arxiv: 2604.23272 · v1 · submitted 2026-04-25 · 💻 cs.RO

Recognition: unknown

Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models

Jimin Lee , Huiwon Jang , Myungkyu Koo , Jungwoo Park , Jinwoo Shin

Authors on Pith no claims yet

Pith reviewed 2026-05-08 08:08 UTC · model grok-4.3

classification 💻 cs.RO

keywords modular sensory streamvision-language-action modelsphysical feedbacktactile sensingtorque feedbackmulti-modal integrationrobot action predictioncontact dynamics

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The pith

A modular framework lets vision-language-action models incorporate multiple physical signals like touch and torque to improve robot action prediction.

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

The paper introduces MoSS as a way to add separate streams for different physical feedback types into existing vision-language-action models. These streams connect through shared attention mechanisms while a two-stage training process keeps the original model parameters fixed at first. An extra task that predicts upcoming physical signals helps the system learn contact dynamics. If this works, robots could use complementary inputs from sight, language, touch, and force together instead of relying on vision alone, which matters for tasks that require careful physical interaction. Real-world tests show that using multiple signals together produces better results than handling them separately.

Core claim

MoSS is a modular sensory stream framework that adapts pretrained Vision-Language-Action models to leverage multiple heterogeneous physical signals such as tactile and torque feedback for action prediction. It uses decoupled modality streams integrated via joint cross-modal self-attention, adopts a two-stage training scheme that freezes pretrained VLA parameters initially to allow stable addition of new signals, and adds an auxiliary task predicting future physical signals to capture contact interaction dynamics. Extensive real-world experiments demonstrate that this approach successfully augments VLAs to integrate diverse signals and achieve synergistic performance gains.

What carries the argument

Decoupled modality streams integrated via joint cross-modal self-attention, which connects new physical signals to the action prediction stream while the two-stage training and auxiliary prediction task stabilize the addition of modalities.

Load-bearing premise

Heterogeneous physical signals are complementary and can be stably added to pretrained vision-language-action models without causing interference or performance drops.

What would settle it

A set of identical real-world robot manipulation trials run once with only single physical signals, once without the auxiliary prediction task, and once with full MoSS, where the combined version fails to show higher task success rates or efficiency than the single-signal baselines.

Figures

Figures reproduced from arXiv: 2604.23272 by Huiwon Jang, Jimin Lee, Jinwoo Shin, Jungwoo Park, Myungkyu Koo.

**Figure 1.** Figure 1: Limitation of existing approaches to incorporating physical sensory signals into VLAs. We compare the average success rates (%) of VLAs as more diverse physical sensory signals are incorporated. Existing approaches (Huang et al., 2025b; Yu et al., 2025; Zhang et al., 2025b) often fail to handle multiple physical sensing modalities, leading to degraded performance when multiple signals are used together. Ho… view at source ↗

**Figure 2.** Figure 2: Overview of the proposed approach. We propose MoSS, a Modular Sensory Stream framework that integrates multiple physical sensory signals into VLAs. Building on a pretrained VLA, MoSS introduces a multimodal stream architecture that processes newly added physical signals (e.g., tactile and torque) in parallel. This figure illustrates a representative instantiation of MoSS with tactile and torque modalities,… view at source ↗

**Figure 3.** Figure 3: Examples of the evaluation tasks. We design contact-rich, real-world robotic manipulation tasks with systematically controlled variations (in object size, spatial layout, geometry, and appearance) that require the policy to rely on physical sensory signals beyond visual observations. These variations induce ambiguity in vision alone, making physical feedback essential for successful execution. This feature… view at source ↗

**Figure 4.** Figure 4: Example rollouts of real-world tasks. We provide example rollouts of the designed tasks that critically depend on physical feedback (e.g., tactile or torque signals). While MoSS leverages physical feedback to successfully perform the tasks, GR00T N1.5 without physical feedback often have difficulties in (a), (b) regulating grasp force, (c) maintaining appropriate pushing force for contact, and (d) probing … view at source ↗

