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arxiv: 2503.06669 · v4 · submitted 2025-03-09 · 💻 cs.RO · cs.CV· cs.LG

Recognition: 3 theorem links

· Lean Theorem

AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

AgiBot-World-Contributors , Qingwen Bu , Jisong Cai , Li Chen , Xiuqi Cui , Yan Ding , Siyuan Feng , Shenyuan Gao
show 44 more authors
Xindong He Xuan Hu Xu Huang Shu Jiang Yuxin Jiang Cheng Jing Hongyang Li Jialu Li Chiming Liu Yi Liu Yuxiang Lu Jianlan Luo Ping Luo Yao Mu Yuehan Niu Yixuan Pan Jiangmiao Pang Yu Qiao Guanghui Ren Cheng Ruan Jiaqi Shan Yongjian Shen Chengshi Shi Mingkang Shi Modi Shi Chonghao Sima Jianheng Song Huijie Wang Wenhao Wang Dafeng Wei Chengen Xie Guo Xu Junchi Yan Cunbiao Yang Lei Yang Shukai Yang Maoqing Yao Jia Zeng Chi Zhang Qinglin Zhang Bin Zhao Chengyue Zhao Jiaqi Zhao Jianchao Zhu
Authors on Pith no claims yet

Pith reviewed 2026-05-11 15:02 UTC · model grok-4.3

classification 💻 cs.RO cs.CVcs.LG
keywords robot manipulationlarge-scale datasetembodied AIgeneralist policydexterous taskstrajectory datascaling behavior
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The pith

A dataset of over one million robot trajectories enables policies that improve 30% over Open X-Embodiment in both familiar and new tasks.

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

The paper introduces AgiBot World, a platform with more than one million trajectories across 217 tasks in five scenarios, representing an order-of-magnitude increase over prior robot datasets. It describes a standardized collection pipeline using human-in-the-loop verification to maintain data quality and diversity, and presents the GO-1 generalist policy that uses latent action representations to improve data utilization. Policies pre-trained on this dataset show an average 30% performance gain over those trained on Open X-Embodiment in both in-domain and out-of-distribution settings. GO-1 further achieves over 60% success on complex real-world dexterous and long-horizon tasks, outperforming the prior RDT approach by 32%. The authors open-source the dataset, tools, and models to support broader progress toward scalable embodied intelligence.

Core claim

The authors establish that pre-training on the AgiBot World dataset of over one million trajectories produces policies with 30% higher average performance than those trained on Open X-Embodiment, both in-domain and out-of-distribution. They further show that the GO-1 policy, which leverages latent action representations, exhibits predictable scaling with data volume and reaches over 60% success on complex dexterous and long-horizon tasks while outperforming the prior RDT method by 32%.

What carries the argument

The AgiBot World dataset of over one million trajectories paired with the GO-1 policy that uses latent action representations to maximize data utilization and enable predictable scaling.

Load-bearing premise

That the standardized collection pipeline with human-in-the-loop verification produces data of sufficient quality and diversity to drive the reported 30% gains, predictable scaling behavior, and 60%+ success rates on complex tasks.

What would settle it

Retraining the same policy architectures on an equally large alternative dataset collected without the human-verification step and observing no improvement in success rates or loss of predictable scaling.

read the original abstract

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets. Accelerated by a standardized collection pipeline with human-in-the-loop verification, AgiBot World guarantees high-quality and diverse data distribution. It is extensible from grippers to dexterous hands and visuo-tactile sensors for fine-grained skill acquisition. Building on top of data, we introduce Genie Operator-1 (GO-1), a novel generalist policy that leverages latent action representations to maximize data utilization, demonstrating predictable performance scaling with increased data volume. Policies pre-trained on our dataset achieve an average performance improvement of 30% over those trained on Open X-Embodiment, both in in-domain and out-of-distribution scenarios. GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks, achieving over 60% success rate on complex tasks and outperforming prior RDT approach by 32%. By open-sourcing the dataset, tools, and models, we aim to democratize access to large-scale, high-quality robot data, advancing the pursuit of scalable and general-purpose intelligence.

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

3 major / 2 minor

Summary. The manuscript introduces AgiBot World, a large-scale robot manipulation dataset with over 1 million trajectories across 217 tasks in five scenarios, collected via a standardized pipeline incorporating human-in-the-loop verification. It presents Genie Operator-1 (GO-1), a generalist policy that employs latent action representations to improve data utilization and exhibit predictable scaling with data volume. Key claims include a 30% average performance improvement for policies pre-trained on AgiBot World versus Open X-Embodiment (in both in-domain and OOD settings), GO-1 achieving over 60% success on complex dexterous and long-horizon tasks, and a 32% outperformance over the prior RDT approach. The work open-sources the dataset, tools, and models.

