Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation

Ing-Sheng Bernard-Tiong; Kazuki Shibata; Ryosuke Sota; Takamitsu Matsubara; Yoshihisa Tsurumine

REVIEW 4 major objections 5 minor 15 references

Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation

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

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Pith's one-line read A ternary force discretization lets two robots keep coordinating despite changes in grip force, object shape, and size.

desk verdict Solid simulation evidence for a simple ternary force trick, but the real-robot claims overstate what the data show. read the letter →

arxiv 2411.13942 v2 pith:KGRH7OBW submitted 2024-11-21 cs.RO

Ing-Sheng Bernard-Tiong , Yoshihisa Tsurumine , Ryosuke Sota , Kazuki Shibata , Takamitsu Matsubara This is my paper · ORCID

classification cs.RO

keywords multi-agentreinforcementlearningternaryforcerepresentationforce-sensingfeedbackcooperativeobjecttransportationasymmetricactor-criticsim-to-realtransferimplicitcommunication

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that two robots can coordinate grasping and transporting an object using only force feedback, without explicit communication, if the force signal is reduced to a ternary value indicating whether the force between timesteps increased, decreased, or stayed the same. The central claim is that this ternary representation stays consistent when the grasping force, object geometry, or object size changes, whereas raw force signals shift with those variations and destabilize coordination. To recover the magnitude information lost in discretization, the authors train an asymmetric actor-critic: the actor sees only ternary forces at deployment, while the critic sees both the ternary signal and the full delta force during training. If the claim holds, force-only coordination becomes practical in changing contact conditions and can transfer from simulation to real hardware without retraining.

What carries the argument

The ternary force representation is the central object: it is the sign of the change in gripper force between consecutive timesteps, taking values $-1$, $0$, or $1$ according to whether the force decreased, stayed the same, or increased. It carries the robustness argument by removing dependence on force magnitude and sensor scale. The supporting mechanism is the asymmetric actor-critic, where the actor observes only $T(\Delta F_i(t))$ during execution but the critic additionally sees $\Delta F_i(t)$ during training, supplying the magnitude information needed for credit assignment and mitigating the partial observability introduced by discretization.

What would settle it

Record the ternary observations while replaying the trained policy under a much wider set of contact changes than the paper tests, such as grasping force scaled by 0.25 or 4, a deformable object, or a shifted grasp pose; if the mean and variance of the ternary distribution shift as much as the raw force distribution does, the consistency claim is falsified. A quicker check is to artificially flip the ternary signs during execution and see whether transport success collapses, which would show that the coordination depends on sign accuracy rather than magnitude robustness.

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Extended reading notes

Core claim

The discovery is that discretizing the delta force $\Delta F_i(t)=F_i(t)-F_i(t-1)$ into $\{-1,0,1\}$ by sign preserves the motion-relevant information for cooperative transport while discarding the magnitudes that depend on grip force, contact area, and object geometry. Using this ternary force as the actor's observation, with the critic given both the ternary and the pre-discretization delta force, the trained policies achieve object transport errors close to the raw-force baseline in simulation and succeed on real two-robot transfers under a different sensor model and different object sizes. The authors show quantitatively that raw force observations have noticeably different mean and variance under different grasping forces, while the ternary representation's distribution changes little, which they identify as the mechanism behind the robustness.

Load-bearing premise

The load-bearing premise is that the distribution of ternary force observations seen during simulation training matches the real world closely enough that a simulation-trained policy acts within its learned distribution on physical robots despite different sensors, friction, and control frequency.

Editorial extensions

If this is right

A force-only coordination signal can replace explicit communication for cooperative transport, removing failure modes from communication delays and interruptions.
The same simulation-trained policy can be deployed on a physical robot pair without retraining, even though the real sensor has a different scale, noise, and bias than the simulation sensor.
Changes to grasping force within a factor of two and changes from a rectangular to a cylindrical grasping geometry keep success rates substantially above a raw-force baseline.
Real-robot transport remains functional when the object cross-section changes from 4 cm by 4 cm to 3 cm by 3 cm, although final position error grows relative to simulation.
Because only ternary values are used at execution, the method does not need to maintain calibrated absolute force magnitudes across different contact conditions.

