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REVIEW 4 major objections 5 minor 63 references

Learning thin deformable object manipulation with a multi-sensory integrated soft hand

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

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Pith's one-line read This paper claims that a single reinforcement-learning policy, trained only on printer paper and winter fabric on flat surfaces, can zero-shot singulate and grasp a single layer from stacks of many unseen thin deformable objects.

desk verdict A solid, honestly reported engineering result that deserves peer review; the pre-grasp-slip limitation is real but the paper already concedes it. read the letter →

arxiv 2411.13952 v2 pith:OPUC4WVE submitted 2024-11-21 cs.RO

Chao Zhao , Chunli Jiang , Lifan Luo , Shuai Yuan , Qifeng Chen , Hongyu Yu This is my paper · ORCID
classification cs.RO
keywords thindeformableobjectmanipulationsoftrobotichandreinforcementlearningtactilesensingforce/torquepassivecompliancesingulationzero-shotgeneralization
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 tries to establish that a robot can learn to singulate and grasp a single layer from a stack of thin, deformable objects without precise control, precise perception, or explicit object modeling. Its system pairs a soft, underactuated two-finger hand with fingertip tactile sensors, a wrist force/torque sensor, and a depth camera, and it learns a model-free reinforcement-learning policy directly from raw sensory data. The key move is a pre-grasp "slip" interaction: the robot slides a finger across the object surface to collect a rich multisensory snapshot that reveals hidden physical properties such as friction and stiffness. Trained only on printer paper and winter suit fabric on flat surfaces, the policy is reported to generalize zero-shot to coated paper, plastic paper, hotel towels, T-shirts, hybrid paper/fabric stacks, and workspaces tilted up to 60 degrees. If correct, this suggests that the complexity of thin-object manipulation can be tamed by embodied multi-sensory integration and passive compliance rather than by high-precision control.

What carries the argument

The load-bearing mechanism is the slip module: before each grasp, the robot uses a pretrained fully convolutional network to select a slip point and direction on the object (informed by human-annotated examples and a pretrained object-mask model), then presses a soft fingertip onto the surface and slides it for one second. This single interaction yields three sensory streams that become the policy's observation: a $40 \times 40$ depth crop around the slip point, two $25 \times 25 \times 3$ tactile deformation fields from the index and thumb fingertips, and a 6-axis force/torque reading. A cross-attention and transformer encoder fuses these modalities into a latent state, and a dual-loop structure first chooses whether to act in a coarse or fine action space and then outputs the concrete gripper displacement $(x_t, z_t, \theta_t)$ and finger-close command. The soft, pneumatically actuated hand provides passive compliance that maintains gentle contact despite positioning errors, which the paper shows in fixed-action experiments by measuring force ranges and allowable vertical offsets. This combination—active probing, multimodal fusion, and compliant execution—is what the paper identifies as making model-free learning feasible and zero-shot generalization possible.

What would settle it

Take two stacks of pages that are visually and dimensionally identical and produce indistinguishable slip-module readings, but glue a few page pairs along the edge in one stack; if the policy cannot separate the glued stack while still separating the normal stack, the slip sample is not carrying the assumed inter-layer information in a way the policy can use.

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Extended reading notes

Core claim

The paper's central claim is that a single RL policy, trained in the real world on just a printer-paper book and a winter-suit-fabric booklet lying flat, can singulate and grasp a single layer from stacks of thin deformable objects it has never seen. On 200-trial tests per scenario, the paper reports 97% success for coated paper, 92% for plastic paper, 98% for hotel towels, 86% for T-shirts, lower but positive rates on hybrid paper/fabric and baking-paper/pancake stacks, and sustained performance when the workspace is tilted up to 60 degrees. The paper attributes this to three components working together: passive compliance of the soft hand, a slip module that actively probes the object to collect pre-grasp multisensory observations, and a hierarchical double-loop learner that selects between coarse and fine action spaces. The system deliberately avoids object models and precise control; the policy learns end-to-end from raw visual, tactile, and force/torque readings via model-free reinforcement learning. The authors frame the result as evidence for "imprecise dexterity"—reliable manipulation achieved through adaptive behavior rather than precision.

Load-bearing premise

The load-bearing premise is that one pre-grasp slip interaction, sampled once and never updated after the fingers close, provides enough information about inter-layer friction and adhesion for the policy to choose a successful action.