Significance. If the performance deltas can be shown to stem from the dataset's scale, diversity, and collection quality under controlled conditions, this would constitute a meaningful advance in scalable robot learning by supplying an order-of-magnitude larger resource than prior corpora such as Open X-Embodiment. The open-sourcing of data, code, and models, together with the emphasis on extensible hardware (grippers to dexterous hands and visuo-tactile sensors), would facilitate community progress toward generalist embodied policies. The absence of matched experimental controls and quantitative data-quality metrics, however, currently limits the strength of these conclusions.

major comments (3)
  1. Abstract: The claim that policies pre-trained on AgiBot World achieve an average 30% performance improvement over those trained on Open X-Embodiment (both in-domain and OOD) does not state whether the GO-1 architecture, latent-action objective, optimizer schedule, and evaluation task suite were held identical when training the Open X-Embodiment baselines. Without explicit confirmation of matched training and evaluation protocols, the reported lift cannot be unambiguously attributed to dataset scale or the human-in-the-loop pipeline rather than confounding implementation differences.
  2. Dataset collection and experimental sections: The standardized collection pipeline with human-in-the-loop verification is asserted to guarantee high-quality, diverse data, yet no quantitative metrics are supplied (e.g., per-trajectory acceptance rates, inter-annotator agreement, task-coverage entropy, or diversity statistics). These metrics are load-bearing for validating the assumption that the pipeline drives the reported 30% gains and >60% success rates on complex tasks.
  3. Experimental results: Success rates (e.g., >60% on complex tasks) and improvement percentages (30%, 32%) are presented without error bars, number of evaluation trials, statistical significance tests, or data-exclusion criteria. This omission prevents assessment of the reliability and reproducibility of the central performance claims.
minor comments (2)
  1. The acronym RDT appears without expansion on first use; provide the full name and a brief citation to the prior method being compared.
  2. A summary table directly juxtaposing AgiBot World statistics (trajectories, tasks, scenarios, sensor modalities) against Open X-Embodiment and other benchmarks would improve clarity and allow readers to assess the claimed order-of-magnitude scale increase.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We address each major comment in detail below, and we will make revisions to the manuscript to incorporate clarifications and additional information as outlined.

read point-by-point responses

  1. Referee: Abstract: The claim that policies pre-trained on AgiBot World achieve an average 30% performance improvement over those trained on Open X-Embodiment (both in-domain and OOD) does not state whether the GO-1 architecture, latent action objective, optimizer schedule, and evaluation task suite were held identical when training the Open X-Embodiment baselines. Without explicit confirmation of matched training and evaluation protocols, the reported lift cannot be unambiguously attributed to dataset scale or the human-in-the-loop pipeline rather than confounding implementation differences.

    Authors: We confirm that all training and evaluation protocols were held identical across the AgiBot World and Open X-Embodiment pre-training experiments, with the sole difference being the dataset used. The GO-1 architecture, latent action objective, optimizer, and task suite were the same. We will revise the abstract to explicitly state this matched setup, ensuring the performance gains can be attributed to the dataset. revision: yes

  2. Referee: Dataset collection and experimental sections: The standardized collection pipeline with human-in-the-loop verification is asserted to guarantee high-quality, diverse data, yet no quantitative metrics are supplied (e.g., per-trajectory acceptance rates, inter-annotator agreement, task-coverage entropy, or diversity statistics). These metrics are load-bearing for validating the assumption that the pipeline drives the reported 30% gains and >60% success rates on complex tasks.

    Authors: We agree that providing quantitative metrics would better support our claims about data quality. We will add a dedicated subsection in the revised manuscript detailing metrics such as per-trajectory acceptance rates, inter-annotator agreement, task-coverage entropy, and diversity statistics. revision: yes

  3. Referee: Experimental results: Success rates (e.g., >60% on complex tasks) and improvement percentages (30%, 32%) are presented without error bars, number of evaluation trials, statistical significance tests, or data-exclusion criteria. This omission prevents assessment of the reliability and reproducibility of the central performance claims.

    Authors: We acknowledge the importance of statistical rigor in reporting results. We will update the experimental results section to include error bars, the number of evaluation trials, statistical significance tests, and data-exclusion criteria. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's core claims consist of empirical results: 30% average performance lift for policies pre-trained on AgiBot World versus Open X-Embodiment (in-domain and OOD), >60% success on complex tasks, and 32% outperformance versus prior RDT. These are presented as direct experimental comparisons to external datasets and methods rather than any closed mathematical derivation. The mention of 'predictable performance scaling with increased data volume' is framed as an observed experimental outcome from training GO-1 on the new data, not a first-principles equation or scaling law derived from the dataset itself. No self-definitional loops, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided abstract or described structure. The human-in-the-loop pipeline is asserted as a quality guarantee but is not used to derive the performance numbers by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Central claims rest on the domain assumption that human-verified data collection yields high-quality diverse trajectories sufficient for scaling and generalization; GO-1 is introduced as a new model without external independent evidence for its latent representation approach.

axioms (1)
  • domain assumption Human-in-the-loop verification in the standardized collection pipeline guarantees high-quality and diverse data distribution
    Invoked directly in the abstract to support data quality claims.
invented entities (1)
  • Genie Operator-1 (GO-1) no independent evidence
    purpose: Generalist policy that leverages latent action representations to maximize data utilization
    New model introduced in the abstract; no independent evidence such as external benchmarks or formal verification provided.

pith-pipeline@v0.9.0 · 5740 in / 1585 out tokens · 124537 ms · 2026-05-11T15:02:37.774255+00:00 · methodology

discussion (0)

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Forward citations

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