Reading between the lines

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

The sign-based discretization is a generic scale-invariance trick: it could be applied to any tactile or force feature stream to build coordination signals insensitive to sensor calibration, a generalization the paper does not claim.
The framework's robustness should depend mainly on whether the sign of the delta force flips under new conditions; a natural stress test would be to vary control frequency or add sensor noise until signs flip and measure the resulting success-rate cliff.
The two-robot setting is the basic case; if the representation is truly consistent, extending to more robots with pairwise force observations should behave similarly, but that scaling remains untested.
The paper does not vary the discretization levels; testing two-level or four-level representations would reveal the trade-off between information content and robustness that the ternary choice optimizes.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Core idea is neat: discretize delta force into {-1,0,1} to make force observations invariant to grasp-force scaling and geometry. It's not a deep insight, but the simulation evidence suggests it works. Under held-out changes to grasping force (0.5x, 2x), your method keeps 85%/80% success versus 40%/30% for raw force—that's a compelling difference. The geometry shift (rectangular to cylinder) is weaker (51% vs 25%) but still in the right direction. The asymmetric actor-critic is a sensible way to compensate for the information loss, and the force-observation statistics (Table III) support the claimed mechanism. Simulation support is legitimate.

The real-robot section is where the paper overreaches. You define success as final position error under 6 cm. The real robot means are 22.67 and 30.88 cm, with variance 65 and 57, from five trials each. That's 3.8–5.1 times your own threshold. By your metric, none of those real trials succeeded. Yet the abstract and Section V claim the robots 'successfully grasp and transport the object to the target position.' There is a mismatch. The Discussion does admit a 'significant discrepancy' and 'notable performance gap,' but that doesn't reconcile the claim with the metric. You also don't report a quantitative raw-force baseline for the real robot—only 'they hovered around the object.' With five trials and no baseline numbers, the sim2real robustness claim is weakly supported at best.

The core method deserves a serious referee. The contribution is incremental, but legitimate: a simple force pre-processing that buys robustness, demonstrated with a fair held-out evaluation. The real-robot claims need to be rewritten: report the raw baseline, increase trial count if feasible, and either relax the success threshold or describe the results as approximate transport rather than 'success.' If the authors correct the reporting and qualify the real-robot claims, the paper is publishable. I'd send it to review with a request for major revision, not desk reject it.

This paper is for people working on communication-free multi-robot manipulation and sim2real transfer. The ternary representation is a useful trick to know. I'd probably cite it if I worked in that area, but it's not central to my work.

Recommendation: engage with it as a referee, but treat the real-robot results as preliminary and demand a corrected narrative.

Referee Report

4 major / 5 minor

Summary. The paper addresses cooperative grasping and transportation by two robots using force feedback as implicit communication. It introduces a ternary force representation obtained by discretizing the change in gripper force between consecutive time steps into {-1, 0, 1}, and trains MAPPO policies with an asymmetric actor-critic in which the actor observes only the ternary force while the critic also sees the pre-discretization delta force. The authors evaluate the method in Isaac Sim against raw-force, no-force, and ternary-only baselines under variations in grasping force and object geometry, and deploy the simulation-trained policy on two HSRs with GelSight sensors to test object-size variations and sim2real transfer. They report that the proposed representation preserves success under force scaling while raw-force policies degrade, and that the real robots can grasp and transport objects, though with larger position errors than in simulation.

Significance. If the results hold, the ternary representation is an elegant and inexpensive way to make force-based implicit communication invariant to sensor scaling and contact-geometry changes, and the asymmetric actor-critic is a sensible way to mitigate the resulting partial observability. The simulation study is well designed: it compares several baselines, uses 100 test episodes, reports five training seeds, and tests held-out force scales and object geometries. The real-robot deployment is a genuine effort to test sim2real transfer. However, the real-robot evidence as reported does not meet the paper's own success criterion, so the strength of the sim2real claim is currently limited.

major comments (4)