Editorial extensions

If this is right

  • If the central claim holds, a single policy can transfer across materials with very different thickness, stiffness, and surface friction without retraining or fine-tuning.
  • The dual-loop action-space selection implies that the appropriate control granularity is itself a learnable, perception-dependent decision, not a fixed hyperparameter.
  • Passive compliance, rather than impedance control or precise force feedback, may be sufficient to protect thin objects and maintain contact under vertical uncertainty.
  • Because the slip module is the only component retrained for new object categories (e.g., bags, garment collars), the policy itself may be reusable across tasks as long as the pre-grasp probe remains informative.
  • The reported failure modes—stuck pages and low-friction pairs lifting together—delimit the approach to stacks where inter-layer friction and adhesion are separable by one slip interaction.

Reading between the lines

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

  • An obvious next step the paper leaves implicit is closing the loop after the fingers close: using post-grasp tactile or force readings to detect a multi-layer grasp and either release or re-slip, which would directly address the dominant failure mode.
  • If the slip sample is the bottleneck, then a policy that actively chooses where and how to slip (rather than using a fixed one-second motion) could extract more information and extend the approach to low-friction or adhesive pairs.
  • The same architecture could be transferred to other thin-object tasks—such as sheet sorting, document scanning, or food assembly—where the objects are thin, stacked, and variable, provided slip annotations can be generated for the new object type.
  • The paper's reliance on depth-only vision suggests that adding RGB texture cues to the visual observation could let the policy exploit material appearance, at the cost of retraining when colors change.
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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 learning-based system for singulating and grasping a single layer from stacks of thin, deformable objects. The hardware is a soft, underactuated two-finger pneumatic hand with fingertip tactile sensors, a wrist-mounted force/torque sensor, and a wrist RGB-D camera. The method first uses a pretrained slip module to choose a slip point and direction; the robot then performs a brief pre-grasp slip contact, collecting one depth crop, two tactile deformation fields, and one six-axis F/T reading. A hierarchical double-loop reinforcement learning policy (SAC) then selects either a coarse or fine action space and outputs a concrete gripper displacement and closing action. The policy is trained in the real world on only a printer-paper book and a winter-suit-fabric booklet on flat workspaces, with an automatic reward based on page numbers or QR codes. The authors report zero-shot transfer to coated paper, plastic paper, hotel towels, T-shirts, hybrid paper/fabric stacks, pancake/baking-paper stacks, and workspaces tilted up to 60 degrees, with success rates between 86% and 98% for the main novel objects, plus ablations and failure analyses.

Significance. If the reported results hold, this is a substantial empirical advance: it demonstrates that a single policy trained on two flat-surface objects can generalize to a wide range of unseen thin deformable objects, and it provides an existence proof for the value of passive compliance plus multimodal pre-grasp exploration in avoiding the need for precise models or precise control. The manuscript deserves credit for real-robot experiments with 200 trials per scenario, an honest failure analysis that attributes 11 of 12 page-turning failures to adhesion, and ablations (OV, NT, NF, SL, NA). The paper also includes supplementary videos, hardware design details, and open data/code links. However, the central mechanistic claim that the one-shot slip observation captures inter-layer friction is not supported by the observation design, and several quantitative comparisons lack confidence intervals and statistical tests. The empirical core is promising, but the interpretation and statistical reporting need revision.

