Compliant Non-Prehensile Pushing Manipulation

Fabio Amadio; Fabio Ruggiero; Francesco Cufino; Mario Selvaggio

arxiv: 2605.25672 · v1 · pith:F54HX4SQnew · submitted 2026-05-25 · 💻 cs.RO

Compliant Non-Prehensile Pushing Manipulation

Francesco Cufino , Mario Selvaggio , Fabio Amadio , Fabio Ruggiero This is my paper

Pith reviewed 2026-06-29 21:44 UTC · model grok-4.3

classification 💻 cs.RO

keywords non-prehensile pushingcompliant manipulationmodel predictive controlimpedance controlpassivityenergy tankrobotic pushing

0 comments

The pith

An MPC framework with energy tank filter allows compliant non-prehensile pushing while maintaining passivity.

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

The paper extends an existing pushing model to work with impedance-controlled robots. It then uses this model in a model predictive controller that chooses the best robot position and velocity targets to push an object correctly by adjusting force and contact location. An energy tank filter is added to adjust velocity and stop energy from building up when external forces cause tracking errors. This setup was tested in simulations and on two real robot systems to show it stays passive during human interactions and handles changes in object properties.

Core claim

The MPC framework enables compliant pushing through optimal modulation of the robot's position/velocity set-point, jointly realizing the required pushing force and contact point adaptation to obtain desired object motion, while the energy tank passivity filter guarantees passivity and avoids uncontrolled energy buildup.

What carries the argument

Model predictive control framework built on the extended pushing model, with integrated energy tank passivity filter for modulating velocity set-points.

If this is right

Compliant pushing operations can be performed safely in environments with humans.
Passive behavior is guaranteed during external physical interactions.
Desired object motions are achieved even with variations in the object's physical parameters.
Tracking errors from disturbances do not lead to indefinite increases in pushing force.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This approach could be applied to other manipulation tasks requiring compliance, such as sliding heavy objects.
Combining the framework with online estimation of object parameters might improve robustness further.

Load-bearing premise

The state-of-the-art pushing model can be directly extended and integrated with impedance control to yield an MPC that produces stable passive behavior under disturbances without extra unmodeled terms.

What would settle it

An experiment in which external disturbances during pushing cause either a loss of passivity or failure to track the desired object trajectory when using the proposed MPC and filter.

Figures

Figures reproduced from arXiv: 2605.25672 by Fabio Amadio, Fabio Ruggiero, Francesco Cufino, Mario Selvaggio.

**Figure 2.** Figure 2: Block scheme of the considered control framework. Our proposed compliant pushing manipulation controller (dashed box) is composed by a planar [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Planar pushing model of a rectangular object pushed via a contact [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Compliant pushing model of an object pushed by a spring-like force [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Illustration of the proposed compliant non-prehensile pushing ma [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Port representation of the compliant pushing system interacting with [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 8.** Figure 8: Time history of the object tracking error [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 10.** Figure 10: Time history of the object tracking error [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗

**Figure 11.** Figure 11: The Mobile YuMi platform while pushing a rack of test tubes. [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗

**Figure 13.** Figure 13: Time history of the tank energy (a) and tank activation parameter (b) during the linear trajectory experiment. B1) Experimental Setup: The KUKA LBR iiwa 7 is a 7-DOF lightweight collaborative robot designed for precise assembly tasks to be performed alongside humans. The Fast Robot Interface (FRI) allows commanding the robot by sending joint torque/position commands rates up to 1-KHz. The control framewo… view at source ↗

**Figure 12.** Figure 12: Time history of the desired and measured object position [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗

**Figure 14.** Figure 14: Time history of the desired and measured object position [PITH_FULL_IMAGE:figures/full_fig_p013_14.png] view at source ↗

**Figure 15.** Figure 15: Time history of the tank energy (a) and tank activation parameter (b) during the curvilinear trajectory experiment. pushing tool about the vertical axis is regulated through a PD controller to keep alignment with the object. The controller parameters are reported in Table III. These values were obtained using the same tuning procedure adopted in the previous experiments, namely iterative adjustments aimed… view at source ↗