[V-B, Table V] The real-robot experiments are not successes by the paper's own metric. Section IV-B defines a successful task as a final Euclidean distance between object and target under 6 cm. Table V reports mean final position errors of 22.67 cm and 30.88 cm, i.e., 3.8 and 5.1 times the success threshold, with variances of 65.32 and 57.19 across five trials. The abstract and Section V-B state that robots "successfully grasp and transport the object to the target position," but the quantitative data show the object did not reach the target in the reported trials. This internal inconsistency affects the load-bearing sim2real claim; the authors should report the percentage of trials satisfying the 6 cm criterion, clearly justify any different success metric used for the real-robot experiments, and adjust the claims accordingly.
[V-B, Raw Force baseline] The comparison with Raw Force on real robots is only qualitative: the authors write that the raw-force-trained robots "only hovered around the object without ever grasping it," with no position-error data, no success counts, and no number of trials. Since the central claim is that the ternary representation is more robust than raw force, the real-robot section should provide the same quantitative outcome measures for the baseline; otherwise the comparative robustness claim rests only on simulation.
[III-B.1] The definition of the ternary representation does not specify the threshold that separates 0 from ±1. Raw force signals from real sensors are noisy, so without an explicit deadband the claim that the representation is consistent under small variations is not well-defined. The paper should state the threshold used (or the quantization rule) and ideally provide a sensitivity analysis, since this discretization is the core mechanism of the method.
[V-B, Table V] The five-trial sample size and the large variances make the real-robot means statistically fragile. With variances of 65.32 and 57.19 over five trials, the standard errors are about 3.6 cm and 3.4 cm respectively, and no confidence intervals or per-trial values are provided, so the difference between the two object sizes is not interpretable as evidence of robustness to object-size variation.

minor comments (5)

[Fig. 2 caption] The caption contains a typo: "Asysmetric" should be "Asymmetric".
[IV-D.2.a, Table III] Table III reports force-observation statistics for grasping force scales 1 and 2, but the success-rate table includes scale 0.5; including the 0.5 case would make the consistency argument more complete.
[V-A and Fig. 5 caption] The text and Table V describe object cross-sections of 4×4 cm and 3×3 cm, while the Fig. 5 caption says a "4 cm-sized object"; please use consistent dimensional notation.
[IV-B] The phrase "force signals (without randomization)" is unclear; specify whether sensor noise was disabled or not randomized during training, since this matters for the sim2real discussion.
[I and VII] The abstract and conclusion claim robustness to object size based on the real-robot experiment, but only two sizes and five trials are reported; the generalization claim should be tempered or supported with more conditions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's robustness claims rest on held-out simulation and real-robot tests, and the only self-citation is background material.

full rationale

The paper does not derive its robustness claim from a fitted parameter, a definitional equivalence, or a self-citation chain. The ternary force representation is a designed preprocessing step: it computes the delta force between consecutive time steps and discretizes it to {-1, 0, 1}. The invariance of the sign of a positively scaled force is inherent to that definition, but the paper does not present this invariance as a derived prediction; it is the proposed mechanism. The load-bearing claims are empirical and tested on variations held out from training: grasping-force scaling factors of 0.5 and 2 (Table II), a cylindrical object geometry not seen in training (Table IV), and direct deployment of a simulation-trained policy on real robots (Table V and Fig. 5). These evaluations are not fitted to the test conditions, and the real-robot results are measured rather than assumed. The only self-citation, reference [6], appears in the related-work discussion of pushing-strategy MARL and is not load-bearing for the method, experiments, or conclusions. The Discussion section candidly reports the sim2real gap, including the lower real-robot control frequency (around 30 Hz versus 100 Hz) and unintended yaw rotation from the real HSR's omnidirectional mechanism, which further indicates that the real-robot outcome is an honest measurement rather than a constructed result. Any overstatement of real-robot success relative to the paper's own 6 cm success threshold is a correctness or reporting concern, not a circularity concern.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its load-bearing free choices are the reward weights and the unspecified digitization threshold for 'no change'. The main domain assumption is that simulated forces and dynamics are representative enough for zero-shot real-world transfer.

free parameters (2)

Reward weights w1-w6 = 3.0, 4.0, 7.5, 9.5, 20, 3.0
Hand-selected in Section IV-B to make training work; the policy's balance among reach, grasp, lift, and transport objectives is tied to these values.
Ternary zero-threshold = Not reported
Section III-B1 defines 0 as 'no change' in delta force, but with continuous sensor readings a finite dead-zone threshold must be used. The paper does not specify it, so the discretization depends on an unreported parameter that may affect robustness.

