Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI

Sze Yiu Chau; Youqian Zhang; Zirui Kong

arxiv: 2605.13492 · v2 · pith:65RFKOOAnew · submitted 2026-05-13 · 💻 cs.CR

Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI

Zirui Kong , Youqian Zhang , Sze Yiu Chau This is my paper

Pith reviewed 2026-05-25 06:06 UTC · model grok-4.3

classification 💻 cs.CR

keywords electromagnetic interferencetactile sensorsadversarial attackrobot securityhall-effect sensorsphantom forcesembodied intelligence

0 comments

The pith

Targeted electromagnetic interference on Hall-effect sensors can create phantom forces that amplify perceived magnitude over 9 times and shift direction by 65 degrees.

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

The paper establishes that Hall-effect fingertip sensors used by embodied robots are susceptible to intentional electromagnetic interference. A crafted EMI signal produces phantom forces that exaggerate both the size and direction of sensed contact. If this holds, attackers could alter robot behavior through the tactile channel alone, without physical contact. Readers should care because many robots rely on these sensors for safe physical interaction, and false force readings could lead to unintended actions like crushing items or releasing loads. The demonstration extends known adversarial techniques from vision systems to the tactile sensing domain in real hardware.

Core claim

This work demonstrates a vulnerability in Hall-effect fingertip sensors to intentional Electromagnetic Interference (EMI). A targeted signal injection induces strong phantom forces, amplifying perceived force magnitude by over 9× and deviating the inferred force direction by 65°. Such perturbations can paralyze learning-based tactile classification models, seriously affecting robot movement. An attacker could exploit this vulnerability to coerce a robot hand into crushing fragile objects or dropping dangerous payloads.

What carries the argument

Targeted electromagnetic interference signal injection into Hall-effect fingertip sensors to induce phantom force readings.

If this is right

Learning-based tactile classification models become unreliable under the injected signals.
Robot movement and control decisions can be disrupted without direct physical tampering.
A robot hand can be made to apply excessive force to fragile objects.
Dangerous payloads can be released unintentionally through altered force perception.

Where Pith is reading between the lines

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

The same EMI approach could be tested on other force-sensing technologies beyond Hall-effect devices.
Robot safety standards may need to incorporate electromagnetic shielding or signal validation steps for tactile channels.
Anomaly detection added to tactile processing pipelines could serve as a practical countermeasure.

Load-bearing premise

The demonstrated EMI attack on Hall-effect fingertip sensors can be executed in practical settings to produce controlled, repeatable phantom forces that reliably affect downstream robot control and learning models.

What would settle it

An experiment in a realistic environment that shows no measurable phantom forces arise from the EMI signal or that the induced readings leave tactile classification models and robot trajectories unaffected.

Figures

Figures reproduced from arXiv: 2605.13492 by Sze Yiu Chau, Youqian Zhang, Zirui Kong.

**Figure 2.** Figure 2: Attack experiment model and setup in reality. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Sensor Attack Simulation during the Stable Hover [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗

read the original abstract

Embodied intelligent robots rely on tactile sensors to interact with the physical world safely. While the security of visual perception systems has been studied (e.g., adversarial samples), the integrity of the tactile sensory channel remains unexplored. This work explores a vulnerability in Hall-effect fingertip sensors, showing their susceptibility to intentional Electromagnetic Interference (EMI). We demonstrate that a targeted signal injection can induce strong "phantom forces", amplifying perceived force magnitude by over 9$\times$ and deviating the inferred force direction by 65$^\circ$. Such perturbations can paralyze learning-based tactile classification models, seriously affecting robot movement. An attacker could exploit this vulnerability to coerce a robot hand into crushing fragile objects or dropping dangerous payloads.

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

The paper flags EMI on Hall-effect tactile sensors as a new attack surface for robots but the practical claims rest on unshown experiments.

read the letter

The main takeaway is that targeted EMI can create phantom forces in robot fingertip sensors, boosting perceived magnitude over 9x and shifting direction by 65 degrees, enough to break learning-based classifiers and change grasping behavior. This is new because prior work has focused on visual channels while leaving the tactile one largely untouched in embodied systems. The authors correctly note the gap and tie the sensor-level effect to downstream robot actions like crushing objects or dropping loads. That framing is useful for the security side of physical AI. The execution details are the soft spot. The abstract states the outcomes but gives no numbers on injection distance, frequency, power levels, or whether the robot body provides shielding. The stress-test note is accurate here: without evidence that the effect holds during actual motion or grasping, it is unclear if the bench result scales to the claimed real-world impact. If the full paper contains those controls and repeatability tests, they are not visible in the provided material. This work is aimed at the narrow group studying sensor-level attacks on robots. A reader already tracking physical-layer vulnerabilities might pick up the idea and try to reproduce it, but the current version does not supply enough to cite or extend directly. I would not send it to peer review yet; the authors need to show the experimental parameters and real-robot results before it earns referee time.