**Figure 5.** Figure 5: Attention scores between action and physical modality stream. We visualize the standardized attention scores between action and tactile/torque sensory streams in the joint cross-modal self-attention layers while conducting tasks. We find that action tokens attend strongly to tactile and torque tokens at moments that demand accurate, contact-intensive control, e.g., (a) when tactile contact is detected, and… view at source ↗

**Figure 6.** Figure 6: Physical feedback and predicted values. We visualize the physical sensory signals (i.e., tactile and torque) predicted by MoSS while conducting tasks. We find that MoSS accurately predicts the changes in physical signals, particularly at moments when such signals are critical for successfully performing the task view at source ↗

**Figure 7.** Figure 7: Overview of our single-arm gripper platform. The platform consists of a 7-DoF Franka Research 3 robot arm equipped with a Robotiq 2F-85 gripper and dual AnySkin tactile sensors mounted on the gripper fingers, together with a wrist-mounted Zed Mini stereo camera for visual observation. This configuration provides synchronized visual, tactile, and proprioceptive sensing for contact-rich manipulation. A.3. Im… view at source ↗

**Figure 8.** Figure 8: Failure case examples. 13 view at source ↗

read the original abstract

Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approaches attempt to incorporate physical sensory signals into Vision-Language-Action models (VLAs). However, they typically focus on a single type of physical signal, failing to capture the heterogeneous and complementary nature of real-world interactions. In this paper, we propose MoSS, a modular sensory stream framework that adapts VLAs to leverage multiple sensory signals for action prediction. Specifically, we introduce decoupled modality streams that integrate heterogeneous physical signals into the action stream via joint cross-modal self-attention. To enable stable incorporation of new modalities, we adopt a two-stage training scheme that freezes pretrained VLA parameters in the early stage. Furthermore, to better capture contact interaction dynamics, we incorporate an auxiliary task that predicts future physical signals. Through extensive real-world experiments, we demonstrate that MoSS successfully augments VLAs to leverage diverse physical signals (i.e., tactile and torque), integrating multiple signals to achieve synergistic performance gains.

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

MoSS gives a modular way to add tactile and torque streams to VLAs via decoupled attention and an auxiliary prediction task, but the abstract leaves the actual performance gains unquantified.

read the letter

The main thing to know is that this paper describes a practical extension for vision-language-action models that lets them take in multiple physical signals at once instead of one at a time. The architecture splits each sensor into its own stream, connects them with joint cross-modal self-attention, freezes the base VLA in the first training stage, and adds a side task that predicts future physical values. That combination is new relative to the single-signal work cited in the abstract, and the two-stage freeze plus auxiliary task look like sensible engineering choices for keeping a pretrained model stable while new inputs come in. The real-world robot experiments add some relevance for manipulation tasks. The paper does a clean job of targeting the risks of instability and non-complementarity that come with heterogeneous signals. The soft spots sit in the results. The abstract states synergistic gains from combining tactile and torque, yet supplies no numbers, baselines, error bars, or task details, so the central claim stays plausible but uncheckable from what is shown. If the full paper has solid ablations and comparisons to simpler fusion methods, that would strengthen it; without them the evaluation feels thin. The assumption that the signals will integrate cleanly without degradation is addressed in the design, but it still needs direct evidence. This is for robotics people already working with VLAs who want concrete patterns for adding sensors. A reader focused on embodied multi-modal learning would pick up usable ideas from the streams and training scheme. It is concrete and timely enough to deserve a serious referee, even if the review will likely press for full metrics and controls. I would send it out for review with those requests rather than desk reject.

Referee Report

2 major / 0 minor

Summary. The paper proposes MoSS, a modular sensory stream framework for adapting pretrained Vision-Language-Action (VLA) models to incorporate heterogeneous physical signals such as tactile and torque feedback. It introduces decoupled modality streams integrated via joint cross-modal self-attention, a two-stage training scheme that freezes pretrained VLA parameters initially, and an auxiliary task predicting future physical signals to capture contact dynamics. The central claim is that this enables stable multi-signal integration and yields synergistic performance gains over single-signal or vision-only baselines, as shown in extensive real-world robot experiments.

