pith. machine review for the scientific record. sign in

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
0
0 comments X

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)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 43 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

    cs.RO 2026-04 conditional novelty 8.0

    Open-H-Embodiment is the largest open multi-embodiment medical robotics dataset, used to train GR00T-H, the first open vision-language-action model that achieves end-to-end suturing completion where prior models fail.

  2. RotVLA: Rotational Latent Action for Vision-Language-Action Model

    cs.RO 2026-05 unverdicted novelty 7.0

    RotVLA models latent actions as continuous SO(n) rotations with triplet-frame supervision and flow-matching to reach 98.2% success on LIBERO and 89.6%/88.5% on RoboTwin2.0 using a 1.7B-parameter model.

  3. ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

    cs.LG 2026-04 unverdicted novelty 7.0

    π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.

  4. HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps

    cs.RO 2026-04 unverdicted novelty 7.0

    HRDexDB is a multi-modal dataset of 1.4K human and robotic dexterous grasps across 100 objects, providing aligned 3D kinematics, high-resolution tactile data, and video streams.

  5. MoRight: Motion Control Done Right

    cs.CV 2026-04 unverdicted novelty 7.0

    MoRight disentangles object and camera motion via canonical-view specification and temporal cross-view attention, while decomposing motion into active user-driven and passive consequence components to learn and apply ...

  6. BiCoord: A Bimanual Manipulation Benchmark towards Long-Horizon Spatial-Temporal Coordination

    cs.RO 2026-04 conditional novelty 7.0

    BiCoord is a new benchmark for long-horizon tightly coordinated bimanual manipulation that includes quantitative metrics and shows existing policies like DP, RDT, Pi0 and OpenVLA-OFT struggle on such tasks.

  7. Towards Generalizable Robotic Manipulation in Dynamic Environments

    cs.CV 2026-03 unverdicted novelty 7.0

    DOMINO dataset and PUMA architecture enable better dynamic robotic manipulation by incorporating motion history, delivering 6.3% higher success rates than prior VLA models.

  8. HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    HarmoWAM unifies predictive and reactive control in world action models via an adaptive gating mechanism to deliver improved zero-shot generalization and precision in robotic manipulation.

  9. PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    PriorVLA preserves pretrained priors in VLA models through a frozen Prior Expert and trained Adaptation Expert, delivering better robot manipulation performance than full fine-tuning with only 25% of the parameter updates.

  10. Unified Noise Steering for Efficient Human-Guided VLA Adaptation

    cs.RO 2026-05 unverdicted novelty 6.0

    UniSteer unifies human corrective actions and noise-space RL for VLA adaptation by inverting actions to noise targets, raising success rates from 20% to 90% in 66 minutes across four real-world manipulation tasks.

  11. RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    RePO-VLA raises average adversarial success rates in VLA manipulation from 20% to 75% by using recovery-aware initialization, a progress-aware semantic value function, and value-conditioned refinement on success and c...

  12. AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    AT-VLA introduces adaptive tactile injection and a dual-stream tactile reaction mechanism to integrate real-time tactile feedback into pretrained VLA models for contact-rich robotic manipulation.

  13. Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

    cs.RO 2026-04 unverdicted novelty 6.0

    X-WAM unifies robotic action execution and 4D world synthesis by adapting video diffusion priors with a lightweight depth branch and asynchronous noise sampling, achieving 79-91% success on robot benchmarks.

  14. Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

    cs.RO 2026-04 unverdicted novelty 6.0

    X-WAM unifies real-time robotic action execution with high-fidelity 4D world synthesis by adapting video diffusion priors through lightweight depth branches and asynchronous noise sampling, achieving 79-91% success on...

  15. Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation

    cs.RO 2026-04 unverdicted novelty 6.0

    MoT-HRA learns embodiment-agnostic human-intention priors from the HA-2.2M dataset of 2.2M human video episodes through a three-expert hierarchy to improve robotic motion plausibility and robustness under distribution shift.

  16. GazeVLA: Learning Human Intention for Robotic Manipulation

    cs.RO 2026-04 unverdicted novelty 6.0

    GazeVLA pretrains on large human egocentric datasets to capture gaze-based intention, then finetunes on limited robot data with chain-of-thought reasoning to achieve better robotic manipulation performance than baselines.

