Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking

Masayoshi Tomizuka; Mingrui Yu; Xiang Li; Xinghao Zhu; Yongpeng Jiang

REVIEW 4 major objections 4 minor 70 references

Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A purely model-based control hierarchy lets a robotic hand reorient, slide, and open objects through contact-rich in-hand manipulation with no learning or task-specific training.

desk verdict A genuinely integrated planning-tracking system, but the coupled force-motion model has a sign error that invalidates the claimed coupling correction. read the letter →

arxiv 2505.04978 v1 pith:JOUJ73C4 submitted 2025-05-08 cs.RO

Yongpeng Jiang , Mingrui Yu , Xinghao Zhu , Masayoshi Tomizuka , Xiang Li This is my paper · ORCID

classification cs.RO

keywords in-handmanipulationmultifingeredhandsdexterouscontact-implicitmodelpredictivecontroltactilefeedbackhybridforce-motionquasi-dynamiccontactintegratedplanningand

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 sets out to prove that contact-rich in-hand manipulation — where fingers deliberately make and break contact to turn, slide, or open an object — can be performed robustly by a purely model-based control hierarchy, without reinforcement learning or task-specific training. The system couples a high-level contact-implicit model predictive controller that jointly plans finger motions, contact locations, and contact forces through a smoothed, quasi-dynamic contact model, with a low-level MPC that tracks both motion and force references using live tactile feedback through a hand-only force-motion model. The authors' key argument is that the low level exists to absorb precisely the modeling errors that defeat planning-only approaches, in particular the force-at-a-distance artifact of smoothed contacts, which leaves open-loop execution with insufficient contact force. If the claim holds, model-based control becomes a practical alternative to trained policies for new in-hand tasks: the user changes a few hyperparameters, and the same machinery deploys on different hands and objects. The paper reports completion of five real-world tasks under deliberate human disturbances and simulation benchmarks against open-loop trajectory execution and sampling-based MPC.

What carries the argument

The argument rides on two models. The high-level planner uses the smoothed quasi-dynamic contact (CQDC) dynamics: contact forces emerge as dual variables of a convex optimization with a barrier smoothing term, and the solver is control-limited differential dynamic programming, warm-started from the previous solution and differentiated through numerically computed contact Jacobians; the smoothing parameter $\kappa$ is the knob that trades nonphysical force-at-a-distance artifacts against the exploration that lets fingers break and re-establish contacts. The low-level controller rests on a force-motion model of the hand alone, whose central identity is the coupled stiffness $K_{\mathrm{coup}} = \bar{K} + \bar{K} G_o^\top (G_o \bar{K} G_o^\top)^{-1} G_o \bar{K}$, derived by requiring the resultant wrench on the object to stay constant under quasi-static motion; it maps desired fingertip displacements into contact forces across all contacts simultaneously and defines the MPC dynamics $\dot{s} = g(s,u)$ on the state $[q; q_d; \Lambda_{\mathrm{ext}}]$. The weighting matrices derived from planned contact normals — force control along the normal, motion control in the tangent plane — decide which subspace each active contact tracks and turn the same MPC into hybrid force-motion control or plain joint-space control by a switch.

What would settle it

A decisive check is a high-acceleration disturbance test: grasp an instrumented object with the same three-finger setup, apply a sharp impulse that makes the object accelerate measurably, and compare the low-level MPC's contact-force predictions from the coupled stiffness $K_{\mathrm{coup}}$ against the tactile readings; systematic divergence under acceleration would show that the coupling model, and the robustness attributed to it, does not hold where inertia matters. The quiet version is a simulation sweep with heavier or faster objects, doubling the object's mass or increasing commanded rotation speed until force-tracking error exceeds the paper's reported levels.

Watch

Extended reading notes

Core claim

The central claim is that robustness in contact-rich manipulation decomposes into two jobs that can be assigned to two control layers running at different rates. The high-level layer replans at about 10 Hz through a smoothed contact-implicit dynamics model, generating coarse finger motions and, along with them, reference contact forces and normal directions; this is what recovers from external disturbances. The low-level layer runs at 30 Hz and jointly tracks those references with an MPC whose dynamics describe only the hand: joint positions, commanded positions, and contact forces evolve under a coupled force-motion model. The coupling is the paper's main modeling contribution: the object's free motion links every finger's contact force to every other finger's motion, summarized by the coupled stiffness identity $K_{\mathrm{coup}} = \bar{K} + \bar{K}G_o^\top(G_o\bar{K}G_o^\top)^{-1}G_o\bar{K}$, so the controller knows that pushing with one finger changes forces elsewhere. With force tracked along the planned contact normal and motion tracked in the tangent plane, the paper reports that real hardware completes door opening, card rotation, board sliding, box opening, and grasping — several under human-imposed disturbances — and that in simulation the method beats open-loop execution and sampling-based MPC on success rate, precision, and smoothness.

Load-bearing premise

The low-level force-motion model assumes the object is quasi-static — negligible inertia, rigid point contacts that move with the object, and a constant resultant wrench during the derivation of the coupled stiffness — so its predictions degrade whenever the object accelerates, the contacts deform, or the contact points do not move rigidly with the object, leaving only the high-level replanner as backstop.

Editorial extensions

If this is right

New tasks become configuration changes: the user edits the desired object motion, grasp pose, and coarse models, and the same unmodified pipeline applies — shown across two different hand designs and seven simulated and five physical task types without retraining.
Human-imposed disturbances are absorbed by the 10 Hz planner rather than by fast feedback alone; the card, board, and door trials show errors shrinking back to target after each intervention.
Adding explicit contact-force tracking to motion tracking is what separates the method from planning-only baselines: in the Rotate Sphere benchmark, success rises from 14/100 (open-loop) to 100/100, with far lower joint acceleration.
The smoothing coefficient $\kappa$ is a genuine design lever: too little smoothing halts finger gaiting, too much introduces nonphysical forces — and the low-level force tracking exists specifically to absorb that artifact.
The 20–50% success rate on the real open-box trials marks the method's practical ceiling: when the true contact geometry deviates strongly from the modeled geometry, model fidelity, not control frequency, limits performance.

