaw grippers are easy to model and control due to their simple geometry an… view at source ↗
Figure 3
Figure 3. Grasping analysis. (a) Planar geometric analysis of grasping a set of objects with rigid, 2-finger parallel jaw gripper and our 3-fingered design. (b) Free-body diagram in 3D comparing grasping of a toy hammer with a 2-finger gripper and the GET gripper, where Fmax is the maximum actuation force by the parallel gripper, µ is the coefficient of friction, w is the width of fingers, L is the distance between fingers, a… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Orthogonal views of GET fingers. Lever arm of grasp L, finger pitch θ, and fingernail dimensions are defined. Smaller objects should be grasped at the tip of the fingers where L is small and larger objects at the base. compensation about both xˆ and zˆ-axes without the…
Figure 5
Figure 5. Figure 5: Fabrication process of GET fingers. Required steps to fabricate designed fingers, including molding of silicone gels, preparing plastic backings, and adhering soft silicone fingerpads to backings. In total, process takes multiple days to complete with bulk of the time …
Figure 6
Figure 6. Figure 6: Scalable finger design. Designs are parametrized by finger length for use across robotic embodiments. To demonstrate this flexibility, fingers are fabricated for both the ALOHA system [1] and Franka Emika Panda arm. along the edges of the finger to illuminate the senso…
Figure 7
Figure 7. Figure 7: Integrated tactile sensing in GET finger. (a) Tactile images recorded from contact with a set of objects. Difference images are calculated with respect to an uncontacted reference image. (b) Left: Data collection process using force probe with interchangeable indentati…
Figure 8
Figure 8. Figure 8: Grasping small objects with rigid fingernail. The process of grasping small objects, such as a coin and paper clip, off of a flat surface with GET fingers. The nail is used to pry underneath the object and lift it off the table. Grasping is executed via teleoperation o…
Figure 9
Figure 9. Figure 9: Performance in grasping objects. 15 objects of varying size and shape were grasped using GET, ALOHA ViperX [1], and traditional parallel jaw fingers. Success in grasping small objects is defined as the ability to grab them off a table. Success in grasping large objects…
Figure 10
Figure 10. Figure 10: Performance in manipulation tasks. 3 manipulations tasks, as defined on the left, were completed using GET, ALOHA ViperX [1], and traditional parallel jaw fingers. Completion time is defined as the average over successful trials for each gripper. Results are reported …

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

34 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Learning fine- grained bimanual manipulation with low-cost hardware,

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn, “Learning fine- grained bimanual manipulation with low-cost hardware,” arXiv preprint arXiv:2304.13705, 2023

    arXiv 2023

  2. [2]

    Diffusion policy: Visuomotor policy learning via action diffusion,

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y . Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , 2023

    work page 2023

  3. [3]

    Survey of imitation learning for robotic manipulation,

    B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun, “Survey of imitation learning for robotic manipulation,” International Journal of Intelligent Robotics and Applications , vol. 3, 2019

    work page 2019

  4. [4]

    Poco: Policy composition from and for heterogeneous robot learning,

    L. Wang, J. Zhao, Y . Du, E. H. Adelson, and R. Tedrake, “Poco: Policy composition from and for heterogeneous robot learning,” arXiv preprint arXiv:2402.02511, 2024

    arXiv 2024

  5. [5]

    Universal manipulation interface: In-the-wild robot teach- ing without in-the-wild robots,

    C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teach- ing without in-the-wild robots,” in Proceedings of Robotics: Science and Systems (RSS) , 2024

    work page 2024

  6. [6]

    Aloha 2: An enhanced low-cost hardware for bimanual teleoperation,

    J. Aldaco, T. Armstrong, R. Baruch, J. Bingham, S. Chan, K. Draper, D. Dwibedi, C. Finn, P. Florence, S. Goodrich et al. , “Aloha 2: An enhanced low-cost hardware for bimanual teleoperation,” arXiv preprint arXiv:2405.02292, 2024

    arXiv 2024

  7. [7]

