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arxiv: 2605.05945 · v6 · pith:GKT5XO5Ynew · submitted 2026-05-07 · 💻 cs.CV · cs.CL

MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

Senthil Palanisamy , Abhishek Anand , Satpal Singh Rathore , Pratyush Patnaik , Shubhanshu Khatana , Ekaksh Janweja This is my paper

Pith reviewed 2026-05-20 23:33 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords egocentric trajectoriessmartphone sensorslong-horizon datapose trackingvision language actiondataset infrastructuremobile hardware
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The pith

Smartphone sensors enable collection of hour-plus egocentric trajectories with high fidelity pose tracking for robotic model training.

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

The paper establishes that standard smartphones can be used to record long egocentric videos lasting over an hour while maintaining accurate camera pose information through their built-in sensors. This addresses the limitation of existing datasets that are usually only a few minutes long, which is insufficient for training models on extended tasks. A sympathetic reader would care because longer data sequences could allow vision-language-action models to learn more complex behaviors that unfold over time. The work releases a 200-hour dataset along with open-source processing tools to make such data collection widely accessible.

Core claim

MobileEgo Anywhere provides an open framework for collecting robust, hour-plus egocentric trajectories on commodity mobile hardware by leveraging smartphone sensor suites for high fidelity long term camera pose tracking. The authors release a novel dataset of 200 hours of diverse long form egocentric data with persistent state tracking, the STERA video processing infrastructure, and a pipeline to convert raw captures into training ready formats for VLA and foundation model research.

What carries the argument

The STERA infrastructure for processing mobile sensor data into persistent egocentric pose estimates and standardized training data.

If this is right

  • Researchers gain access to hour-scale egocentric episodes instead of minute-scale ones for training.
  • Data collection extends to everyday global environments without dedicated robotics setups.
  • VLA models can incorporate persistent state tracking across extended temporal horizons.
  • The open pipeline allows any user to convert mobile recordings into standardized training formats.

Where Pith is reading between the lines

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

  • Widespread use of the tools could produce community-scale datasets far exceeding current sizes.
  • Integration with everyday phone usage might enable continuous data gathering from real activities.
  • If tracking remains stable, the approach could support online adaptation of models from live phone streams.

Load-bearing premise

The sensor data from modern smartphones can maintain high-fidelity camera pose tracking over hour-long periods without significant drift or the need for specialized corrections.

What would settle it

Direct comparison of pose estimates from the smartphone method against ground-truth motion capture systems over multiple hour-long sequences would reveal whether tracking accuracy holds for downstream model training.

Figures

Figures reproduced from arXiv: 2605.05945 by Abhishek Anand, Ekaksh Janweja, Pratyush Patnaik, Satpal Singh Rathore, Senthil Palanisamy, Shubhanshu Khatana.

Figure 1
Figure 1. Figure 1: MobileEgo Anywhere turns any modern iPhone into a long horizon egocentric capture device. (a) Contributors record hands free using a helmet mounted phone. (b) Episodes are substantially longer than those in prior datasets. (c) ARKit based visual-inertial fusion yields continuous 6 DoF pose, which can later be used to generate 3D hand trajectories in a consistent world frame across the full session. human d… view at source ↗
Figure 2
Figure 2. Figure 2: Overall process The data collection process utilizes an iPhone as the primary sensing platform as illustrated in Fig. 1a. The overall process 1Project resources: (1) Mobile App: Will be released after peer review to maintain anonymity; (2) Python Processing Suite: fpvlabs.ai/python-package; (3) Data Download: fpvlabs.ai/data; (4) Data Visualization: fpvlabs.ai/ dataset-visualization; (5) App Code: fpvlabs.… view at source ↗
Figure 2
Figure 2. Figure 2: Overall data flow: raw mobile capture (RGB-D, IMU, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Task diversity III-B3. Hierarchical Task Instructions Long horizon sessions spanning 20-60 minutes contain dozens of atomic labels that belong to distinct sub-tasks as shown in 3, which highlights the action diversity spanning 45K different action categories. To expose this structure, the atomic span captions from the previous stage are organized into a three level instruction tree: a session level goal, s… view at source ↗
Figure 4
Figure 4. Figure 4: Overall data flow: raw mobile capture (RGB-D, IMU, [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of estimated joint flexion angles for view at source ↗
Figure 4
Figure 4. Figure 4: Per-bone coefficient of variation (CV) of bone length across all valid frames, pooled over 98 sessions. Each bone of [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Task diversity across 354 sessions and 16 contributors. Atomic action labels span a long-tail vocabulary covering [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Wrist velocity and acceleration distributions for left and view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of estimated joint flexion angles for each finger, pooled over 98 sessions. Shaded regions indicate published [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Wrist velocity and acceleration distributions for left and [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Hierarchical decomposition of a 36-minute cooking session (217 atomic spans). A single session goal decomposes view at source ↗
Figure 6
Figure 6. Figure 6: Wrist velocity and acceleration distributions for left and right hands, pooled over 98 sessions. Shaded bands indicate [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of estimated joint flexion angles for [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Hierarchical instruction labeling across 354 sessions (45,415 atomic spans). (a) Temporal scale separation: each level of view at source ↗
read the original abstract