Reading between the lines

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

The same division of labor could transfer to other contact-rich domains, such as legged locomotion: a high-level contact-implicit planner sets foothold forces while a low-level force-tracking layer absorbs terrain-model error, replacing the need for either fast dynamics inversion or heavy domain randomization.
Because the coupled stiffness depends only on grasp geometry and estimated stiffness, it could be estimated or adapted online from tactile and state measurements, turning the constant-wrench quasi-static premise into a continuously checked condition rather than a fixed assumption.
A natural hybrid extension is to keep the 10 Hz contact-implicit planner and replace the analytic low-level stiffness model with a learned residual that predicts force-tracking error; the paper's own reported benchmarks, including the open-box failure rates, would be the baseline for judging whether the residual adds anything.
The authors' stated limit on fast, dynamic tasks points to the next step for the same architecture: closed-form or learned contact dynamics that run faster than the convex contact solver would extend the framework from quasi-static to genuinely dynamic in-hand manipulation.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the paper is a solid systems contribution with five real-world demos, but the stress-test sign error is real. Eq. (22) and (23) in Sec. 6.3 have the wrong sign for the coupling term. From δwext = 0 and δPc = Go^T δxu, the implicit function theorem gives ∂xu/∂Pd = +(Go \bar K Go^T)^{-1} Go \bar K, not the negative. So Kcoup should be a Schur complement with a minus, not a plus. This matters because Kcoup enters the low-level dynamics (24) as the claimed multi-contact coupling correction. As written, the model predicts positive force coupling when it should be zero for rigid free-object motion. So the central low-level contribution is mathematically wrong as stated.

That said, the paper has real value. The high-level contact-implicit MPC with numerical differentiation for contact Hessians is a practical improvement over Pang et al., and the warm-start/shifting tricks are sensible. The automatic weighting-matrix design from planned contact normals is a nice touch. The simulation comparisons are well specified, and the real-world experiments on four LEAP Hand tasks (plus two Allegro sim tasks) are honest and detailed — they even report the open-box success rate at 20-50%, which is candid.

Soft spots beyond the sign error: the openloop baseline in Sec. 7.3.2 is explicitly missing the trajectory optimization step from Pang et al., so it is a weak comparator; the claim that the method 'outperforms existing model-based methods' rests partly on that weakened baseline. The real-world open-box task has a low success rate, and the text plots only successful trials, though it does disclose the range. The low-level model's quasi-static constant-wrench assumption is a further idealization, and the sign error compounds the concern.

Bottom line: this deserves a serious referee. The sign error should be caught and fixed (or convincingly argued as a typo that does not affect implementation), and the baseline comparisons need tightening. If the sign is fixed, the paper is a solid model-based in-hand manipulation advance. I'd send it out with a request for a focused check of Sec. 6.3.

Referee Report

4 major / 4 minor

Summary. The paper proposes a hierarchical, model-based framework for in-hand manipulation in which fingers make and break contact. At the high level, a contact-implicit MPC planner based on smoothed quasi-dynamic contact dynamics generates finger motion and contact-force references at 10 Hz; at the low level, an MPC-based hybrid force-motion controller with tactile feedback tracks these references at 30 Hz. The low-level model includes a claimed multi-contact coupling correction. The paper reports simulation comparisons on a rotate-sphere task, robustness studies under noise and model error, and five real-world manipulation tasks, plus a grasping experiment. The central claims are that the integrated motion-contact planning and tracking improves accuracy and robustness relative to existing model-based methods and that the system works without task-specific learning.

Significance. If the claims hold, this is a valuable demonstration that contact-rich in-hand manipulation with regrasping can be performed without task-specific training, and the two-level architecture is a sensible way to combine real-time contact-implicit planning with tactile-feedback tracking. The paper's strengths include the breadth of evaluation, the 100-trial simulation statistics, the use of two different hands, and the availability of code and videos. The main theoretical contribution is the coupled force-motion model in Sec. 6.3, so the correctness of that derivation is decisive for the paper's contribution.

major comments (4)

[Sec. 6.3, Eqs. (22)-(23)] There is a sign error in the derivation of the coupled stiffness. Starting from δwext = GoKbar(δPd − Go^T δxu) = 0, the implicit function theorem gives ∂xu/∂Pd = (GoKbarGo^T)^{-1}GoKbar, with a positive sign, because ∂(δwext)/∂xu = −GoKbarGo^T. Equation (22) states the negative of this result. Consequently Eq. (23) should be a Schur complement, Kcoup = Kbar − KbarGo^T(GoKbarGo^T)^{-1}GoKbar, not Kbar + KbarGo^T(...)GoKbar. The sign is physically meaningful: in a 1D example with two equal-stiffness contacts (Go = [1,1], Kbar = diag(k,k)), displacing both desired contact points by the same δ should move the object rigidly and leave contact forces unchanged, but the printed plus-sign Kcoup predicts δΛext = [2kδ;2kδ]. Since Kcoup enters the low-level dynamics in Eq. (24), the claimed coupling correction is wrong as stated and the low-level model must be re-derived and re-validated.
[Sec. 7.3.2 and Table 4] The 'openloop' baseline is not a faithful implementation of Pang et al. (2023): the footnote in Sec. 7.3.2 states that the additional trajectory-optimization stage with a smaller time step is omitted. As implemented, this baseline is an ablation of the proposed framework without low-level tracking, not an external model-based method. The claim that the approach 'outperforms existing model-based methods' should therefore be either supported by a full reproduction of Pang et al. or explicitly reworded, and Table 4 should distinguish the ablation from external baselines.
[Sec. 8.2.5 and Fig. 20] The open-box task has a success rate acknowledged to fluctuate between 20% and 50% for different boxes, and only two successful trials per box are plotted. The abstract and conclusion claim that the method 'successfully completes five challenging tasks' and is 'robust' overstates the evidence as reported. Please report all trials, the failure modes, and a full success-rate table, and qualify the robustness claim accordingly.
[Sec. 6.3 and Sec. 3.1] The coupled force-motion model is derived under the assumptions wext = Const and quasi-dynamic first-order dynamics, yet the target tasks involve fingers making and breaking contacts and external disturbances. These conditions can produce object acceleration and time-varying external wrenches, so the validity of Eq. (24) for the tested scenarios is not established. Please provide a concrete check, e.g., comparing force predictions of the Kcoup model against a full rigid-body simulation on a representative task, to show the assumption is adequate for control.