    Grasping,

    D. Prattichizzo and J. Trinkle, “Grasping,” Springer Handbook of Robotics, pp. 671–700, Sep. 2008

    work page 2008

  8. [8]

    Comparison of contact capabilities for underactuated parallel jaw grippers for use on industrial robots,

    R. Carpenter, R. Hatton, and R. Balasubramanian, “Comparison of contact capabilities for underactuated parallel jaw grippers for use on industrial robots,” in International Design Engineering Technical Con- ferences and Computers and Information in Engineering Conference , vol. 46377. American Society of Mechanical Engineers, 2014

    work page 2014

Show all 34 references
  1. [9]

    Vital pre- training: Visuo-tactile pretraining for tactile and non-tactile manipulation policies,

    A. George, S. Gano, P. Katragadda, and A. B. Farimani, “Vital pre- training: Visuo-tactile pretraining for tactile and non-tactile manipulation policies,” 2024

    2024

  2. [10]

    Tedrake, Robotic Manipulation

    R. Tedrake, Robotic Manipulation . MIT, 2024. [Online]. Available: http://manipulation.mit.edu

    2024

  3. [11]

    Fin ray® effect inspired soft robotic gripper: From the robosoft grand challenge toward optimization,

    W. Crooks, G. Vukasin, M. O’Sullivan, W. Messner, and C. Rogers, “Fin ray® effect inspired soft robotic gripper: From the robosoft grand challenge toward optimization,” Frontiers in Robotics and AI , vol. 3, 2016

    2016

  4. [12]

    Gelsight fin ray: Incorporating tactile sensing into a soft compliant robotic gripper,

    S. Q. Liu and E. H. Adelson, “Gelsight fin ray: Incorporating tactile sensing into a soft compliant robotic gripper,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft) . IEEE, 2022

    2022

  5. [13]

    Soft-bubble grippers for robust and perceptive ma- nipulation,

    N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive ma- nipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020

    2020

  6. [14]

    Spot® - the agile mobile robot

    “Spot® - the agile mobile robot.” [Online]. Available: https: //bostondynamics.com/products/spot/

  7. [15]

    A century of robotic hands,

    C. Piazza, G. Grioli, M. G. Catalano, and A. Bicchi, “A century of robotic hands,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, no. 1, 2019

    2019

  8. [16]

    Gelsight svelte hand: A three-finger, two- dof, tactile-rich, low-cost robot hand for dexterous manipulation,

    J. Zhao and E. H. Adelson, “Gelsight svelte hand: A three-finger, two- dof, tactile-rich, low-cost robot hand for dexterous manipulation,” arXiv preprint arXiv:2309.10886, 2023

    2023 arXiv

  9. [17]

    Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning,

    K. Shaw, A. Agarwal, and D. Pathak, “Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning,” 2023

    2023

  10. [18]

    From grasping to manipulation with gecko-inspired adhesives on a multifinger gripper,

    W. Ruotolo, D. Brouwer, and M. R. Cutkosky, “From grasping to manipulation with gecko-inspired adhesives on a multifinger gripper,” Science Robotics , vol. 6, no. 61, p. eabi9773, 2021

    2021

  11. [19]

    Dexo: Hand exoskeleton system for teaching robot dexterous manipu- lation in-the-wild,

    H.-S. Fang, B. Romero, A. Hu, L. Wang, E. Adelson, and P. Agrawal, “Dexo: Hand exoskeleton system for teaching robot dexterous manipu- lation in-the-wild,” 2025

    2025

  12. [20]

    Optimized fingertip mapping for teleoperation of dextrous robot hands,

    R. N. Rohling and J. M. Hollerbach, “Optimized fingertip mapping for teleoperation of dextrous robot hands,” in [1993] Proceedings IEEE International Conference on Robotics and Automation . IEEE, 1993

    1993

  13. [21]