**Figure 16.** Figure 16: Box plots showing the aggregated results of the experimental campaign conducted to assess the robustness of the method. [PITH_FULL_IMAGE:figures/full_fig_p014_16.png] view at source ↗

read the original abstract

In this paper, we address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system. To ensure safe operations in human-populated environments, robots must comply with external physical interactions and exhibit passive behavior. To achieve this, we extend a state-of-the-art pushing model to integrate it with impedance-controlled robots. We develop a model predictive control framework built upon this model that enables compliant pushing through optimal modulation of the robot's position/velocity set-point, jointly realizing the required pushing force and contact point adaptation to obtain desired object motion. However, external interactions may induce tracking errors, causing a consequent potentially indefinite increase of the pushing force. To prevent this, we integrate an energy tank passivity filter that further modulates the robot velocity set-point to guarantee passivity and avoid uncontrolled energy buildup. The proposed method has been rigorously tested in simulation and validated through experiments on two different robotic systems, demonstrating passive compliance during human-robot interactions and assessing trajectory tracking performance and robustness to variations in the object's physical parameters.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a straightforward engineering integration of an existing pushing model with MPC and an energy tank for compliant non-prehensile tasks, with decent experimental checks but no major conceptual advance.

read the letter

The paper's main move is to take a prior pushing model, embed it in an MPC that tweaks the robot's position and velocity setpoints to produce the right force and contact adaptation, then run the output through an energy tank filter so the impedance-controlled system stays passive even when tracking errors appear. That combination is presented as the new framework.

The experiments are the strongest part. The authors report simulation results plus hardware tests on two separate robotic systems, covering human-robot contact, trajectory tracking, and robustness when object parameters change. For applied work on safe manipulation around people, those checks give a concrete sense of how the method behaves.

The approach follows a familiar sequence without internal contradictions: model extension first, then optimization, then the passivity layer applied to the velocity command. The stress-test note is accurate here; nothing in the description suggests the tank is being used in a way that would break the guarantee or that the MPC is relying on unstated compensation terms.

The soft spot is the incremental nature of the contribution. It layers standard tools on top of cited prior work rather than deriving a new model or control law from scratch. Without equations in the abstract it is hard to judge how cleanly the extension was done or whether the passivity holds exactly under the stated disturbances, but the overall logic lines up.

This is useful reading for people already working on non-prehensile pushing who need to add compliance for deployment safety. A robotics engineer looking for a packaged way to handle contact adaptation and passivity in pushing tasks would get practical value from the setup and the reported tests.

I would send it for peer review. The validation is present and the claims are consistent on their own terms, so specialists can check the derivations and experiment details.

Referee Report

0 major / 0 minor

Summary. The paper proposes extending a state-of-the-art pushing model for integration with impedance-controlled robots, then embedding it in an MPC framework that modulates the robot's position/velocity set-point to achieve compliant non-prehensile pushing while realizing required forces and contact adaptation. An energy-tank passivity filter is layered on the velocity command to enforce passivity and bound energy under external disturbances and tracking errors. The approach is evaluated in simulation and on two physical robotic platforms for passive compliance during human interaction, trajectory tracking, and robustness to object parameter variation.

Significance. If the model extension and tank application preserve the claimed guarantees without unmodeled compensation, the work supplies a concrete, experimentally validated pipeline for safe pushing manipulation that combines predictive set-point optimization with energy-based passivity; this is a useful engineering contribution for human-populated environments where both compliance and object-motion tracking are required.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of the manuscript and for noting the potential utility of the MPC-plus-energy-tank pipeline for compliant pushing in human environments. The recommendation is listed as uncertain, yet the report contains no enumerated major comments. We therefore have no specific points to rebut or revise at this time and remain available to address any additional questions the referee may wish to raise.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes extending an external state-of-the-art pushing model, embedding it in an MPC framework for set-point modulation, and layering a standard energy-tank passivity filter. No equations or claims in the provided abstract reduce by construction to author-defined fitted parameters, self-citations that bear the central load, or ansatzes smuggled from prior work. The derivation chain relies on independent external models and established passivity techniques, with validation via simulation and experiments on separate hardware. This is a standard engineering pipeline without internal reduction to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no concrete free parameters, axioms, or invented entities can be extracted. The approach depends on the accuracy of an external pushing model and standard passivity concepts whose details are not provided here.

pith-pipeline@v0.9.1-grok · 5708 in / 1120 out tokens · 35706 ms · 2026-06-29T21:44:29.114091+00:00 · methodology

discussion (0)

Reference graph

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