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arxiv: 2504.18662 · v3 · submitted 2025-04-25 · 💻 cs.RO · cs.AI

M2R2: MultiModal Robotic Representation for Temporal Action Segmentation

Daniel Sliwowski , Dongheui Lee This is my paper

Pith reviewed 2026-05-22 17:35 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords temporal action segmentationmultimodal fusionrobotic perceptionproprioceptive sensingexteroceptive sensingfeature reuseaction boundary detection
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The pith

M2R2 fuses proprioceptive and exteroceptive data into a reusable multimodal feature extractor that raises performance on robotic temporal action segmentation.

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

The paper proposes M2R2 as a multimodal feature extractor built specifically for temporal action segmentation in robotics. It integrates signals from both proprioceptive sensors, such as joint positions and forces, and exteroceptive sensors like cameras, while adding a training procedure that lets the same extracted features serve multiple downstream segmentation models. This combination is shown to exceed prior results on the REASSEMBLE, (Im)PerfectPour, and JIGSAWS datasets. The work also includes an ablation study that isolates how each sensor type contributes to boundary detection. A sympathetic reader would care because better action segmentation directly improves skill learning and error recovery in physical robots that must operate with partial visibility or noisy single-modality input.

Core claim

We address these challenges by proposing M2R2, a multimodal feature extractor tailored for TAS, which combines information from both proprioceptive and exteroceptive sensors. We introduce a novel training strategy that enables the reuse of learned features across multiple TAS models. Our method sets a new state-of-the-art performance on three robotic datasets REASSEMBLE, (Im)PerfectPour, and JIGSAWS. Additionally, we conduct an extensive ablation study to evaluate the contribution of different modalities in robotic TAS tasks.

What carries the argument

The M2R2 multimodal feature extractor, which fuses proprioceptive and exteroceptive inputs through a training strategy designed to support feature reuse across separate TAS models.

If this is right

  • Robotic systems can segment actions more reliably when both internal state and external visual cues are available to the same feature extractor.
  • Features learned once can be plugged into different temporal action segmentation heads without retraining the extractor from scratch.
  • Performance gains appear in settings where objects are sometimes occluded or lighting varies, because proprioception supplies information vision alone misses.
  • Ablation results indicate that neither sensor type alone matches the combined representation on the evaluated datasets.

Where Pith is reading between the lines

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

  • The reuse training could reduce the cost of deploying the same representation on new robot platforms that share similar sensor suites.
  • Similar fusion-plus-reuse patterns might transfer to other sequential robotic tasks such as long-horizon planning or anomaly detection during execution.
  • If the method scales, it suggests that future robotic datasets should record both proprioceptive and visual streams as a standard practice rather than vision-only recordings.

Load-bearing premise

The fusion method and reuse training will continue to improve results when applied to new robotic tasks and datasets beyond the three tested here, and the ablation runs isolate modality effects without hidden post-processing.

What would settle it

A fourth robotic temporal action segmentation dataset on which M2R2 fails to exceed the prior best accuracy, or an ablation rerun in which removing one modality leaves performance unchanged.

Figures

Figures reproduced from arXiv: 2504.18662 by Daniel Sliwowski, Dongheui Lee.

Figure 1
Figure 1. Figure 1: The overview of mutimodal temporal action segmen [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: To summarize, our main contributions are as follows: 1) A deep-learning-based multimodal feature extractor for robotic temporal action segmentation in contact-rich manipulation tasks. 2) A pretraining strategy for learning multimodal features for robotic temporal action segmentation. 3) An extensive evaluation of the influence of different sensor modalities on robotic temporal action segmen￾tation performa… view at source ↗
Figure 2
Figure 2. Figure 2: M2R2 Model Architecture. To compute the multimodal feature at time instant ti , we first process each modality separately to obtain image features Ii , audio features Ai , and proprioceptive features {S s i } Ns s=1 , which are later fused using a Transformer encoder layer followed by an MLP. To obtain Ii , we use the ActionCLIP image encoder [15]. For Ai , we extract features using the Audio Spectrogram T… view at source ↗
Figure 3
Figure 3. Figure 3: Temporal Fusion and Pretraining. Given a window [ib − p,ie + p), we sample Nw frames and extract features using our M2R2 feature extractor. A Temporal Fusion Trans￾former refines these features into Xb, which we average to obtain the window representation Ew. To learn action order, we minimize the distance between Ew and a textual embed￾ding Es generated from action labels by using a template. To enhance b… view at source ↗
Figure 4
Figure 4. Figure 4: E. Qualitative Evaluation [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: Quantitative evaluation of different baseline TAS models. AWE [1] performs poor in sections of highly nonlinear [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Coarse level prediction compared to fine-grain level [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Exmaple predictions for different modality combina [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Temporal action segmentation (TAS) has long been a key area of research in both robotics and computer vision. In robotics, algorithms have primarily focused on leveraging proprioceptive information to determine skill boundaries, with recent approaches in surgical robotics incorporating vision. In contrast, computer vision typically relies on exteroceptive sensors, such as cameras. Existing multimodal TAS models in robotics integrate feature fusion within the model, making it difficult to reuse learned features across different models. Meanwhile, pretrained vision-only feature extractors commonly used in computer vision struggle in scenarios with limited object visibility. In this work, we address these challenges by proposing M2R2, a multimodal feature extractor tailored for TAS, which combines information from both proprioceptive and exteroceptive sensors. We introduce a novel training strategy that enables the reuse of learned features across multiple TAS models. Our method sets a new state-of-the-art performance on three robotic datasets REASSEMBLE, (Im)PerfectPour, and JIGSAWS. Additionally, we conduct an extensive ablation study to evaluate the contribution of different modalities in robotic TAS tasks.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces M2R2, a multimodal feature extractor for temporal action segmentation (TAS) in robotics that fuses proprioceptive and exteroceptive (vision) sensor data. It proposes a novel training strategy to enable reuse of learned features across different TAS models. The central claims are that this yields new state-of-the-art performance on the REASSEMBLE, (Im)PerfectPour, and JIGSAWS datasets, supported by an ablation study isolating modality contributions.