minor comments (4)

[Sec. 5.2.2, Eq. (10)] The numerical-differentiation step size ∆ is never specified; please give its value and comment on its effect on gradient accuracy.
[Sec. 7.2.1] The reference 'loco 3d (2025)' is a GitHub issue; please cite a stable archival source or remove the citation.
[Fig. 9] The axis labels in Fig. 9 are only 'iter', 'yaw', and 'cost'; please add units and a fuller caption so the heatmaps can be interpreted.
[Sec. 7.3.2] The sentence explaining that the openloop baseline 'exhibits the lowest standard deviation in task error' due to 'reduced accidental contacts' reads as contradictory and should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivations are self-contained, and the central claims are supported by external simulation and hardware benchmarks.

full rationale

The paper's central claims are validated against MuJoCo simulation and real-world hardware experiments, not against the same fitted quantities used to construct the models. The high-level planner uses the CQDC model of Pang et al. (2023), an external and independently published formulation, and its contribution is evaluated by comparing with open-loop, CEM, and iLQG baselines in simulation and by real-world tasks. The low-level force-motion model is derived from physical principles (PD control, environment stiffness, grasp matrix, and contact Jacobians) rather than fitted to the success metric; the coupled stiffness Kcoup is obtained from an explicit algebraic derivation in Eqs. (20)-(23), so it does not reduce by construction to the quantities it is said to predict. The paper does build on the authors' prior work (Jiang et al. 2024), but that self-citation is not load-bearing: the current system is re-implemented with external code (Crocoddyl, CQDC, MuJoCo) and tested against external physical and simulated environments. Section 7.2 uses the same dynamics for planning and simulation, but that is explicitly labeled as a planning-module self-consistency check, not as the paper's external evidence. The stated limitations (modeling inaccuracy, quasi-dynamic assumptions, local optimality, low open-box success rate) further indicate that the authors do not claim their models are validated by construction. I find no step where an output is equivalent by definition to an input, no fitted parameter is renamed as a prediction, and no load-bearing argument reduces to a self-citation chain. The algebraic sign concern about Eq. (22)-(23) raised in the skeptical assessment is a correctness issue, not circularity, and does not affect this score.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claim rests on approximately six hand-chosen parameters, the quasi-dynamic rigid-body contact assumptions, the inherited CQDC model, and the constant-wrench coupling assumption. The honest contribution is therefore the integration, not a parameter-free derivation.

free parameters (5)

Smoothing factor kappa = 100
Chosen through the Sec. 7.2.3 study on the Open Door task; kappa = 10 gives excessive smoothing, kappa >= 500 gives too little. The choice directly affects the force-at-a-distance magnitude and planning behavior.
MPC horizon N = 10
Selected from the Sec. 7.2.2 Rotate Valve study as a trade-off between rotation speed and solve time.
Hyperparameters beta1, beta2 = beta1 = 0.5, beta2 = 0.75
Hand-tuned in Sec. 5.4 to balance warm-start speed and trajectory smoothness.
Low-level weights KP, KD, ke, kq, ku, wori, lambda = KP = 4I, KD = I, ke = 200, kq = 20, ku = 1, wori = 0.0015, lambda = 0.5 N
Reported in Sec. 8.1 as fixed parameters; kLambda and kp vary per task (e.g., kLambda = 1, kp = 2000 for the open door task). These are hand-chosen and central to the low-level tracking behavior.
Force threshold L and slope k in Eq. (28) = L = 2, k = 0.04
Soft threshold on reference force magnitude, introduced in Sec. 8.1 without an ablation or systematic justification.

assumptions (5)

domain assumption Quasi-dynamic assumption: negligible inertia and first-order dynamics (Sec. 3.1)
The high-level CQDC model and the low-level derivation of Kcoup both rely on this. It limits validity to slow or damped motions, which the paper acknowledges.
standard math CQDC model from Pang et al. (2023) is a valid approximation of the physical contact dynamics (Eq. (1), (8))
The high-level planner inherits this model and its smoothed force-at-a-distance behavior; the paper does not re-derive or validate the model itself beyond citing prior work.
domain assumption The object is a single rigid body with known geometry and approximately estimated inertia/contact parameters (Sec. 3.1)
Needed for collision detection, the grasp matrix, and the quasi-dynamic dynamics. It excludes deformable or multi-body objects, which the paper notes is out of scope.
domain assumption Force-motion model: environment stiffness Ke, joint PD control with stiffness KP and damping KD, inertia neglected (Sec. 6.1)
The low-level force-tracking law (Eqs. (15), (24)) depends on this model. Inertia and Coriolis effects are neglected at the scale of contact forces, a simplification for a moving hand-object system.
ad hoc to paper wext = Const and contact points move rigidly with the object in deriving Kcoup (Eqs. (20) to (23))
This assumption produces the coupling correction term. It is stated in Sec. 6.3 without an independent validation experiment, and it is load-bearing for the low-level dynamics.