Vision-language-action (VLA) models have driven demand for large-scale egocentric datasets, yet the hardware and infrastructure to collect long-horizon data remain inaccessible. Datasets today typically have episodes only a few minutes long, which fails to capture the long-horizon temporal dependencies that complex robotic task execution requires. We present MobileEgo Anywhere, a framework for collecting hour-plus egocentric trajectories on commodity mobile hardware that uses modern smartphone sensors for long-term pose tracking without the hardware barriers of traditional robotics data collection. We release three components: (1) STERA, an open-source video-processing pipeline that converts raw mobile captures into standardized, training-ready formats for VLA and foundation-model research; (2) a free mobile app that lets any user record egocentric activity; and (3) a 200-hour dataset of diverse, long-form egocentric data with persistent state tracking across 584 sessions. We further show this data is a usable training signal:mid-training a VLA on it lowers held-out action-prediction error.

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 presents MobileEgo Anywhere, a framework for collecting robust hour-plus egocentric trajectories on commodity smartphones. It claims to deliver high-fidelity long-term camera pose tracking via the STERA pipeline, releases a 200-hour dataset with persistent state tracking, and open-sources the full video processing infrastructure plus a conversion pipeline to produce training-ready formats for Vision-Language-Action models.

Significance. If the pose-tracking fidelity claim holds, the work would meaningfully lower barriers to large-scale long-horizon egocentric data collection, directly addressing a bottleneck for VLA and foundation-model research. The explicit release of both the 200-hour dataset and the complete open-source STERA infrastructure are concrete strengths that support reproducibility and community use.

major comments (2)
  1. [Abstract] Abstract: the assertion of 'high fidelity, long term camera pose tracking' that 'effectively remov[es] the high hardware barriers' is not accompanied by any quantitative validation (ATE, RPE, drift rate, or comparison to ground-truth systems). This metric gap is load-bearing for the central claim that commodity-phone VIO suffices for downstream VLA training over hour-scale sequences.
  2. [Contributions] Contributions (1)–(3): the description of the STERA pipeline and 'persistent state tracking' does not specify the mechanisms (loop closure, global bundle adjustment, or post-processing corrections) used to bound drift on trajectories longer than a few minutes, leaving the stability claim unverified.
minor comments (2)
  1. The dataset access URL should be accompanied by explicit licensing and usage terms to facilitate adoption.
  2. [Abstract] Minor typographical inconsistency: 'hour plus' in the abstract would read more cleanly as 'hour-plus'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for identifying areas where the manuscript's claims can be better supported. We address each major comment below and have revised the manuscript to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion of 'high fidelity, long term camera pose tracking' that 'effectively remov[es] the high hardware barriers' is not accompanied by any quantitative validation (ATE, RPE, drift rate, or comparison to ground-truth systems). This metric gap is load-bearing for the central claim that commodity-phone VIO suffices for downstream VLA training over hour-scale sequences.

    Authors: We agree that the abstract makes a strong claim without accompanying quantitative metrics such as ATE or RPE. The manuscript's primary focus is the release of the open infrastructure and 200-hour dataset rather than a new VIO algorithm benchmark. In the revised version we have added a dedicated evaluation subsection that reports drift rates derived from loop-closure consistency on long sequences and indirect validation through successful use in downstream VLA training tasks. We also explicitly discuss the practical difficulty of obtaining external ground truth on commodity phones in unconstrained environments. revision: yes

  2. Referee: [Contributions] Contributions (1)–(3): the description of the STERA pipeline and 'persistent state tracking' does not specify the mechanisms (loop closure, global bundle adjustment, or post-processing corrections) used to bound drift on trajectories longer than a few minutes, leaving the stability claim unverified.

    Authors: We accept that the original text did not sufficiently detail the drift-bounding mechanisms. The revised manuscript now expands the STERA pipeline description to explicitly state the use of loop-closure detection, global bundle adjustment, and post-processing corrections that maintain persistent state across hour-scale trajectories. revision: yes

Circularity Check

0 steps flagged

No derivations or self-referential predictions; infrastructure release stands independently

full rationale

The paper describes an open-source framework (STERA) and dataset release for egocentric data collection on commodity phones. No equations, fitted parameters, predictions, or first-principles derivations appear in the abstract or described contributions. Claims rest on the practical utility of the released code and 200-hour dataset rather than any mathematical reduction to prior self-citations or inputs. This matches the default case of a self-contained infrastructure paper with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on the domain assumption that commodity smartphone sensors suffice for high-fidelity long-term tracking; no free parameters, invented physical entities, or additional ad-hoc axioms are introduced in the abstract.

axioms (1)
  • domain assumption Modern smartphone sensor suites can provide high-fidelity, long-term camera pose tracking.
    Directly invoked when the abstract states that ubiquitous sensor suites remove traditional hardware barriers.

pith-pipeline@v0.9.0 · 5798 in / 1334 out tokens · 41278 ms · 2026-05-20T23:33:36.378977+00:00 · methodology

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