    Soft robotic grippers,

    J. Shintake, V . Cacucciolo, D. Floreano, and H. Shea, “Soft robotic grippers,” Advanced Materials , vol. 30, no. 29, p. 1707035, 2018

    2018

  14. [22]

    Gelsight: High-resolution robot tactile sensors for estimating geometry and force,

    W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors, vol. 17, no. 12, 2017

    2017

  15. [23]

    Gelsight wedge: Measur- ing high-resolution 3d contact geometry with a compact robot finger,

    S. Wang, Y . She, B. Romero, and E. Adelson, “Gelsight wedge: Measur- ing high-resolution 3d contact geometry with a compact robot finger,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021

    2021

  16. [24]

    Rainbowsight: A family of general- izable, curved, camera-based tactile sensors for shape reconstruction,

    M. H. Tippur and E. H. Adelson, “Rainbowsight: A family of general- izable, curved, camera-based tactile sensors for shape reconstruction,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024

    2024

  17. [25]

    Learning object compliance via young’s modulus from single grasps using camera-based tactile sensors,

    M. Burgess, J. Zhao, and L. Willemet, “Learning object compliance via young’s modulus from single grasps using camera-based tactile sensors,” 2025

    2025

  18. [26]

    Physics-informed online estimation of compliance with gelsight sensor,

    L. Willemet, M. Burgess, and E. Adelson, “Physics-informed online estimation of compliance with gelsight sensor,” in 8th IEEE-RAS Inter- national Conference on Soft Robotics Extended Abstracts , 2025

    2025

  19. [27]

    Survey of imitation learning for robotic manipulation,

    B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun, “Survey of imitation learning for robotic manipulation,” Int. J. Intell. Robot. Appl. , vol. 3, no. 4, pp. 362–369, Dec. 2019

    2019

  20. [28]

    Poly- touch: A robust multi-modal tactile sensor for contact-rich manipulation using tactile-diffusion policies,

    J. Zhao, N. Kuppuswamy, S. Feng, B. Burchfiel, and E. Adelson, “Poly- touch: A robust multi-modal tactile sensor for contact-rich manipulation using tactile-diffusion policies,” arXiv, 2025

    2025

  21. [29]

    Multimodal proximity and visuotactile sensing with a selectively transmissive soft membrane,

    J. Yin, G. M. Campbell, J. Pikul, and M. Yim, “Multimodal proximity and visuotactile sensing with a selectively transmissive soft membrane,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft) . IEEE, 2022

    2022

  22. [30]

    Fractals and self similarity,

    J. E. Hutchinson, “Fractals and self similarity,” Indiana University Mathematics Journal, vol. 30, no. 5, 1981

    1981

  23. [31]

    A soft gripper with retractable nails for advanced grasping and ma- nipulation,

    S. Jain, T. Stalin, V . Subramaniam, J. Agarwal, and P. V . y Alvarado, “A soft gripper with retractable nails for advanced grasping and ma- nipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020

    2020

  24. [32]

    Improvement of precision grasping performance by interaction between soft finger pulp and hard nail,

    A. Kumagai, Y . Obata, Y . Yabuki, Y . Jiang, H. Yokoi, and S. Togo, “Improvement of precision grasping performance by interaction between soft finger pulp and hard nail,” Soft Robotics , vol. 10, no. 2, 2023

    2023

  25. [33]

    Under-actuated robotic gripper with multiple grasping modes inspired by human finger,

    J. Li, T. Liao, H. Nigatu, H. Guo, G. Lu, and H. Dong, “Under-actuated robotic gripper with multiple grasping modes inspired by human finger,” 2024

    2024

  26. [34]

    Allsight: A low-cost and high-resolution round tactile sensor with zero-shot learning capability,

    O. Azulay, N. Curtis, R. Sokolovsky, G. Levitski, D. Slomovik, G. Lilling, and A. Sintov, “Allsight: A low-cost and high-resolution round tactile sensor with zero-shot learning capability,” IEEE Robotics and Automation Letters , vol. 9, no. 1, Jan. 2024

    2024

Pith tools

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