invented entities (1)

None
purpose: No new physical entities are introduced
The paper introduces no new particles, forces, or conserved quantities. Its contributions are algorithmic and modeling.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking." pith.science (2026). https://pith.science/paper/JOUJ73C4

@misc{pith2026250504978,
  author       = {Pith},
  title        = {Pith review of: Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JOUJ73C4}},
  note         = {Machine review of arXiv:2505.04978}
}

read the original abstract

Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications. Unlike learning-based approaches that rely on large-scale training or extensive data collection for each specific task, model-based methods offer an efficient alternative. Their online computing nature allows for ready application to new tasks without extensive retraining. However, due to the complexity of physical contacts, existing model-based methods encounter challenges in efficient online planning and handling modeling errors, which limit their practical applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel integrated framework that mitigates these limitations. The integration involves two key aspects: 1) integrated real-time planning and tracking achieved by a hierarchical structure; and 2) joint optimization of motions and contacts achieved by integrated motion-contact modeling. Specifically, at the high level, finger motion and contact force references are jointly generated using contact-implicit model predictive control. The high-level module facilitates real-time planning and disturbance recovery. At the low level, these integrated references are concurrently tracked using a hand force-motion model and actual tactile feedback. The low-level module compensates for modeling errors and enhances the robustness of manipulation. Extensive experiments demonstrate that our approach outperforms existing model-based methods in terms of accuracy, robustness, and real-time performance. Our method successfully completes five challenging tasks in real-world environments, even under appreciable external disturbances.

Figures

Figures reproduced from arXiv: 2505.04978 by the authors.

**Figure 1.** Proposed framework for in-hand manipulation. The framework features integrated, real-time motion-contact planning and tracking. The system simultaneously plans and tracks finger motions and contact forces, enabling robust and precise manipulation in real-world scenarios. It allows the fingers to efficiently alternate between making and breaking contacts. helps mitigate sim-to-real transfer challenges by allowing dem… view at source ↗

**Figure 2.** [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Detailed view of the proposed framework. The top figures show the high-level integrated motion-contact planning module, which generates real-time finger motions and contact information (visualizing only the index finger and forces). The top-right figure illustrates how modeling errors lead to the force-at-a-distance effect, where non-zero planned forces appear even when contact is inactive. Modeling errors can be mi… view at source ↗

Figures from the paper (17 more)

**Figure 5.** Figure 5: Notations of the force-motion model. The finger is modeled with joint proportional-derivative (PD) control with stiffness KP and KD. Due to controller compliance, the desired fingertip location differs from the actual location. At the contact point, the environment sti…

**Figure 5.** Figure 5: In other words, the force-motion model is no longer [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Snapshots of the tasks used for evaluation in the simulation and on the hardware. The tasks of Rotate Valve and Rotate Sphere involve the use of the Allegro Hand. The remaining tasks are performed using the LEAP Hand, which features simplified visual geometries. The pu…

**Figure 7.** Figure 7: Elapsed time of high-level motion-contact planning during key sub-steps. Here, calc and calc_diff represent the forward dynamics and gradient computation of the CQDC model, respectively. The total height of the bar indicates the average iteration time for each task. 7.…

**Figure 8.** Figure 8: Rotation speed and solution time per iteration under different MPC horizons in the Rotate Valve task. The error bars indicate standard deviations. The dashed lines represent average values. Prepared using sagej.cls [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

**Figure 9.** Figure 9: Effects of κ on optimization performance, including the cost of DDP, the sphere’s rotation (yaw angle), and the action norm. Warmer colors indicate a weaker smoothing effect and less nonphysical behavior. DDP step sizes. This highlights the importance of the smoothed f…

**Figure 10.** Figure 10: Task error distribution for the Rotate Sphere task with noisy object orientation. The x-axis labels indicate the standard deviation of the added Gaussian noise. The bars represent mean and extreme values, whereas the gray stripes show the intervals between the first a…

**Figure 11.** Figure 11: Distribution of the task error and task error S.D. across 100 trials in the Rotate Sphere task, accounting for modeling errors. We use a consistent radius of r = 6.0cm in the planning model across different groups. The x-axis labels indicate the actual radius used in …

**Figure 13.** Figure 13: Results of the in-grasp object movement experiment. Top: Task snapshot and waypoints. The marker at the top center of the cylinder successively moves to the eight corners of the cube and back to the initial position. Middle: Normal contact force. Dashed lines represen…

**Figure 14.** Figure 14: Experimental setup: (a) The LEAP Hand is mounted on the flange of a UR5 arm. An AprilTag and a RealSense D405 camera are used for perception. (b) The fingertips of the LEAP Hand are replaced with four Tac3D tactile sensors. Due to a sensor malfunction, we have replace…

**Figure 15.** Figure 15: Experimental results for the open door task with human disturbance. (a) Snapshots from two views. (b) Door handle rotation in different trials. (c) Planned and real contacts (Trial 4) represented as time intervals during which the contact forces exceed a given thresho…

**Figure 16.** Figure 16: Additional results of the open door task with different relative wrist-door poses. (a) The door is rotated 30 degrees counterclockwise (case 1). The ring finger is not used. (b) The door is rotated 30 degrees clockwise (case 2). The index finger is not used. (c) Door …

**Figure 17.** Figure 17: Real-world open door task: Comparison with the openloop baseline. The curves represent the mean handle rotation, while the shaded areas indicate the standard deviation. The dashed horizontal line marks the target rotation. Prepared using sagej.cls [PITH_FULL_IMAGE:fi…