major comments (4)
  1. [Section III-C and Section S3] The observation set is defined in Section III-C as one 40x40 depth crop, two 25x25x3 tactile deformation fields, and one 6-axis F/T reading, all recorded while a single fingertip slides over the top surface before finger closure. Section IV-C states that this sensory information captures 'interlayer friction' and enables the policy to adapt actions accordingly, but the slip procedure never separates the layers or probes the interface between layer A and layer B. The paper's own failure analysis in Section S3 identifies exactly this inter-layer friction/adhesion contrast as the dominant failure cause: 11 of 12 page-turning failures were caused by stuck pages, and low-friction layer pairs lift together. The stated mechanism for successful generalization is therefore not established by the observation design, even though the external success criterion (exactly one layer lifted) is not circular. To support the mechanistic claim, the authors should either rephrase the claim to say the policy uses top-layer compliance, thickness, or positional cues, or provide a controlled experiment in which top-layer properties are held constant and only the inter-layer friction/adhesion contrast is varied.
  2. [Section IV-A, Fig. 6, Table I] Success rates are reported as point estimates without confidence intervals, per-cell trial counts, or statistical tests. For example, Section IV-A gives success rates of 97%, 92%, 98%, and 86% for new objects, but the heatmaps in Fig. 6D and the tilt-angle results in Fig. 6G do not show error bars or the number of trials per cell. The text in Section IV-E claims that OV has a 'low success rate' and that the full method 'significantly outperforms' SL and NA, but the underlying differences could be within a few trials of each other for the reported 200-trial protocol; Table I also shows a position-based policy reaching 80% on the printer book, close to the learned policy's 94% in Section S3. The quantitative component-wise conclusions need confidence intervals, trial counts, and ideally a statistical test, or the wording should be relaxed.
  3. [Section IV-D] The claim that the soft hand's passive compliance is indispensable relies on a comparison with a rigid gripper, but no quantitative data for the rigid gripper are provided. The text states only that 'neither the position-based policies nor manually set actions were successful' with a rigid gripper. Because the central contribution includes the soft hand, the paper should report at least the number of attempts and the failure modes (collisions, emergency stops, slippage, or object damage) for the rigid gripper condition. Without that, the statement that 'a feat unachievable with rigid grippers' is not quantitatively supported.
  4. [Section IV-E and Fig. 6H] The SingleLoop ablation comparison is presented as evidence that the dual-loop structure accelerates learning, but Fig. 6H shows training curves without defined axes or error bars, and no convergence-time or sample-efficiency numbers are given in the text. The claim 'our method significantly outperforms the SL method within the same timeframe' needs a precise definition of the timeframe, the number of independent training runs, and the success-rate variance across runs. This is load-bearing because the dual-loop structure is one of the four stated contributions.
minor comments (5)
  1. [Section III-C and Section S5] The fine action-space range for z (-3 to 3 mm) is stated in Section S5 to approximate the thickness of 60 sheets of printer paper or 10 layers of winter fabric, and the 3 mm slip standoff in Section III-C is 'determined empirically.' These parameters are hand-chosen from the training objects, so the phrase 'without further tuning' in Section IV-A should be qualified to acknowledge that the action-space granularity and slip standoff were fixed for the training distribution.
  2. [Section IV-B, Fig. 6] The text in Section IV-B refers to 'Fig. 6G' for the tilt-angle success-rate distribution, but the caption for Fig. 6 labels panel G as 'Training curves' and panel H as a boxplot; the panel references appear inconsistent and should be corrected.
  3. [Section S2, Table S1] The bag-opening and garment experiments in Section S2 use an additional annotated dataset to retrain the slip module. These results are interesting but should not be described as 'zero-shot' for the bag task; the main-text abstract and Section IV-A only claim zero-shot for the manipulation policy itself, so the distinction should be made explicit.
  4. [Throughout] Several minor language errors remain, for example 'makes robot impossible to discover effective policies' in Section I, and 'appearing feature dependence' in Section IV-G. These should be corrected in a copyedit pass.
  5. [Section S10, Table S5] The size-generalization table reports small differences across sizes (e.g., 90%, 91%, 89% for printer paper) without confidence intervals. Given the large success rates, this is likely fine, but the absence of trial counts makes it impossible to assess whether the small differences are meaningful.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; the reported generalization is externally benchmarked and the self-citations to Flipbot are disclosed, not load-bearing.

full rationale

No circularity found. The success criterion is defined externally: a positive reward is given only for grasping a single layer, determined automatically from page numbers in the book environment and QR-code sequences in the fabric environment (Section III-B3). The reported success rates for coated paper, plastic paper, hotel towels, T-shirts, hybrid stacks, and tilted workspaces are direct measurements on held-out objects using the same frozen policy, not quantities derived from the training data or from the definition of the method. The policy is trained only on printer paper and winter fabric, and the test objects are explicitly described as unseen (Section IV-A). The authors disclose their inheritance from their own prior work: 'This journal paper evolves upon the conference paper presented in [9]' and 'Compared with the gripper in Flipbot [9], we maintained its general design but augmented its sensory capabilities.' These self-citations concern hardware design, the reset mechanism, and the automatic reward signal; they do not define the generalization outcome, and the experimental evidence stands independently. The empirically chosen 3 mm slip standoff and the action-space granularities (Section III-C and Section S5) are disclosed design inputs, not predictions presented as results. The limitation stated in Section IV-G that 'the reliance on pre-obtained multisensory information has its inherent limitations' and the failure analysis in Section S3 (11 of 12 page-turning failures from page adhesion) are honest assessments of failure modes; they bear on correctness and generalization, not on circularity, because the information-sufficiency question is an empirical question rather than an identity between inputs and outputs. No equation in the paper reduces to its own input, and no uniqueness theorem or ansatz is imported from the authors' prior work as a load-bearing premise.