assumptions (4)

domain assumption Force sensor readings at the gripper fingers encode sufficient information about the other agent's actions and the object's motion for coordination.
The method builds the entire implicit communication channel on delta force signals (Section III-B1). If force does not reliably reflect inter-robot interaction, the ternary representation cannot support coordination.
domain assumption Isaac Sim dynamics and contact models are an adequate approximation of the real HSRs for zero-shot policy transfer.
Section V deploys the simulation-trained policy directly on real robots without fine-tuning. The paper acknowledges a sim2real gap in Section VI, so this assumption is only partially satisfied.
standard math Standard Dec-POMDP and discounted-return RL assumptions hold.
Used in Section III-A to formulate the problem and define the objective; no proof of convergence or optimality is provided.
ad hoc to paper The reward weights w1...w6 are an appropriate shaping of the true task objective.
The weights are manually chosen in Section IV-B with the note that 'higher coefficients for team rewards over individual rewards improve policy optimization.' The learned behavior depends on this choice.

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Cite this review

Pith. "Pith review of Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation." pith.science (2026). https://pith.science/paper/KGRH7OBW

@misc{pith2026241113942,
  author       = {Pith},
  title        = {Pith review of: Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KGRH7OBW}},
  note         = {Machine review of arXiv:2411.13942}
}

read the original abstract

Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interruptions; however, force-sensing is highly sensitive and prone to interference from variations in grasping environment, such as changes in grasping force, grasping pose, object size and geometry, which can interfere with force signals, subsequently undermining coordination. We propose multi-agent reinforcement learning (MARL) with ternary force representation, a force representation that maintains consistent representation against variations in grasping environment. The simulation and real-world experiments demonstrate the robustness of the proposed method to changes in grasping force, object size and geometry as well as inherent sim2real gap.

Figures

Figures reproduced from arXiv: 2411.13942 by the authors.

**Figure 2.** Asysmetric actor-critic-based MARL framework with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Simulation environment for cooperative grasping and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Successive frames of two HSRs trained by our method successfully grasp and transport an object to the target position. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Pith tools

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

[1] [1]

Kinematic multi-robot manipulation with no communication using force feedback,

Z. Wang and M. Schwager, “Kinematic multi-robot manipulation with no communication using force feedback,” in IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 427–432

work page 2016

[2] [2]

Cooperative manipulation exploiting only implicit communication,

A. Tsiamis, C. K. Verginis, C. P. Bechlioulis, and K. J. Kyriakopoulos, “Cooperative manipulation exploiting only implicit communication,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 864–869

work page 2015

[3] [3]

A collaborative control method of dual-arm robots based on deep reinforcement learning,

L. Liu, Q. Liu, Y . Song, B. Pang, X. Yuan, and Q. Xu, “A collaborative control method of dual-arm robots based on deep reinforcement learning,” Applied Sciences , vol. 11, no. 4, 2021

work page 2021

[4] [4]

The surprising effectiveness of PPO in cooperative multi-agent games,

C. Yu, A. Velu, E. Vinitsky, J. Gao, Y . Wang, A. Bayen, and Y . Wu, “The surprising effectiveness of PPO in cooperative multi-agent games,” in Conference on Neural Information Processing Systems (NeurIPS), vol. 35, 2022

work page 2022

[5] [5]

Occlusion-based cooperative transport with a swarm of miniature mobile robots,

J. Chen, M. Gauci, W. Li, A. Kolling, and R. Groß, “Occlusion-based cooperative transport with a swarm of miniature mobile robots,” IEEE Transactions on Robotics , vol. 31, no. 2, pp. 307–321, 2015

2015

[6] [6]

Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport,

K. Shibata, T. Jimbo, and T. Matsubara, “Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport,” Robotics and Autonomous Systems , vol. 159, p. 104307, 2023

work page 2023

[7] [7]

Cooperative object trans- portation by multiple humanoid robots,

M.-H. Wu, A. Konno, and M. Uchiyama, “Cooperative object trans- portation by multiple humanoid robots,” in IEEE/SICE International Symposium on System Integration (SII) , 2011, pp. 779–784

work page 2011

[8] [8]

The need for combining implicit and explicit communication in cooperative robotic systems,

N. Gildert, A. G. Millard, A. Pomfret, and J. Timmis, “The need for combining implicit and explicit communication in cooperative robotic systems,” Frontiers in Robotics and AI , vol. 5, 2018

work page 2018