**Figure 18.** Figure 18: Experimental results for the rotate card task with disturbance. (a) Snapshots of the task. The white border represents the target pose. Rotational and translational disturbances are exerted by the human operator. (b)(c) Object rotation (around the x-axis) and translat…

**Figure 19.** Figure 19: Experimental results for the slide board task with disturbance. (a) Snapshots of the task. The white border represents the target pose. External disturbances are exerted by the human operator. (b)(c) Object rotation and translation refer to the coordinate system shown…

**Figure 20.** Figure 20: Experimental results for the open box task. At the top are snapshots of the simulation and the real-world experiments on four boxes A∼D. The first row shows the initial state, and the second row shows the state when the box is opened. The yellow and cyan boxes represe…

**Figure 21.** Figure 21: Relative force tracking error and snapshots of the grasping experiment. The objects are (a) banana, (b) mango, (c) onion, (d) pear, and (e) pepper. The scatter points in the box plot represent outliers, indicating moments when the relative force tracking error exceeds…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

70 extracted references · 22 canonical work pages

[2]

IEEE Transactions on Robotics 40: 3879--3896

Aydinoglu A, Wei A, Huang WC and Posa M (2024) Consensus complementarity control for multicontact mpc. IEEE Transactions on Robotics 40: 3879--3896. doi:10.1109/TRO.2024.3435423

arXiv 2024
[3]

Science 364(6446): eaat8414

Billard A and Kragic D (2019) Trends and challenges in robot manipulation. Science 364(6446): eaat8414

work page 2019
[4]

In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Chen C, Culbertson P, Lepert M, Schwager M and Bohg J (2021) Trajectotree: Trajectory optimization meets tree search for planning multi-contact dexterous manipulation. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 8262--8268. doi:10.1109/IROS51168.2021.9636346

arXiv 2021
[5]

arXiv preprint arXiv:2412.16490

Chen J, Ke Y and Wang H (2024) Bodex: Scalable and efficient robotic dexterous grasp synthesis using bilevel optimization. arXiv preprint arXiv:2412.16490

arXiv 2024
[6]

Science Robotics 8(84): eadc9244

Chen T, Tippur M, Wu S, Kumar V, Adelson E and Agrawal P (2023 a ) Visual dexterity: In-hand reorientation of novel and complex object shapes. Science Robotics 8(84): eadc9244

work page 2023
[7]

IEEE Transactions on Pattern Analysis and Machine Intelligence 46(5): 2804--2818

Chen Y, Geng Y, Zhong F, Ji J, Jiang J, Lu Z, Dong H and Yang Y (2023 b ) Bi-dexhands: Towards human-level bimanual dexterous manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 46(5): 2804--2818

work page 2023
[8]

In: Proceedings of The 8th Conference on Robot Learning

Chen Y, Wang C, Yang Y and Liu K (2025) Object-centric dexterous manipulation from human motion data. In: Proceedings of The 8th Conference on Robot Learning. PMLR, pp. 3828--3846

work page 2025
[9]

In: 2015 IEEE International Conference on Robotics and Automation (ICRA)

Chen Z, Wimböck T, Roa MA, Pleintinger B, Neves M, Ott C, Borst C and Lii NY (2015) An adaptive compliant multi-finger approach-to-grasp strategy for objects with position uncertainties. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). pp. 4911--4918. doi:10.1109/ICRA.2015.7139881

arXiv 2015

Show all 70 references

[11]

IEEE Robotics and Automation Letters 9(1): 390--397

Cheng X, Patil S, Temel Z, Kroemer O and Mason MT (2024) Enhancing dexterity in robotic manipulation via hierarchical contact exploration. IEEE Robotics and Automation Letters 9(1): 390--397. doi:10.1109/LRA.2023.3333699

2024
[12]

In: Proceedings of Robotics: Science and Systems

Chi C, Feng S, Du Y, Xu Z, Cousineau E, Burchfiel BC and Song S (2023) Diffusion Policy: Visuomotor Policy Learning via Action Diffusion . In: Proceedings of Robotics: Science and Systems. doi:10.15607/RSS.2023.XIX.026

2023 doi
[13]

In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Cruciani S, Smith C, Kragic D and Hang K (2018) Dexterous manipulation graphs. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 2040--2047. doi:10.1109/IROS.2018.8594303

2018
[14]

IEEE Robotics and Automation Letters 5(2): 588--595

Cruciani S, Sundaralingam B, Hang K, Kumar V, Hermans T and Kragic D (2020) Benchmarking in-hand manipulation. IEEE Robotics and Automation Letters 5(2): 588--595. doi:10.1109/LRA.2020.2964160

2020
[15]

In: Proceedings of Robotics: Science and Systems

Dafle NC, Holladay R and Rodriguez A (2018) In-hand manipulation via motion cones. In: Proceedings of Robotics: Science and Systems. doi:10.15607/RSS.2018.XIV.058

2018 doi
[16]

The International Journal of Robotics Research 36(13-14): 1437--1454

Fazeli N, Kolbert R, Tedrake R and Rodriguez A (2017) Parameter and contact force estimation of planar rigid-bodies undergoing frictional contact. The International Journal of Robotics Research 36(13-14): 1437--1454

2017
[17]

In: 2021 60th IEEE Conference on Decision and Control (CDC)

Gold T, Rohrmuller M, Volz A and Graichen K (2021) Model predictive interaction control for force closure grasping. In: 2021 60th IEEE Conference on Decision and Control (CDC). pp. 1018--1023. doi:10.1109/CDC45484.2021.9683405

2021
[18]

IEEE Transactions on Robotics 39(1): 76--89

Gold T, Völz A and Graichen K (2023) Model predictive interaction control for robotic manipulation tasks. IEEE Transactions on Robotics 39(1): 76--89. doi:10.1109/TRO.2022.3196607

2023
[19]

arXiv preprint arXiv:2501.02116

Gu Z, Li J, Shen W, Yu W, Xie Z, McCrory S, Cheng X, Shamsah A, Griffin R, Liu CK, Kheddar A, Peng XB, Zhu Y, Shi G, Nguyen Q, Cheng G, Gao H and Zhao Y (2025) Humanoid locomotion and manipulation: Current progress and challenges in control, planning, and learning. arXiv prepr...