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

The paper contributes an engineered system rather than a derivation, so the ledger is dominated by design choices (action-space granularity, slip geometry, dwell time, episode length) and domain assumptions (pre-grasp slip sensing is sufficient, compliance suffices in lieu of precision, annotated slip priors generalize). Each listed item affects the reported success rates, and the paper discloses most of them explicitly in Sections III-B, III-C, S5, and S6. No new physical entities, forces, or conserved quantities are introduced.

free parameters (5)
  • Fine/coarse action-space ranges and discretization = fine z in [-3,3] mm at 1 mm; coarse z in [-7.5,7.5] mm; fine x +/-7.5 mm at 5 mm; coarse x +/-18.7 mm; fine theta 0-3…
    Hand-chosen in Section S5 to approximate the thickness of 60 sheets of printer paper or 10 fabric layers; the policy can only express actions inside these ranges, so the headline success rates depend on them.
  • Slip standoff distance = 3 mm
    Section III-C states this was determined empirically as a balance between avoiding unintended collisions and ensuring reliable surface contact during the slip.
  • Slip direction discretization = 15 deg steps within (-90, 90) deg
    Section S6 fixes the affordance map resolution for slip direction; finer or coarser sampling changes the initial-pose prior fed to the policy.
  • Slip contact dwell time = 1 s
    Section III-C records sensory information 1 s after air pressure inflates the finger; this window determines the tactile and F/T features the policy sees.
  • Episode length = 1 step
    Section III-B fixes the episode length to one, which with the stated infinite-horizon SAC objective makes the learner effectively a contextual bandit and bypasses temporal credit assignment.
assumptions (5)
  • standard math MDP formulation and Soft Actor-Critic convergence
    Section III-B models the task as an MDP and uses SAC [55]; standard background assumed without proof.
  • domain assumption Passive compliance of the soft hand absorbs positioning error without precise control
    Central to the 'imprecise dexterity' premise; Section IV-D provides supporting experiments (position-based policies at 47-80% success) but the premise is assumed at the outset.
  • domain assumption A single pre-grasp slip interaction produces observations sufficient to select a successful action
    Section III-C collects touch, F/T, and cropped depth once before grasping; the policy has no post-grasp feedback, so all action-selection information must be contained in that one slip sample.
  • domain assumption Human-annotated slip points encode generalizable affordances
    Section III-C trains the slip network on a few annotated images of a book, a shirt, and a spring pancake with augmentation; Section IV-G concedes the slip module may need retraining for novel object structures.
  • domain assumption Page-number and QR-code reading correctly identify single-layer success
    Section III-B makes the reward automatic by reading page numbers or a QR sequence; this presumes the reader reliably detects a one-layer flip, the very property being rewarded.

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Cite this review

Pith. "Pith review of Learning thin deformable object manipulation with a multi-sensory integrated soft hand." pith.science (2026). https://pith.science/paper/OPUC4WVE

@misc{pith2026241113952,
  author       = {Pith},
  title        = {Pith review of: Learning thin deformable object manipulation with a multi-sensory integrated soft hand},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OPUC4WVE}},
  note         = {Machine review of arXiv:2411.13952}
}
read the original abstract

Robotic manipulation has made significant advancements, with systems demonstrating high precision and repeatability. However, this remarkable precision often fails to translate into efficient manipulation of thin deformable objects. Current robotic systems lack imprecise dexterity, the ability to perform dexterous manipulation through robust and adaptive behaviors that do not rely on precise control. This paper explores the singulation and grasping of thin, deformable objects. Here, we propose a novel solution that incorporates passive compliance, touch, and proprioception into thin, deformable object manipulation. Our system employs a soft, underactuated hand that provides passive compliance, facilitating adaptive and gentle interactions to dexterously manipulate deformable objects without requiring precise control. The tactile and force/torque sensors equipped on the hand, along with a depth camera, gather sensory data required for manipulation via the proposed slip module. The manipulation policies are learned directly from raw sensory data via model-free reinforcement learning, bypassing explicit environmental and object modeling. We implement a hierarchical double-loop learning process to enhance learning efficiency by decoupling the action space. Our method was deployed on real-world robots and trained in a self-supervised manner. The resulting policy was tested on a variety of challenging tasks that were beyond the capabilities of prior studies, ranging from displaying suit fabric like a salesperson to turning pages of sheet music for violinists.

Figures

Figures reproduced from arXiv: 2411.13952 by the authors.

