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Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

T0 review · 4 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Pre-training a robot policy on over 100,000 hours of auto-captioned real-world trajectories yields steady gains from data and model scale, and the gains survive into unseen real-robot environments.

desk verdict A serious scaling study for robot foundation models, but the 100k-hour headline claim is only validated on a 20k-hour subset and the caption-quality premise is unexamined. read the letter →

arxiv 2607.15330 v2 pith:4B3HM437 submitted 2026-07-16 cs.RO cs.CV

Xiaomi Robotics Team: Jun Guo , Piaopiao Jin , Jason Li , Peiyan Li , Yingyan Li , Futeng Liu , Wanli Peng , Optimus Qin
show 25 more authors
Yifei Su Nan Sun Qiao Sun Runze Suo Heyun Wang Yunhong Wang Rujie Wu Caoyu Xia Lina Zhang Jack Zhao Guoliang Chen Wenlong Chen Xinze He Bin Li Qing Li Zhuorong Li Heng Qu Wenxuan Song Diyun Xiang Yifan Xie Peiran Xu Hangjun Ye Wen Ye Han Zhao Quanyun Zhou
This is my paper · ORCID
classification cs.ROcs.CV
keywords vision-language-actionmodelrobotfoundationpolicyscalingbehaviorauto-labelingUMItrajectoriesflowmatchingcross-embodimenttransfermanipulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper's thesis is that robot manipulation can be scaled like a large language model if the data bottleneck is removed. It removes that bottleneck in two moves: collect trajectories with handheld UMI grippers instead of expensive teleoperated robots, and annotate them automatically with a vision-language model that writes captions describing how the scene's objects and gripper change within each clip. Trained on this corpus, the model learns to produce actions that move the scene from its current state to the language-described target state; a second, smaller stage of cross-embodiment data aligns it to real robots and to the imperative instructions humans actually use. The paper reports monotonic improvements with both data scale and model size, transfer of those gains to out-of-the-box unseen-environment success, state-of-the-art results on four simulation benchmarks, and 75% success on four new tasks fine-tuned with under 10 hours of data each. A sympathetic reader would care because this is concrete evidence that cheap, auto-labeled data, not just curated teleoperation, can drive a generalist robot policy.

What carries the argument

The load-bearing mechanism is the auto-labeling pipeline: a large pre-trained vision-language model (Qwen3.5-27B) captions fixed-length trajectory clips by describing how grippers and interacting objects change state, so each training example becomes 'generate actions that carry the scene from the observed state to the language-described target state.' That conversion makes 100,000 hours labelable in about two weeks and is the paper's answer to the robotics data bottleneck. The architecture is a Mixture-of-Transformers coupling a pre-trained VLM with a diffusion transformer (DiT) that emits action chunks via flow matching; an auxiliary Choice-Policy head on the VLM proposes action candidates

What would settle it

Re-annotate a random sample of the pre-training corpus by hand and compare: (a) measure human agreement with the auto-captions; (b) pre-train identical models on the human-labeled subset and the auto-labeled subset and compare held-out action error and post-training success. If the auto-labeled model does not at least match the human-labeled one, or if caption agreement is low, the stated caption-conditioning mechanism is not the source of the reported gains.

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Extended reading notes

Core claim

The paper's central claim is that a vision-language-action model pre-trained on over 100,000 hours of UMI trajectories—annotated automatically with scene state-transition captions rather than by hand—scales cleanly in both data and model size, and that this scaling carries over to post-trained real-robot performance in unseen environments. Aligned to robot embodiments with about 10,000 hours of cross-embodiment data, the model reports new state-of-the-art results on four simulation benchmarks; fine-tuned on fewer than 10 hours per task, it reaches 75% average success across four new dexterous tasks against 40% for the leading open comparison policy. The authors take this as evidence that sca

Load-bearing premise

The load-bearing premise is that the automatically generated captions describing scene state transitions are accurate enough to supervise action learning; the paper reports no caption-accuracy numbers, no human-agreement check, and no ablation with human-labeled captions, so if those captions are noisy or misaligned with the recorded gripper motions, the scaling and transfer results are not explained by the stated mechanism.