2025 arXiv
[20]

arXiv preprint arXiv:2303.12076

Guzey I, Evans B, Chintala S and Pinto L (2023) Dexterity from touch: Self-supervised pre-training of tactile representations with robotic play. arXiv preprint arXiv:2303.12076

2023 arXiv
[21]

In: 2023 IEEE International Conference on Robotics and Automation (ICRA)

Handa A, Allshire A, Makoviychuk V, Petrenko A, Singh R, Liu J, Makoviichuk D, Van Wyk K, Zhurkevich A, Sundaralingam B and Narang Y (2023) Dextreme: Transfer of agile in-hand manipulation from simulation to reality. In: 2023 IEEE International Conference on Robotics and Autom...

2023
[23]

arXiv preprint arXiv:2411.06183

Hess A, K \"u bler AM, Forrai B, Dogar M and Katzschmann RK (2024) Sampling-based model predictive control for dexterous manipulation on a biomimetic tendon-driven hand. arXiv preprint arXiv:2411.06183

2024
[24]

In: 2019 International Conference on Robotics and Automation (ICRA)

Hou Y and Mason MT (2019) Robust execution of contact-rich motion plans by hybrid force-velocity control. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 1933--1939. doi:10.1109/ICRA.2019.8794366

2019
[25]

arXiv preprint arXiv:2212.00541

Howell T, Gileadi N, Tunyasuvunakool S, Zakka K, Erez T and Tassa Y (2022) Predictive sampling: Real-time behaviour synthesis with mujoco. arXiv preprint arXiv:2212.00541

2022 arXiv
[27]

arXiv preprint arXiv:2408.07855

Jin W (2024) Complementarity-free multi-contact modeling and optimization for dexterous manipulation. arXiv preprint arXiv:2408.07855

2024 arXiv
[28]

IEEE Transactions on Robotics 39(5): 3350--3367

Khadivar F and Billard A (2023) Adaptive fingers coordination for robust grasp and in-hand manipulation under disturbances and unknown dynamics. IEEE Transactions on Robotics 39(5): 3350--3367. doi:10.1109/TRO.2023.3280028

2023
[29]

In: Proceedings of Robotics: Science and Systems

Khandate G, Shang S, Chang ET, Saidi TL, Adams J and Ciocarlie M (2023) Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation . In: Proceedings of Robotics: Science and Systems. doi:10.15607/RSS.2023.XIX.020

2023 doi
[30]

The International Journal of Robotics Research 44(3): 486--510

Kim G, Kang D, Kim JH, Hong S and Park HW (2025) Contact-implicit model predictive control: Controlling diverse quadruped motions without pre-planned contact modes or trajectories. The International Journal of Robotics Research 44(3): 486--510. doi:10.1177/02783649241273645. :...

2025 doi
[31]

O nol A \

Kurtz V, Castro A, \"O nol A \"O and Lin H (2023) Inverse dynamics trajectory optimization for contact-implicit model predictive control. arXiv preprint arXiv:2309.01813

2023 arXiv
[33]

arXiv preprint arXiv:2411.02479

Lambeta M, Wu T, Sengul A, Most VR, Black N, Sawyer K, Mercado R, Qi H, Sohn A, Taylor B, Tydingco N, Kammerer G, Stroud D, Khatha J, Jenkins K, Most K, Stein N, Chavira R, Craven-Bartle T, Sanchez E, Ding Y, Malik J and Calandra R (2024) Digitizing touch with an artificial mu...

2024 arXiv
[34]

IEEE Transactions on Robotics 40: 1617--1629

Le Cleac'h S, Howell TA, Yang S, Lee CY, Zhang J, Bishop A, Schwager M and Manchester Z (2024) Fast contact-implicit model predictive control. IEEE Transactions on Robotics 40: 1617--1629. doi:10.1109/TRO.2024.3351554

2024
[35]

IEEE Transactions on Robotics 40: 3716--3733

Le Lidec Q, Jallet W, Montaut L, Laptev I, Schmid C and Carpentier J (2024) Contact models in robotics: A comparative analysis. IEEE Transactions on Robotics 40: 3716--3733. doi:10.1109/TRO.2024.3434208

2024
[36]

arXiv preprint arXiv:2409.14562

Li AH, Culbertson P, Kurtz V and Ames AD (2024) Drop: Dexterous reorientation via online planning. arXiv preprint arXiv:2409.14562

2024 arXiv
[37]

In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Liu H, Huang B, Li Q, Zheng Y, Ling Y, Lee W, Liu Y, Tsai YY and Yang C (2022) Multi-fingered tactile servoing for grasping adjustment under partial observation. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 7781--7788. doi:10.1109/IR...