Figure 1
Figure 1. Our robot assists a violinist with page-turning during performances. robust and adaptive behaviors despite imprecision in control, movement, and perception. Singulation and grasping thin, deformable objects are com￾mon daily tasks, such as turning book pages ( [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the presented approach. (A) We train manipulation policies in the real world using RL. During training, the robot proactively interacts with the object to obtain multisensory observations via a slip module. The outer loop selects an action space based on observations, and the inner loop explores within it to output concrete actions for the robot to execute. After training, we deploy the learned policy ze… view at source ↗
Figure 3
Figure 3. Details of components for robust policy learning. (A) Our hardware setup for policy learning. Two robots explore and learn policies simultaneously in parallel environments, updating their policies based on the rewards received. (B) Multisensory encoder architecture. It incorporates a cross-attention mechanism gc and a transformer encoder gκ that merges features from different senses into a latent vector. FFN denotes… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Robust singulation and grasping in challenging scenarios. The policy learned by the presented method successfully overcame a full spectrum of challenging thin, deformable objects, which include various textures of paper (A to C, E to G), suit fabrics from different sea…
Figure 5
Figure 5. Figure 5: Zero-shot generalization to various gravity conditions. The inclinations of the workspace rise up to 60°. As the workspace’s inclinations increase, the physical interactions between layers change due to shifts in the direction of gravity. Our policy consistently demons…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Results for mechanics of the task and the robot. (A) Performance heatmap showing the success rate when repeating the same grasping action but adjusting the α-axis and β-axis offsets in the action. (B) Mean and range of forces along γ-axis during grasping for printer pa…
Figure 8
Figure 8. Figure 8: Analysis and visualization of multisensory information. (A to C) Visualization of feature clustering results by t-SNE. (D and E) Visualization of F/T measurement after slip motion, including forces (fα, fβ, f γ) and torques (mα, mβ, mγ), on different layers of the fabr…

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Reference graph

Works this paper leans on

63 extracted references · 57 canonical work pages

  1. [1]

    Deep learning approaches to grasp synthesis: A review,

    R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic et al. , “Deep learning approaches to grasp synthesis: A review,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3994–4015, 2023. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 19

    work page 2023

  2. [2]

    Toward next-generation learned robot manipulation,

    J. Cui and J. Trinkle, “Toward next-generation learned robot manipulation,” Sci. Robot. , vol. 6, no. 54, p. eabd9461, 2021

    work page 2021

  3. [3]

    Challenges and outlook in robotic manipulation of deformable objects,

    J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. Li et al., “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robotics & Automation Magazine , vol. 29, no. 3, pp. 67–77, 2022

    work page 2022

  4. [4]

    Covariant,

    “Covariant,” https://covariant.ai/. [Online]. Available: https://covariant.ai/

    work page

  5. [5]

    Boston dynamics,

    “Boston dynamics,” https://bostondynamics.com/. [Online]. Available: https://bostondynamics.com/

    work page

  6. [6]

    Multi-dimensional compliance of soft grippers enables gentle interaction with thin, flexible objects,

    C. B. Teeple, J. Werfel, and R. J. Wood, “Multi-dimensional compliance of soft grippers enables gentle interaction with thin, flexible objects,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, Conference Proceedings

    work page 2022

  7. [7]

    Differential contributions of vision, touch and muscle proprioception to the coding of hand movements,

    C. Blanchard, R. Roll, J.-P. Roll, and A. Kavounoudias, “Differential contributions of vision, touch and muscle proprioception to the coding of hand movements,” PLoS One , vol. 8, no. 4, p. e62475, 2013

    work page 2013

  8. [8]

    Learning to singulate layers of cloth using tactile feedback,

    S. Tirumala, T. Weng, D. Seita, O. Kroemer, Z. Temel, and D. Held, “Learning to singulate layers of cloth using tactile feedback,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, Conference Proceedings

    work page 2022

Show all 63 references
  1. [9]

    Flipbot: Learning continuous paper flipping via coarse-to-fine exteroceptive- proprioceptive exploration,

    C. Zhao, C. Jiang, J. Cai, M. Y . Wang, H. Yu, and Q. Chen, “Flipbot: Learning continuous paper flipping via coarse-to-fine exteroceptive- proprioceptive exploration,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, Conference Proceedings

    2023

  2. [10]

    Rotipbot: Robotic handling of thin and flexible objects using rotatable tactile sensors,

    J. Jiang, X. Zhang, D. F. Gomes, T.-T. Do, and S. Luo, “Rotipbot: Robotic handling of thin and flexible objects using rotatable tactile sensors,” arXiv preprint arXiv:2406.09332, 2024

    2024 arXiv

  3. [11]

    Autonomous learning of page flipping movements via tactile feedback,

    Y . Zheng, F. F. Veiga, J. Peters, and V . J. Santos, “Autonomous learning of page flipping movements via tactile feedback,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2734–2749, 2022

    2022

  4. [12]

    Bagging by learning to singulate layers using interactive perception,

    L. Y . Chen, B. Shi, R. Lin, D. Seita, A. Ahmad, R. Cheng, T. Kollar, D. Held, and K. Goldberg, “Bagging by learning to singulate layers using interactive perception,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp. 3176–3183

    2023

  5. [13]

    One policy to dress them all: Learning to dress people with diverse poses and garments,

    Y . Wang, Z. Sun, Z. Erickson, and D. Held, “One policy to dress them all: Learning to dress people with diverse poses and garments,” in Robotics: Science and Systems XIX . Robotics: Science and Systems Foundation, 2023, Conference Proceedings