Significance. If the performance gains hold under rigorous validation, the work would advance multimodal TAS in robotics by addressing feature-reuse limitations in existing fusion models and visibility issues in vision-only extractors. The ablation study on modality contributions is a positive element that could inform future sensor-selection decisions in robotic skill segmentation.

major comments (2)
  1. [Results] Results section: The SOTA claims on REASSEMBLE, (Im)PerfectPour, and JIGSAWS rest on single-run point estimates for metrics such as edit score and F1@50. No standard deviations, multiple random seeds, or statistical significance tests are reported. On small, noisy robotic TAS datasets this leaves open the possibility that observed gains arise from seed, split, or hyperparameter effects rather than the multimodal fusion and feature-reuse strategy.
  2. [Ablation study] Ablation study description: The claim that the study 'sufficiently isolates modality contributions' is not supported by details on whether post-hoc adjustments or selective reporting were used; without explicit controls for confounding factors (e.g., total parameter count or training schedule differences), the ablation cannot reliably attribute performance differences to individual modalities.
minor comments (2)
  1. [Abstract] Abstract: The SOTA claim is stated without any numerical values, baseline comparisons, or metric names, which reduces immediate readability and makes it difficult for readers to gauge the magnitude of the reported advance.
  2. [Method] Notation: The distinction between the proposed multimodal fusion mechanism and prior feature-fusion approaches could be clarified with a short equation or diagram in the method section to highlight the reuse-enabled training strategy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help improve the clarity and rigor of our work. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Results] Results section: The SOTA claims on REASSEMBLE, (Im)PerfectPour, and JIGSAWS rest on single-run point estimates for metrics such as edit score and F1@50. No standard deviations, multiple random seeds, or statistical significance tests are reported. On small, noisy robotic TAS datasets this leaves open the possibility that observed gains arise from seed, split, or hyperparameter effects rather than the multimodal fusion and feature-reuse strategy.

    Authors: We agree that single-run point estimates on small robotic datasets leave room for variability due to random seeds or splits. Our reported results followed the single-run evaluation protocols used in prior work on these datasets. To address this concern, we will rerun the key experiments with multiple random seeds, report means and standard deviations, and include statistical significance tests in the revised manuscript. revision: yes

  2. Referee: [Ablation study] Ablation study description: The claim that the study 'sufficiently isolates modality contributions' is not supported by details on whether post-hoc adjustments or selective reporting were used; without explicit controls for confounding factors (e.g., total parameter count or training schedule differences), the ablation cannot reliably attribute performance differences to individual modalities.

    Authors: The ablation experiments varied input modalities while holding the model architecture, optimizer, learning rate schedule, and number of training epochs fixed across conditions. No post-hoc adjustments or selective reporting were performed. We will revise the manuscript to explicitly document these controls, including parameter counts for each ablation variant and the precise training schedules, to make the isolation of modality effects more transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical multimodal TAS model

full rationale

The paper is an empirical ML contribution proposing M2R2 as a multimodal feature extractor with a novel training strategy for feature reuse in temporal action segmentation. It evaluates on external robotic datasets (REASSEMBLE, (Im)PerfectPour, JIGSAWS) and reports SOTA via ablation studies. No equations, derivations, or first-principles predictions appear in the provided text that reduce to fitted parameters or self-referential inputs by construction. Central claims rest on experimental benchmarks rather than self-citation chains or definitional loops. Any incidental self-citations would not be load-bearing for the reported results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on empirical performance gains from the proposed architecture and training procedure, with standard assumptions about dataset representativeness and the value of multimodal fusion drawn from prior robotics and vision work.

free parameters (1)
  • modality fusion parameters
    Learned weights or mechanisms for combining proprioceptive and exteroceptive features within the extractor.
axioms (1)
  • domain assumption Multimodal sensor fusion improves TAS accuracy over single-modality baselines in robotic settings
    Invoked implicitly when claiming benefits of the combined approach in the abstract.

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Reference graph

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