2022
[38]

https://github.com/loco-3d/crocoddyl/issues/1042

loco 3d (2025) Issue \#1042 - crocoddyl. https://github.com/loco-3d/crocoddyl/issues/1042. Accessed: 2025-04-11

2025
[39]

arXiv preprint arXiv:2410.21845

Luo J, Xu C, Wu J and Levine S (2024) Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning. arXiv preprint arXiv:2410.21845

2024 arXiv
[40]

In: 2011 15th International Conference on Advanced Robotics (ICAR)

Ma RR and Dollar AM (2011) On dexterity and dexterous manipulation. In: 2011 15th International Conference on Advanced Robotics (ICAR). pp. 1--7. doi:10.1109/ICAR.2011.6088576

2011
[41]

arXiv preprint arXiv:2310.12931

Ma YJ, Liang W, Wang G, Huang DA, Bastani O, Jayaraman D, Zhu Y, Fan L and Anandkumar A (2023) Eureka: Human-level reward design via coding large language models. arXiv preprint arXiv:2310.12931

2023 arXiv
[42]

Science Robotics 7(66): eabm6074

Macenski S, Foote T, Gerkey B, Lalancette C and Woodall W (2022) Robot operating system 2: Design, architecture, and uses in the wild. Science Robotics 7(66): eabm6074

2022
[43]

arXiv preprint arXiv:2108.10470

Makoviychuk V, Wawrzyniak L, Guo Y, Lu M, Storey K, Macklin M, Hoeller D, Rudin N, Allshire A, Handa A and State G (2021) Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470

2021 arXiv
[44]

MIT press

Mason MT (2001) Mechanics of robotic manipulation. MIT press

2001
[45]

In: 2020 IEEE International Conference on Robotics and Automation (ICRA)

Mastalli C, Budhiraja R, Merkt W, Saurel G, Hammoud B, Naveau M, Carpentier J, Righetti L, Vijayakumar S and Mansard N (2020) Crocoddyl: An efficient and versatile framework for multi-contact optimal control. In: 2020 IEEE International Conference on Robotics and Automation (I...

2020
[46]

O nol A \

\"O nol A \"O , Long P and Pad r T (2019) Contact-implicit trajectory optimization based on a variable smooth contact model and successive convexification. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 2447--2453. doi:10.1109/ICRA.2019.8794250

2019
[47]

IEEE Transactions on Robotics 39(6): 4691--4711

Pang T, Suh HJT, Yang L and Tedrake R (2023) Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models. IEEE Transactions on Robotics 39(6): 4691--4711. doi:10.1109/TRO.2023.3300230

2023
[48]

The International Journal of Robotics Research 33(1): 69--81

Posa M, Cantu C and Tedrake R (2014) A direct method for trajectory optimization of rigid bodies through contact. The International Journal of Robotics Research 33(1): 69--81

2014
[49]

IEEE Robotics and Automation Letters 6(4): 8150--8157

Psomopoulou E, Pestell N, Papadopoulos F, Lloyd J, Doulgeri Z and Lepora NF (2021) A robust controller for stable 3d pinching using tactile sensing. IEEE Robotics and Automation Letters 6(4): 8150--8157. doi:10.1109/LRA.2021.3104057

2021
[50]

In: Proceedings of The 7th Conference on Robot Learning

Qi H, Yi B, Suresh S, Lambeta M, Ma Y, Calandra R and Malik J (2023) General in-hand object rotation with vision and touch. In: Proceedings of The 7th Conference on Robot Learning. PMLR, pp. 2549--2564

2023
[51]

In: European Conference on Computer Vision

Qin Y, Wu YH, Liu S, Jiang H, Yang R, Fu Y and Wang X (2022) Dexmv: Imitation learning for dexterous manipulation from human videos. In: European Conference on Computer Vision. Springer, pp. 570--587

2022
[52]

In: 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)

Röstel L, Pitz J, Sievers L and Bäuml B (2023) Estimator-coupled reinforcement learning for robust purely tactile in-hand manipulation. In: 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids). pp. 1--8. doi:10.1109/Humanoids57100.2023.10375194

2023
[53]

In: Proceedings of Robotics: Science and Systems

Si Z, Zhang KL, Temel Z and Kroemer O (2024) Tilde: Teleoperation for Dexterous In-Hand Manipulation Learning with a DeltaHand . In: Proceedings of Robotics: Science and Systems. doi:10.15607/RSS.2024.XX.128

2024 doi
[54]

In: 2022 International Conference on Robotics and Automation (ICRA)

Sievers L, Pitz J and Bäuml B (2022) Learning purely tactile in-hand manipulation with a torque-controlled hand. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 2745--2751. doi:10.1109/ICRA46639.2022.9812093

2022
[55]

Science Robotics 8(81): eadg5014

Sleiman JP, Farshidian F and Hutter M (2023) Versatile multicontact planning and control for legged loco-manipulation. Science Robotics 8(81): eadg5014

2023
[56]

In: 2018 IEEE International Conference on Robotics and Automation (ICRA)

Sundaralingam B and Hermans T (2018) Geometric in-hand regrasp planning: Alternating optimization of finger gaits and in-grasp manipulation. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). pp. 231--238. doi:10.1109/ICRA.2018.8460496

2018
[57]

In: Autonomous Robots, volume 43

Sundaralingam B and Hermans T (2019) Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation. In: Autonomous Robots, volume 43. Springer, pp. 469--483

2019
[58]

Science Robotics 9(96): eadl0628

Suresh S, Qi H, Wu T, Fan T, Pineda L, Lambeta M, Malik J, Kalakrishnan M, Calandra R, Kaess M, Ortiz J and Mukadam M (2024) Neuralfeels with neural fields: Visuotactile perception for in-hand manipulation. Science Robotics 9(96): eadl0628

2024
[59]

In: 2014 IEEE International Conference on Robotics and Automation (ICRA)

Tassa Y, Mansard N and Todorov E (2014) Control-limited differential dynamic programming. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). pp. 1168--1175. doi:10.1109/ICRA.2014.6907001

2014
[60]

In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

Todorov E, Erez T and Tassa Y (2012) Mujoco: A physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 5026--5033. doi:10.1109/IROS.2012.6386109

2012
[61]

In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Wang J and Olson E (2016) Apriltag 2: Efficient and robust fiducial detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4193--4198. doi:10.1109/IROS.2016.7759617

