arxiv: 2605.05945 · v4 · submitted 2026-05-07 · 💻 cs.CV · cs.CL

Recognition: no theorem link

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

Senthil Palanisamy , Abhishek Anand , Satpal Singh Rathor , Pratyush Patnaik , Shubhanshu Khatana

Authors on Pith no claims yet

Pith reviewed 2026-05-15 07:05 UTC · model grok-4.3

classification 💻 cs.CV cs.CL

keywords egocentric videolong-horizon datasmartphone datasetvision language actionrobot learningpose trackingdata collectionpersistent state

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The pith

Smartphones enable collection of 200 hours of long-horizon egocentric data for robot training

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

The paper presents MobileEgo Anywhere as a way to gather extended egocentric video sequences using everyday smartphones instead of specialized robotics rigs. Existing datasets for vision-language-action models typically last only minutes and miss the extended temporal patterns needed for realistic tasks. The work releases 200 hours of such data with continuous state tracking, plus an open app and conversion tools, to let anyone record and prepare training examples. This approach removes the equipment cost and expertise barrier that has kept long-horizon egocentric data scarce.

Core claim

MobileEgo Anywhere uses the built-in sensors of standard smartphones to deliver high-fidelity, long-term camera pose tracking, allowing users to record hour-plus egocentric trajectories anywhere. The authors contribute a 200-hour dataset of diverse long-form egocentric recordings that include persistent state tracking, an open-source mobile application for data capture, and a processing pipeline that turns raw phone footage into standardized formats ready for vision-language-action model training.

What carries the argument

Smartphone sensor fusion for continuous camera pose estimation that maintains persistent state across long recordings without external hardware

If this is right

Any user with a modern phone can now record hour-scale egocentric trajectories for robot learning.
Vision-language-action models can train on continuous sequences that span many minutes to hours rather than short clips.
Data collection becomes feasible outside controlled labs and across many different real-world settings.
The open processing pipeline turns raw mobile video and sensor logs into uniform training formats for foundation models.

Where Pith is reading between the lines

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

Widespread phone-based collection could rapidly expand dataset diversity across global environments and cultures.
Persistent state labels from phone tracking might support new benchmarks on object permanence and long-term memory in robotic policies.
The same phone infrastructure could be extended to include additional onboard sensors for richer multimodal egocentric streams.

Load-bearing premise

Ordinary smartphone cameras and sensors can track pose accurately for full hours without accumulating errors that break the data for training.

What would settle it

Quantitative comparison of phone-derived poses against ground-truth motion capture over multiple one-hour sessions, showing whether drift exceeds thresholds that would make the trajectories unusable for long-horizon policy learning.

Figures

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

**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: 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: 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: 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: 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: Distribution of estimated joint flexion angles for view at source ↗

**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: 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: Wrist velocity and acceleration distributions for left and view at source ↗

**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: Wrist velocity and acceleration distributions for left and [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗

**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: 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: Distribution of estimated joint flexion angles for [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

**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

The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, existing datasets are often limited by short episode durations, typically spanning only a few minutes, which fails to capture the long horizon temporal dependencies necessary for complex robotic task execution. To bridge this gap, we present MobileEgo Anywhere, a framework designed to facilitate the collection of robust, hour plus egocentric trajectories using commodity mobile hardware. We leverage the ubiquitous sensor suites of modern smartphones to provide high fidelity, long term camera pose tracking, effectively removing the high hardware barriers associated with traditional robotics data collection. Our contributions are three fold: (1) we release a novel dataset comprising 200 hours of diverse, long form egocentric data with persistent state tracking; (2) we open source a mobile application that enables any user to record egocentric data, and (3) we provide a comprehensive processing pipeline to convert raw mobile captures into standardized, training ready formats for Vision Language Action model and foundation model research. By democratizing the data collection process, this work enables the massive scale acquisition of long horizon data across varied global environments, accelerating the development of generalizable robotic policies.

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

MobileEgo Anywhere releases useful open tools and a 200-hour long egocentric dataset on phones, but needs tracking accuracy metrics to back up its claims.

read the letter

Here's the quick take on this one: they have put together an open framework for collecting long egocentric videos using just smartphones, released 200 hours of such data with persistent tracking, and provided the app and pipeline to make it usable for VLA research. What stands out as new is the emphasis on hour-plus trajectories collected on commodity hardware. Most egocentric datasets in robotics are short clips, so this tries to fill that gap by making long-form capture easy and accessible to anyone with a phone. The open-sourcing of the mobile app and the processing pipeline to standardize the data is a practical step that could encourage broader data collection in varied settings. They do a decent job highlighting the motivation around long-horizon dependencies for robotic tasks and how traditional setups have high barriers. The three-fold contribution is clearly laid out in the abstract. The main soft spot is the missing validation for the core technical claim. The paper asserts that modern smartphones deliver high fidelity long term camera pose tracking, but from the description, there are no numbers on tracking accuracy, no error metrics against references, and no analysis of drift over time or in difficult environments. That makes the soundness lower than it could be, because if the poses aren't reliable, the dataset won't support the intended use for training models on extended sequences. The stress-test note captures this accurately; without those details, it's difficult to assess how well it actually works. This paper is aimed at researchers building Vision Language Action models who are looking for larger scale, longer duration egocentric data. A reader interested in data collection infrastructure or scaling datasets would get value from the tools and the release itself. It deserves a serious referee. The infrastructure release is concrete enough and addresses a real need, so peer review makes sense to get feedback on the validation and to potentially strengthen the claims with added experiments.