Editorial extensions

If this is right

  • Data volume, not model capacity, is the current bottleneck: at 2B–10B parameters the gains from scaling model size were smaller than from scaling data, so collecting more UMI hours should keep paying off.
  • Out-of-the-box behavior in unseen environments improves monotonically with both pre-training data and model size (26% success with no action pre-training, rising to 75–79% after full pre-training).
  • A pre-trained foundation policy can adapt to dexterous, long-horizon, and deformable-object tasks with under 10 hours of data per task, reaching 75% average success.
  • The composite-unseen split of RoboCasa365 (32.1% vs. 7.9% for the best prior method) suggests the model can recombine learned atomic skills for novel task compositions.
  • The two-stage recipe—state-transition pre-training on robot-free data, then imperative-instruction alignment on cross-embodiment data—transfers across mobile manipulators, dual-arm robots, and static arms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • [Editorial inference] If caption accuracy is the true driver, the natural next experiment is caption-quality control: measuring human agreement with the auto-captions or ablating against a human-labeled subset would tell whether the scaling curves come from the stated supervision mechanism.
  • [Editorial inference] The scaling curves are measured as action-prediction error on a 20k-hour subset at four data fractions; whether the trend extends to the full 100k+ hours and to success-rate metrics is open.
  • [Editorial inference] A testable consequence: if the recipe works as claimed, datasets gathered more cheaply (e.g., crowdsourced handheld-gripper recordings) should improve such policies roughly in proportion to hours added, making captioning cost the main scaling lever.
  • [Editorial inference] The attention-exclusion trick (keeping the DiT from copying VLM action tokens) is supported by a hypothesis—shortcut copying—not an ablation; an ablation at scale would clarify whether it is load-bearing.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents Xiaomi-Robotics-1, a vision-language-action (VLA) foundation model trained in two stages: pre-training on 'over 100k hours' of real-world UMI gripper trajectories annotated by an automatic captioning pipeline that describes scene state transitions, then post-training on about 10k hours of cross-embodiment robot data with human-style imperative instructions. The authors report scaling experiments during pre-training (data and model size), transfer of these scaling gains to out-of-the-box post-training performance in unseen real environments, efficient fine-tuning on four novel tasks with under 10 hours of data per task, and state-of-the-art results on RoboCasa, RoboCasa365, VLABench, and RoboDojo. The central claimed contributions are the scaling law-like behavior of the pre-training recipe and the effectiveness of the two-stage alignment procedure.

Significance. If the results hold, this is one of the largest-scale demonstrations of VLA pre-training on real-world manipulation data, and the two-stage pre-training/post-training recipe with auto-labeled state-transition captions is a plausible path toward generalist robot policies. The paper provides a detailed and internally coherent architecture, explicit scaling experiments, and broad evaluation across four simulated benchmarks plus real-robot trials. The main strengths are the scale of the proposed data collection, the concrete description of the auto-labeling pipeline, and the direct evaluation of pre-training scale transfer to post-training and downstream fine-tuning. However, the headline 100k-hour claim is not exercised in the reported scaling experiments, the auto-labeling mechanism is unvalidated, and the real-robot evaluations use very small trial counts with no confidence intervals. These gaps currently prevent full confidence in the stated conclusions.

major comments (4)
  1. [§3.1, §3.2, §3.4] The scaling experiments are run on 'about 20k hours' of UMI data, not the 'over 100k hours' claimed in the abstract and §1. Specifically, §3.1 pre-trains on 12.5%, 25%, 50%, and 100% of about 20k hours, and §3.2 post-trains checkpoints from those runs. §3.4 (Tabs. 2–5) and §3.3 (Fig. 10) never state which pre-training corpus or checkpoint produced the reported numbers. If the benchmark/real-robot results come from a 100k-hours model, there is no apples-to-apples 20k-vs-100k comparison; if they come from a 20k model, the 'over 100K hours' contribution is not exercised. Please state the pre-training corpus and checkpoint for every downstream result, and add at least one data point at the full 100k-hour scale to support the paper's central scaling claim.
  2. [§2.2.1] The auto-labeling pipeline is load-bearing: the model learns to generate actions conditioned on language annotations of scene state transitions produced by Qwen3.5-27B. No quantitative evaluation of caption quality is reported — no accuracy against human labels, no human agreement, no sample size, and no ablation comparing auto-labels with human labels or with no language conditioning. If the captions are noisy or misaligned with the actual motion, the pre-training signal is corrupted and the stated mechanism does not explain the results. Please add a caption-quality evaluation and a small-scale human-label ablation.
  3. [§3.3, Fig. 8] Real-robot evaluations use very small trial counts. §3.3 states 10 trials per task for downstream fine-tuning; Fig. 8 does not state the number of trials or environments for the out-of-the-box post-training evaluation. With 10 trials, a reported 75% success rate has an approximate standard error of 13.7 percentage points, making comparisons such as 75% vs. 40% far less conclusive than implied. Please report trial counts, confidence intervals, and the number of environments/object instances for all real-robot evaluations, and increase trial counts where feasible.
  4. [§3.1, Fig. 5] The 12.5% and 25% data-scaling runs were terminated early because of overfitting, yet the resulting checkpoints are used in §3.2 for the post-training transfer study. Early stopping is a reasonable practice, but the choice of stopping time affects the measured validation error and the subsequent transfer results. Please clarify how the early-stopped checkpoints were selected, whether the same criterion was applied across all runs, and whether the qualitative conclusions in §3.2 are robust to this selection.
minor comments (5)
  1. [Abstract vs. §1] The RoboCasa365 state-of-the-art number is inconsistent: the abstract and Table 3 report 57.4%, while §1 reports 57.6%. Please correct.
  2. [§3.2.1] The claim that performance 'shows no sign of saturation' is based on only four data points, two of which were early-stopped; the 50%→100% gain is 6 percentage points. This is a reasonable observation but should be phrased more cautiously.
  3. [§2.1] The decision to exclude action-related tokens from the DiT's attention is justified by a shortcut hypothesis, but no ablation is shown. Adding an ablation would strengthen the architectural contribution.
  4. [§3.4, VLABench] The CoT labeling used for VLABench is adopted from ERVLA [61] with a 50% NTP loss probability, but no comparison to training without CoT is provided in this paper. A small ablation would clarify the contribution of this component.
  5. [Figures 5 and 8] In the provided version, the figures contain garbled character rendering (e.g., Fig. 8 axis labels). Please ensure all figures are legible and that numeric values in the figures match the text.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: scaling and benchmark claims are evaluated externally; only minor self-citations present.