Referee Report

2 major / 1 minor

Summary. The paper claims that Hall-effect fingertip sensors in embodied robots are vulnerable to targeted electromagnetic interference (EMI), enabling injection of 'phantom forces' that amplify perceived force magnitude by over 9× and deviate direction by 65°. These perturbations can disable learning-based tactile classifiers, altering robot behavior in ways that could cause unsafe actions such as crushing objects or dropping payloads. The work positions this as the first exploration of EMI attacks on tactile channels, contrasting with prior work on visual perception.

Significance. If the experimental claims are substantiated with repeatable, real-world parameters, the result would identify a new physical-layer attack surface on tactile sensing that has received little prior attention. This could motivate sensor shielding standards and robust tactile models in robotics, with direct relevance to safety-critical embodied systems.

major comments (2)

[Abstract] Abstract: the central claims of 9× force amplification and 65° directional deviation are presented without any reported parameters for injection distance, transmit power, frequency, antenna placement relative to the robot body, or repeatability under motion; these omissions make it impossible to evaluate whether the bench-top effect transfers to the claimed impact on grasping or payload handling.
[Experimental Results (missing)] No experimental section or results table provides setup details, raw sensor traces, error bars, or statistical validation for the reported 9× and 65° figures, leaving the load-bearing assertion that the attack 'seriously affect[s] robot movement' unsupported by verifiable data.

minor comments (1)

[Abstract] The abstract would benefit from one or two citations to prior EMI work on other sensors or to tactile sensor datasheets to situate the novelty claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater experimental transparency. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

Referee: [Abstract] Abstract: the central claims of 9× force amplification and 65° directional deviation are presented without any reported parameters for injection distance, transmit power, frequency, antenna placement relative to the robot body, or repeatability under motion; these omissions make it impossible to evaluate whether the bench-top effect transfers to the claimed impact on grasping or payload handling.

Authors: We agree the abstract should be self-contained with key parameters. In revision we will incorporate concise values for injection distance, transmit power, frequency, antenna placement, and repeatability metrics while preserving the 150-word limit. These parameters are already quantified in the experimental setup section of the full manuscript; the revision will simply surface them in the abstract for immediate context. revision: yes
Referee: [Experimental Results (missing)] No experimental section or results table provides setup details, raw sensor traces, error bars, or statistical validation for the reported 9× and 65° figures, leaving the load-bearing assertion that the attack 'seriously affect[s] robot movement' unsupported by verifiable data.

Authors: We acknowledge that the current presentation of results can be strengthened for verifiability. The manuscript contains an experimental methods subsection and sensor trace figures, but we will expand it in revision to include a dedicated results table summarizing raw data statistics, error bars across repeated trials, and quantitative validation of the effect on robot behavior (e.g., grasp failure rates). This directly supports the claim of impact on movement. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental demonstration with no derivations or self-referential claims

full rationale

The paper presents an empirical experimental demonstration of EMI attacks inducing phantom forces on Hall-effect tactile sensors. The abstract and described content contain no equations, derivations, fitted parameters, or self-citations that reduce any central claim to its own inputs by construction. Claims about 9× magnitude amplification and 65° direction deviation are presented as measured outcomes rather than predictions derived from prior fits or self-referential definitions. No load-bearing steps match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on an experimental demonstration whose details are not provided; no free parameters, ad-hoc axioms, or invented entities are mentioned in the abstract.

axioms (1)

domain assumption Hall-effect fingertip sensors respond to external electromagnetic fields in a manner that allows targeted force misreporting
Implicit assumption required for the EMI injection to produce phantom forces as described.

pith-pipeline@v0.9.0 · 5650 in / 1123 out tokens · 23365 ms · 2026-05-25T06:06:21.805817+00:00 · methodology

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Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

NVIDIA. [n. d.].Isaac Sim. https://github.com/isaac-sim/IsaacSim

work page
[2]

2006.How the Body Shapes the Way We Think: A New View of Intelligence

Rolf Pfeifer and Josh Bongard. 2006.How the Body Shapes the Way We Think: A New View of Intelligence. The MIT Press. doi:10.7551/mitpress/3585.001.0001

work page doi:10.7551/mitpress/3585.001.0001 2006
[3]

Yuchuang Tong, Haotian Liu, and Zhengtao Zhang. 2024. Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects.IEEE/CAA Journal of Automatica Sinica11, 2 (February 2024), 301–328. doi:10.1109/JAS.2023. 124140

work page doi:10.1109/jas.2023 2024

[1] [1]

NVIDIA. [n. d.].Isaac Sim. https://github.com/isaac-sim/IsaacSim

work page

[2] [2]

2006.How the Body Shapes the Way We Think: A New View of Intelligence

Rolf Pfeifer and Josh Bongard. 2006.How the Body Shapes the Way We Think: A New View of Intelligence. The MIT Press. doi:10.7551/mitpress/3585.001.0001

work page doi:10.7551/mitpress/3585.001.0001 2006

[3] [3]

Yuchuang Tong, Haotian Liu, and Zhengtao Zhang. 2024. Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects.IEEE/CAA Journal of Automatica Sinica11, 2 (February 2024), 301–328. doi:10.1109/JAS.2023. 124140

work page doi:10.1109/jas.2023 2024