Referee Report

2 major / 1 minor

Summary. The paper claims to introduce an open infrastructure called MobileEgo Anywhere for collecting long-horizon egocentric data using commodity mobile hardware. Key contributions include releasing a 200-hour dataset of diverse long-form egocentric trajectories with persistent state tracking, open-sourcing a mobile app for data recording, and providing a processing pipeline to convert raw captures into standardized formats for Vision Language Action (VLA) models and foundation model research. The work emphasizes leveraging ubiquitous smartphone sensors to achieve high-fidelity, long-term camera pose tracking, thereby lowering barriers to large-scale data collection.

Significance. If the results hold, this infrastructure could have substantial impact by enabling researchers worldwide to collect massive amounts of long-horizon egocentric data in varied real-world environments without specialized hardware. This would directly address the limitation of short episode durations in existing datasets and support the development of more capable VLA models for complex robotic tasks.

major comments (2)

[Abstract] The assertion of 'high fidelity, long term camera pose tracking' using smartphone sensors lacks any supporting quantitative evidence, such as absolute trajectory error (ATE), relative pose error (RPE), or accumulated drift metrics over extended trajectories. Without these, it is unclear whether the released dataset maintains the accuracy required for persistent state tracking in long-horizon applications.
[Abstract] The manuscript provides no ablation studies or error analysis on the tracking performance in challenging conditions like low-texture environments or dynamic scenes, which are essential to substantiate the claim that this approach effectively removes high hardware barriers.

minor comments (1)

The abstract mentions 'diverse' data but does not specify the range of environments or activities covered, which would help readers assess the dataset's utility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that strengthening the quantitative support for the tracking claims will improve the paper and will revise accordingly. Below we respond point by point to the major comments.

read point-by-point responses

Referee: [Abstract] The assertion of 'high fidelity, long term camera pose tracking' using smartphone sensors lacks any supporting quantitative evidence, such as absolute trajectory error (ATE), relative pose error (RPE), or accumulated drift metrics over extended trajectories. Without these, it is unclear whether the released dataset maintains the accuracy required for persistent state tracking in long-horizon applications.

Authors: We agree that explicit quantitative metrics would strengthen the claim. The MobileEgo Anywhere app uses the smartphone's native visual-inertial odometry, which is engineered for extended sessions. In the revised manuscript we will add a dedicated evaluation subsection that reports ATE and RPE on a representative subset of trajectories (computed via loop-closure consistency and cross-sequence alignment where external references are available) together with drift statistics over multi-hour captures. These numbers will be included in both the abstract and main text. revision: yes
Referee: [Abstract] The manuscript provides no ablation studies or error analysis on the tracking performance in challenging conditions like low-texture environments or dynamic scenes, which are essential to substantiate the claim that this approach effectively removes high hardware barriers.

Authors: We acknowledge the absence of systematic ablation studies. The original manuscript prioritizes the open infrastructure and dataset release over exhaustive benchmarking of the underlying tracker. In revision we will insert a concise robustness analysis section that (1) qualitatively documents failure modes observed in low-texture and dynamic scenes across the 200-hour corpus and (2) provides limited quantitative checks (e.g., pose consistency before/after loop closure) on representative challenging sequences. This will clarify the practical limits of commodity hardware while preserving the paper's primary focus on data accessibility. revision: yes

Circularity Check

0 steps flagged

No circularity: infrastructure and dataset release paper

full rationale

The paper presents an open-source framework and 200-hour egocentric dataset collected via commodity smartphones. No mathematical derivations, equations, parameter fittings, predictions, or self-citation chains appear in the provided text. The central claims concern data collection infrastructure and release rather than any derived result that reduces to its own inputs by construction. The assumption about smartphone pose tracking fidelity is presented as an enabling premise without any fitted quantities or self-referential logic.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that smartphone sensors suffice for high-fidelity long-term pose tracking; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Smartphone sensor suites can deliver high fidelity long term camera pose tracking
Invoked to justify removal of traditional hardware barriers for hour-plus trajectories.

pith-pipeline@v0.9.0 · 5534 in / 1074 out tokens · 44413 ms · 2026-05-15T07:05:23.667049+00:00 · methodology

Review history (4 revisions) →

discussion (0)

Reference graph

Works this paper leans on

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