full rationale

The central claims—data/model scaling, transfer to post-training, efficient fine-tuning, and SOTA on RoboCasa, RoboCasa365, VLABench, and RoboDojo—are supported by held-out validation action errors, real-robot trials in unseen environments, and official benchmark training/evaluation protocols against external baselines. No equation reduces a fitted parameter to a claimed prediction: Eq. (1) is a standard multi-term training loss, and the scaling curves are empirical learning curves on held-out data, not derivations from the model's own outputs. The paper does rely on self-citations: the vision-language co-training data and asynchronous fine-tuning recipe come from Xiaomi-Robotics-0 [8], and VLABench uses CoT labeling from ERVLA [61] with overlapping authors. These are engineering precedents rather than the load-bearing justification for scaling or benchmark superiority. A separate reporting gap is that the scaling experiments use only 'about 20k hours' while the headline dataset is over 100k hours (Sec. 3.1/3.2), and the benchmark section does not state which pretraining corpus produced the SOTA numbers; this is missing evidence, not a circular reduction. Overall score 1 reflects minor self-citation without circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities. Its contributions are a large dataset, an auto-labeling pipeline, and a training recipe. The free parameters are hyperparameters; the main load-bearing assumptions are the quality of auto-generated captions, representativeness of the 20k-hour scaling subset, and cross-embodiment transfer.

free parameters (4)
  • lambda (NTP loss weight) = 0.1
    Eq. 1; hand-set hyperparameter controlling co-training on vision-language data; no sweep reported.
  • Data sampling ratio (vision-language : UMI) = 1:9 pre-training; 0.5:0.5:0.5:8.5 post-training
    Sections 2.2.1 and 2.2.2; hand-chosen mixture, no ablation reported.
  • Flow-matching timestep distribution = Beta(1.5,1), tau=(1-u)*0.999
    Section 2.1; following prior work [4], not fit to the current data.
  • Euler integration steps and step size = 5 steps, dt=0.2
    Section 2.1; inference schedule, chosen without reported justification or sweep.
assumptions (5)
  • domain assumption Auto-labeled state-transition captions from Qwen3.5-27B are accurate and sufficiently detailed to condition action generation.
    Section 2.2.1; no quality evaluation or human validation of captions is reported; the entire pre-training objective relies on them.
  • domain assumption Actions learned from handheld UMI trajectories transfer to robot embodiments after post-training on ~10k hours of robot data.
    Sections 2.2.2 and 3.2; tested only on four tasks with unseen environments, not on unseen task categories.
  • domain assumption The 20k-hour subset used in scaling experiments is representative of the full 100k-hour corpus.
    Section 3.1; no analysis shows scaling trends continue past 20k hours, yet the abstract claims 100k hours.
  • standard math Flow-matching objective plus VLM choice-policy auxiliary loss learns generalizable action chunks.
    Section 2.1; standard flow-matching/regression setup borrowed from prior work [4,59], not derived here.
  • domain assumption Baseline numbers from other papers are directly comparable without running their code in the same environment.
    Section 3.4; SOTA claims rely on self-reported baseline numbers from prior publications.

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Cite this review

Pith. "Pith review of Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories." pith.science (2026). https://pith.science/paper/4B3HM437

@misc{pith2026260715330,
  author       = {Pith},
  title        = {Pith review of: Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4B3HM437}},
  note         = {Machine review of arXiv:2607.15330}
}
read the original abstract

We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural languages describing scene state transitions, providing rich and precise conditioning for action learning. During post-training, we aim to align these capabilities with robot embodiments and imperative instructions that humans naturally use to prompt robots. Extensive experiments demonstrate strong scaling behavior. Xiaomi-Robotics-1 consistently improves with increased data scales and model sizes during pre-training. This scaling behavior directly transfers to post-training, where a stronger pre-training model yields better out-of-the-box real-robot performance in unseen environments. Furthermore, Xiaomi-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency. Across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods. Notably, it establishes a new state-of-the-art with a 57.4% success rate on RoboCasa365, surpassing the previous best of 46.6%. Furthermore, it achieves an average score of 20.07 on RoboDojo, significantly outperforming the prior state-of-the-art (13.07). Code and model checkpoints will be released. Project page: https://robotics.xiaomi.com/xiaomi-robotics-1.html

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Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.