    2023

  6. [14]

    Learning garment manipulation policies toward robot-assisted dressing,

    F. Zhang and Y . Demiris, “Learning garment manipulation policies toward robot-assisted dressing,” Sci. Robot. , vol. 7, no. 65, p. eabm6010, 2022

    2022

  7. [15]

    SpeedFolding: Learning efficient bimanual folding of garments,

    Y . Avigal, L. Berscheid, T. Asfour, T. Kroger, and K. Goldberg, “SpeedFolding: Learning efficient bimanual folding of garments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, Conference Proceedings

    2022

  8. [16]

    AutoBag: Learning to open plastic bags and insert objects,

    L. Y . Chen, B. Shi, D. Seita, R. Cheng, T. Kollar, D. Held, and K. Goldberg, “AutoBag: Learning to open plastic bags and insert objects,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, Conference Proceedings

    2023

  9. [17]

    Stable, open-loop precision manipulation with underactuated hands,

    L. U. Odhner and A. M. Dollar, “Stable, open-loop precision manipulation with underactuated hands,” The International Journal of Robotics Research, vol. 34, no. 11, pp. 1347–1360, 2015

    2015

  10. [18]

    Pre-grasp sliding manipulation of thin objects using soft, compliant, or underactuated hands,

    K. Hang, A. S. Morgan, and A. M. Dollar, “Pre-grasp sliding manipulation of thin objects using soft, compliant, or underactuated hands,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 662–669, 2019

    2019

  11. [19]

    Position-based simulation methods in computer graphics,

    J. Bender, M. M ¨uller, and M. Macklin, “Position-based simulation methods in computer graphics,” in Eurographics EG 2015 - Tutorials . The Eurographics Association, 2015, Conference Proceedings

    2015

  12. [20]

    Learning where to trust unreliable models in an unstructured world for deformable object manipulation,

    P. Mitrano, D. McOnachie, and D. Berenson, “Learning where to trust unreliable models in an unstructured world for deformable object manipulation,” Sci. Robot. , vol. 6, no. 54, p. eabd8170, 2021

    2021

  13. [21]

    Model-driven feedforward prediction for manipulation of deformable objects,

    Y . Li, Y . Wang, Y . Yue, D. Xu, M. Case, S.-F. Chang, E. Grinspun, and P. K. Allen, “Model-driven feedforward prediction for manipulation of deformable objects,” IEEE Trans. Autom. Sci. Eng. , vol. 15, no. 4, p. 1621–1638, 2018

    2018

  14. [22]

    A geometric approach to robotic laundry folding,

    S. Miller, J. van den Berg, M. Fritz, T. Darrell, K. Goldberg, and P. Abbeel, “A geometric approach to robotic laundry folding,” Int. J. Rob. Res., vol. 31, no. 2, p. 249–267, 2012

    2012

  15. [23]

    Action-conditional implicit visual dynamics for deformable object manipulation,

    B. Shen, Z. Jiang, C. Choy, S. Savarese, L. J. Guibas, A. Anandkumar, and Y . Zhu, “Action-conditional implicit visual dynamics for deformable object manipulation,” Int. J. Rob. Res. , 2023

    2023

  16. [24]

    Dynamic flex-and- flip manipulation of deformable linear objects,

    C. Jiang, A. Nazir, G. Abbasnejad, and J. Seo, “Dynamic flex-and- flip manipulation of deformable linear objects,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, Conference Proceedings

    2019

  17. [25]

    Model- free visually servoed deformation control of elastic objects by robot manipulators,

    D. Navarro-Alarcon, Y .-H. Liu, J. G. Romero, and P. Li, “Model- free visually servoed deformation control of elastic objects by robot manipulators,” IEEE Transactions on Robotics , vol. 29, no. 6, pp. 1457– 1468, 2013

    2013

  18. [26]

    Modeling, learning, perception, and control methods for deformable object manipulation,

    H. Yin, A. Varava, and D. Kragic, “Modeling, learning, perception, and control methods for deformable object manipulation,” Sci. Robot. , vol. 6, no. 54, p. eabd8803, 2021

    2021

  19. [27]

    Deep learning approaches to grasp synthesis: A review,

    R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic, D. Fox, and A. Cosgun, “Deep learning approaches to grasp synthesis: A review,” IEEE Trans. Robot., vol. 39, no. 5, p. 3994–4015, 2023

    2023

  20. [28]

    Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding,

    H. Ha and S. Song, “Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding,” in Proceedings of the 5th Conference on Robot Learning (CoRL 2021) . PMLR, 2022, Conference Proceedings

    2021

  21. [29]