Significance. If validated by detailed quantitative results, the work would be significant for embodied robotics and multi-modal learning, as it provides a practical modular approach to extending VLAs beyond vision to diverse physical feedback. This could improve robustness in contact-rich tasks where single modalities are insufficient, addressing a clear gap in current VLA literature.

major comments (2)

Abstract: The claim that MoSS 'integrating multiple signals to achieve synergistic performance gains' is presented without any quantitative metrics, success rates, baselines, error bars, or task-specific results. This is load-bearing for the central contribution, as the soundness of the synergistic-gains assertion cannot be assessed from the provided description alone.
The weakest assumption (heterogeneous signals are complementary and integrable without degradation via two-stage training plus auxiliary prediction) is stated but not supported by ablations or comparisons in the abstract; if the full results section lacks controls showing no performance drop when adding modalities, the stability claim remains unverified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that the abstract would benefit from greater specificity regarding quantitative results and have revised it accordingly. Below we respond point-by-point to the major comments.

read point-by-point responses

Referee: Abstract: The claim that MoSS 'integrating multiple signals to achieve synergistic performance gains' is presented without any quantitative metrics, success rates, baselines, error bars, or task-specific results. This is load-bearing for the central contribution, as the soundness of the synergistic-gains assertion cannot be assessed from the provided description alone.

Authors: We agree that the original abstract lacked specific quantitative support for the synergistic-gains claim. In the revised manuscript we have updated the abstract to include key metrics from our real-world experiments, such as average success rates across contact-rich tasks (with standard deviations from multiple trials), direct comparisons to vision-only and single-signal baselines, and the magnitude of improvement when combining tactile and torque signals. These additions make the central claim verifiable from the abstract while preserving its length and readability. revision: yes
Referee: The weakest assumption (heterogeneous signals are complementary and integrable without degradation via two-stage training plus auxiliary prediction) is stated but not supported by ablations or comparisons in the abstract; if the full results section lacks controls showing no performance drop when adding modalities, the stability claim remains unverified.

Authors: The full results section already contains the requested controls: we report ablations comparing single-modality, dual-modality, and vision-only configurations, showing that the two-stage training plus auxiliary future-signal prediction yields synergistic gains without any performance drop upon adding modalities. These experiments are quantified with success rates, failure-mode analysis, and statistical significance across multiple real-world tasks. To address the abstract-specific concern we have added a concise clause referencing the stability of integration. We therefore do not believe the stability claim is unverified in the manuscript, but the revision improves immediate accessibility. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines MoSS as a new modular architecture with decoupled modality streams, joint cross-modal self-attention, a two-stage training scheme that freezes pretrained VLA weights, and an auxiliary future-signal prediction task. These elements are introduced as design choices motivated by the problem of heterogeneous signal integration and are validated through independent real-world robot experiments on external tasks. No equation or claim reduces by construction to a fitted parameter, self-citation chain, or renamed input; the derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard machine-learning assumptions about modality integration and training stability; no free parameters or invented physical entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Pretrained VLA parameters remain effective when frozen during initial integration of new sensory modalities.
Invoked to justify the two-stage training scheme described in the abstract.

pith-pipeline@v0.9.0 · 5485 in / 1112 out tokens · 39912 ms · 2026-05-08T08:08:46.314797+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

RLDX-1 Technical Report
cs.RO 2026-05 unverdicted novelty 4.0

RLDX-1 achieves 86.8% success on complex ALLEX humanoid manipulation tasks where prior VLAs reach only around 40%.
RLDX-1 Technical Report
cs.RO 2026-05 unverdicted novelty 4.0

RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.

Reference graph

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tactile sensor on the gripper, and for the torque modality, we use the joint torque measurements provided by the robot. Avg. denotes averaged success rates over entire tasks.Boldindicates best results. Unstack Cup Board Erase Plug Insertion Method Tactile Torque Small BigPnP EggLow Middle High Yellow White BlackAvg. GR00T N1.5 (Bjorck et al., 2025a)✗ ✗0.0...

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