2016
[62]

In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Winkelbauer D, Triebel R and Bäuml B (2024) A learning-based controller for multi-contact grasps on unknown objects with a dexterous hand. In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 7331--7337. doi:10.1109/IROS58592.2024.10801894

2024
[63]

IEEE Robotics and Automation Letters 10(5): 5170--5177

Yang F, Power T, Marinovic SA, Iba S, Zarrin RS and Berenson D (2025 a ) Multi-finger manipulation via trajectory optimization with differentiable rolling and geometric constraints. IEEE Robotics and Automation Letters 10(5): 5170--5177. doi:10.1109/LRA.2025.3557752

2025
[64]

In: Proceedings of The 8th Conference on Robot Learning

Yang M, lu c, Church A, Lin Y, Ford CJ, Li H, Psomopoulou E, Barton DA and Lepora NF (2025 b ) Anyrotate: Gravity-invariant in-hand object rotation with sim-to-real touch. In: Proceedings of The 8th Conference on Robot Learning. PMLR, pp. 4727--4747

2025
[65]

IEEE Robotics and Automation Letters 10(5): 4212--4219

Yang W and Jin W (2025) Contactsdf: Signed distance functions as multi-contact models for dexterous manipulation. IEEE Robotics and Automation Letters 10(5): 4212--4219. doi:10.1109/LRA.2025.3548503

2025
[66]

Frontiers in Neurorobotics 16: 861825

Yu C and Wang P (2022) Dexterous manipulation for multi-fingered robotic hands with reinforcement learning: A review. Frontiers in Neurorobotics 16: 861825

2022
[67]

IEEE Robotics and Automation Letters 10(5): 4738--4745

Yu M, Jiang Y, Chen C, Jia Y and Li X (2025) Robotic in-hand manipulation for large-range precise object movement: The rgmc champion solution. IEEE Robotics and Automation Letters 10(5): 4738--4745. doi:10.1109/LRA.2025.3555138

2025
[68]

arXiv preprint arXiv:2503.04613

Zhang JZ, Howell TA, Yi Z, Pan C, Shi G, Qu G, Erez T, Tassa Y and Manchester Z (2025 a ) Whole-body model-predictive control of legged robots with mujoco. arXiv preprint arXiv:2503.04613

2025
[69]

arXiv preprint arXiv:2202.06211

Zhang L, Wang Y and Jiang Y (2022) Tac3d: A novel vision-based tactile sensor for measuring forces distribution and estimating friction coefficient distribution. arXiv preprint arXiv:2202.06211

2022 arXiv
[70]

IEEE Transactions on Robotics 41: 2677--2690

Zhang M, Jha DK, Raghunathan AU and Hauser K (2025 b ) Simultaneous trajectory optimization and contact selection for contact-rich manipulation with high-fidelity geometry. IEEE Transactions on Robotics 41: 2677--2690. doi:10.1109/TRO.2025.3554380

2025
[71]

In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Zhu H, Meduri A and Righetti L (2023) Efficient object manipulation planning with monte carlo tree search. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 10628--10635. doi:10.1109/IROS55552.2023.10341813

2023
[72]

, " * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter doi edition editor eid howpublished institution isbn journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence...
[73]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...
[74]

, " * write output.state after.block = add.period write newline

ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type url volume year label INTEGERS output.state before.all mid.sentence after.sentence aft...
[75]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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

[1] [2]

IEEE Transactions on Robotics 40: 3879--3896

Aydinoglu A, Wei A, Huang WC and Posa M (2024) Consensus complementarity control for multicontact mpc. IEEE Transactions on Robotics 40: 3879--3896. doi:10.1109/TRO.2024.3435423

arXiv 2024

[2] [3]

Science 364(6446): eaat8414

Billard A and Kragic D (2019) Trends and challenges in robot manipulation. Science 364(6446): eaat8414

work page 2019

[3] [4]

In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Chen C, Culbertson P, Lepert M, Schwager M and Bohg J (2021) Trajectotree: Trajectory optimization meets tree search for planning multi-contact dexterous manipulation. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 8262--8268. doi:10.1109/IROS51168.2021.9636346

arXiv 2021

[4] [5]

arXiv preprint arXiv:2412.16490

Chen J, Ke Y and Wang H (2024) Bodex: Scalable and efficient robotic dexterous grasp synthesis using bilevel optimization. arXiv preprint arXiv:2412.16490

arXiv 2024

[5] [6]

Science Robotics 8(84): eadc9244

Chen T, Tippur M, Wu S, Kumar V, Adelson E and Agrawal P (2023 a ) Visual dexterity: In-hand reorientation of novel and complex object shapes. Science Robotics 8(84): eadc9244

work page 2023

[6] [7]

IEEE Transactions on Pattern Analysis and Machine Intelligence 46(5): 2804--2818

Chen Y, Geng Y, Zhong F, Ji J, Jiang J, Lu Z, Dong H and Yang Y (2023 b ) Bi-dexhands: Towards human-level bimanual dexterous manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 46(5): 2804--2818

work page 2023

[7] [8]

In: Proceedings of The 8th Conference on Robot Learning

Chen Y, Wang C, Yang Y and Liu K (2025) Object-centric dexterous manipulation from human motion data. In: Proceedings of The 8th Conference on Robot Learning. PMLR, pp. 3828--3846

work page 2025

[8] [9]

In: 2015 IEEE International Conference on Robotics and Automation (ICRA)

Chen Z, Wimböck T, Roa MA, Pleintinger B, Neves M, Ott C, Borst C and Lii NY (2015) An adaptive compliant multi-finger approach-to-grasp strategy for objects with position uncertainties. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). pp. 4911--4918. doi:10.1109/ICRA.2015.7139881

arXiv 2015