    Fabricflownet: Bimanual cloth manipulation with a flow-based policy,

    T. Weng, S. M. Bajracharya, Y . Wang, K. Agrawal, and D. Held, “Fabricflownet: Bimanual cloth manipulation with a flow-based policy,” in Proceedings of the 5th Conference on Robot Learning (CoRL 2021) . PMLR, 2022, Conference Proceedings

    2021

  22. [30]

    SGTM 2.0: Autonomously untangling long cables using interactive perception,

    K. Shivakumar, V . Viswanath, A. Gu, Y . Avigal, J. Kerr, J. Ichnowski, R. Cheng, T. Kollar, and K. Goldberg, “SGTM 2.0: Autonomously untangling long cables using interactive perception,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, Con...

    2023

  23. [31]

    Learning rope manipulation policies using dense object descriptors trained on synthetic depth data,

    P. Sundaresan, J. Grannen, B. Thananjeyan, A. Balakrishna, M. Laskey, K. Stone, J. E. Gonzalez, and K. Goldberg, “Learning rope manipulation policies using dense object descriptors trained on synthetic depth data,” in 2020 IEEE International Conference on Robotics and Automati...

    2020

  24. [32]

    How to train your robot with deep reinforcement learning: lessons we have learned,

    J. Ibarz, J. Tan, C. Finn, M. Kalakrishnan, P. Pastor, and S. Levine, “How to train your robot with deep reinforcement learning: lessons we have learned,” The International Journal of Robotics Research , vol. 40, no. 4-5, pp. 698–721, 2021

    2021

  25. [33]

    Model-free reinforcement learning al- gorithms: A survey,

    S. C ¸alıs ¸ır and M. K. Pehlivano ˘glu, “Model-free reinforcement learning al- gorithms: A survey,” in 2019 27th signal processing and communications applications conference (SIU) . IEEE, 2019, pp. 1–4

    2019

  26. [34]

    Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control,

    H. Ito, K. Yamamoto, H. Mori, and T. Ogata, “Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control,” Sci. Robot. , vol. 7, no. 65, p. eaax8177, 2022

    2022

  27. [35]

    Scientific exploration of challenging planetary analog environments with a team of legged robots,

    P. Arm, G. Waibel, J. Preisig, T. Tuna, R. Zhou, V . Bickel, G. Ligeza, T. Miki, F. Kehl, H. Kolvenbach, and M. Hutter, “Scientific exploration of challenging planetary analog environments with a team of legged robots,” Sci. Robot. , vol. 8, no. 80, p. eaax8177, 2023

    2023

  28. [36]

    Swarm of micro flying robots in the wild,

    X. Zhou, X. Wen, Z. Wang, Y . Gao, H. Li, Q. Wang, T. Yang, H. Lu, Y . Cao, C. Xu, and F. Gao, “Swarm of micro flying robots in the wild,” Sci. Robot. , vol. 7, no. 66, p. eabm5954, 2022

    2022

  29. [37]

    Learning to manipulate deformable objects without demonstrations,

    Y . Wu, W. Yan, T. Kurutach, L. Pinto, and P. Abbeel, “Learning to manipulate deformable objects without demonstrations,” in Robotics: Science and Systems XVI . Robotics: Science and Systems Foundation, 2020, Conference Proceedings

    2020

  30. [38]

    Dynamic cloth manipulation with deep reinforcement learning,

    R. Jangir, G. Alenya, and C. Torras, “Dynamic cloth manipulation with deep reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, Conference Proceedings

    2020

  31. [39]

    Sim-to-real reinforcement learning for deformable object manipulation,

    J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Proceedings of the 2nd Conference on Robot Learning (CoRL 2018) . PMLR, 2018, Conference Proceedings

    2018

  32. [40]

    Benchmarking the sim-to-real gap in cloth manipulation,

    D. Blanco-Mulero, O. Barbany, G. Alcan, A. Colom ´e, C. Torras, and V . Kyrki, “Benchmarking the sim-to-real gap in cloth manipulation,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 2981–2988, 2024

    2024

  33. [41]

    Soft robotic hand with tactile palm-finger coordination,

    N. Zhang, J. Ren, Y . Dong, X. Yang, R. Bian, J. Li, G. Gu, and X. Zhu, “Soft robotic hand with tactile palm-finger coordination,” Nature Communications, vol. 16, no. 1, p. 2395, 2025

    2025

  34. [42]

    Soft robotic fingers with embedded ionogel sensors and discrete actuation modes for somatosensitive manipulation,

    R. L. Truby, R. K. Katzschmann, J. A. Lewis, and D. Rus, “Soft robotic fingers with embedded ionogel sensors and discrete actuation modes for somatosensitive manipulation,” in 2019 2nd IEEE international conference on soft robotics (RoboSoft) . IEEE, 2019, pp. 322–329

    2019

  35. [43]

    Rbo hand 3: A platform for soft dexterous manipulation,

    S. Puhlmann, J. Harris, and O. Brock, “Rbo hand 3: A platform for soft dexterous manipulation,” IEEE Transactions on Robotics , vol. 38, no. 6, pp. 3434–3449, 2022

    2022

  36. [44]

    A dexterous soft robotic hand for delicate in-hand manipulation,

    S. Abondance, C. B. Teeple, and R. J. Wood, “A dexterous soft robotic hand for delicate in-hand manipulation,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5502–5509, 2020

    2020

  37. [45]

    Tossingbot: Learning to throw arbitrary objects with residual physics,

    A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” IEEE Transactions on Robotics , vol. 36, no. 4, pp. 1307–1319, 2020

    2020

  38. [46]

    Learning better generative models for dexterous, single-view grasping of novel objects,

    M. S. Kopicki, D. Belter, and J. L. Wyatt, “Learning better generative models for dexterous, single-view grasping of novel objects,” The JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 20 International Journal of Robotics Research , vol. 38, no. 10-11, pp. 1246– 1267, 2019

    2021

  39. [47]

    Perturbation-based stiffness inference in variable impedance control,

    E. Caldarelli, A. Colom ´e, and C. Torras, “Perturbation-based stiffness inference in variable impedance control,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 8823–8830, 2022

    2022

  40. [48]

    Iterative path optimisation for personalised dressing assistance using vision and force information,

    Y . Gao, H. J. Chang, and Y . Demiris, “Iterative path optimisation for personalised dressing assistance using vision and force information,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2016, pp. 4398–4403

    2016

  41. [49]

    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 (Basel) , vol. 17, no. 12, p. 2762, 2017

    2017

  42. [50]

    DIGIT: A novel design for a low-cost compact high- resolution tactile sensor with application to in-hand manipulation,

    M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V . R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, D. Jayaraman, and R. Calandra, “DIGIT: A novel design for a low-cost compact high- resolution tactile sensor with application to in-hand manipulation,” IEEE Robot. ...

    2020

  43. [51]

    Cable manipulation with a tactile-reactive gripper,

    Y . She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” Int. J. Rob. Res. , vol. 40, no. 12–14, p. 1385–1401, 2021

    2021

  44. [52]

    Active clothing material perception using tactile sensing and deep learning,

    W. Yuan, Y . Mo, S. Wang, and E. H. Adelson, “Active clothing material perception using tactile sensing and deep learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, Conference Proceedings

    2018

  45. [53]

    Pneumatic networks for soft robotics that actuate rapidly,

    B. Mosadegh, P. Polygerinos, C. Keplinger, S. Wennstedt, R. F. Shepherd, U. Gupta, J. Shim, K. Bertoldi, C. J. Walsh, and G. M. Whitesides, “Pneumatic networks for soft robotics that actuate rapidly,” Advanced functional materials , vol. 24, no. 15, pp. 2163–2170, 2014

    2014

  46. [54]

    Multidimensional tactile sensor with a thin compound eye-inspired imaging system,

    Y . Zhang, X. Chen, M. Y . Wang, and H. Yu, “Multidimensional tactile sensor with a thin compound eye-inspired imaging system,” Soft Robot. , vol. 9, no. 5, p. 861–870, 2022

    2022

  47. [55]

    Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

    T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proceedings of the 35th International Conference on Machine Learning (ICML 2018) . PMLR, 2018, Conference Proceedings

    2018

  48. [56]

    Segment anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, and W.-Y . Lo, “Segment anything,” arXiv preprint, p. arXiv:2304.02643, 2023

    2023 arXiv

  49. [57]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022, Conference Proceedings

    2022

  50. [58]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems . Curran Associates Inc., 2017, Conference P...

    2017

  51. [59]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations (ICLR) , 2015, Conference Proceedings

    2015

  52. [60]

    Llama: Open and efficient foundation language models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi `ere, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “Llama: Open and efficient foundation language models,” p. arXiv:2302.13971, 2023

    2023 arXiv

  53. [61]

    Gpt-4 technical report,

    OpenAI, “Gpt-4 technical report,” p. arXiv:2303.08774, 2023

    2023 arXiv

  54. [62]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conf. Comput. Visi. Pattern Recognit. (CVPR) . IEEE, 2016, Conference Proceedings

    2016

  55. [63]

    Rectified linear units improve restricted boltzmann machines,

    V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML’10: Proceedings of the 27th Interna- tional Conference on International Conference on Machine Learning . Omnipress, 2010, Conference Proceedings

    2010

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.