  17. Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models

    cs.RO 2026-04 unverdicted novelty 6.0

    State-of-the-art vision-language-action models catastrophically fail dynamic embodied reasoning due to lexical-kinematic shortcuts, behavioral inertia, and semantic feature collapse caused by architectural bottlenecks...

  18. AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models

    cs.RO 2026-04 unverdicted novelty 6.0

    AnchorRefine factorizes VLA action generation into a trajectory anchor for coarse planning and residual refinement for local corrections, improving success rates by up to 7.8% in simulation and 18% on real robots acro...

  19. FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes

    cs.RO 2026-04 unverdicted novelty 6.0

    A new GPU-accelerated deformable simulation framework trains manipulation policies in minutes using only synthetic data, achieving robust zero-shot transfer to physical robots.

  20. Robotic Manipulation is Vision-to-Geometry Mapping ($f(v) \rightarrow G$): Vision-Geometry Backbones over Language and Video Models

    cs.RO 2026-04 unverdicted novelty 6.0

    Vision-geometry backbones using pretrained 3D world models outperform vision-language and video models for robotic manipulation by enabling direct mapping from visual input to geometric actions.

  21. DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks

    cs.CV 2026-04 unverdicted novelty 6.0

    CLWM with DINOv3 targets, O(1) TTT memory, SAI latency masking, and EmbodiChain training achieves SOTA dual-arm simulation performance and zero-shot sim-to-real transfer that beats real-data finetuned baselines.

  22. AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement

    cs.RO 2026-04 unverdicted novelty 6.0

    AnySlot decouples language grounding from low-level control by inserting an explicit visual goal image, yielding better zero-shot performance on precise slot placement tasks than flat VLA policies.

  23. VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis

    cs.RO 2026-04 unverdicted novelty 6.0

    VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.

  24. TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks

    cs.RO 2026-04 unverdicted novelty 6.0

    TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.

  25. A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model

    cs.RO 2026-04 unverdicted novelty 6.0

    A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.

  26. ARM: Advantage Reward Modeling for Long-Horizon Manipulation

    cs.RO 2026-04 unverdicted novelty 6.0

    ARM trains reward models on Progressive/Regressive/Stagnant labels to enable adaptive reweighting in offline RL, reaching 99.4% success on towel-folding with minimal human intervention.

  27. Fast-WAM: Do World Action Models Need Test-time Future Imagination?

    cs.CV 2026-03 unverdicted novelty 6.0

    Fast-WAM shows that explicit future imagination at test time is not required for strong WAM performance; video modeling during training provides the main benefit.

  28. InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy

    cs.RO 2025-10 unverdicted novelty 6.0

    InternVLA-M1 uses spatially guided pre-training on 2.3M examples followed by action post-training to deliver up to 17% gains on robot manipulation benchmarks and 20.6% on unseen objects.

  29. SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

    cs.RO 2025-09 conditional novelty 6.0

    SimpleVLA-RL applies tailored reinforcement learning to VLA models, reaching SoTA on LIBERO, outperforming π₀ on RoboTwin, and surpassing SFT in real-world tasks while reducing data needs and identifying a 'pushcut' p...

  30. RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

    cs.RO 2025-06 unverdicted novelty 6.0

    RoboTwin 2.0 automates diverse synthetic data creation for dual-arm robots via MLLMs and five-axis domain randomization, leading to 228-367% gains in manipulation success.

  31. UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

    cs.RO 2025-05 unverdicted novelty 6.0

    UniVLA trains cross-embodiment vision-language-action policies from unlabeled videos via a latent action model in DINO space, beating OpenVLA on benchmarks with 1/20th pretraining compute and 1/10th downstream data.

  32. GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

    cs.RO 2025-03 unverdicted novelty 6.0

    GR00T N1 is a new open VLA foundation model for humanoid robots that outperforms imitation learning baselines in simulation and shows strong performance on real-world bimanual manipulation tasks.

  33. Embody4D: A Generalist 4D World Model for Embodied AI

    cs.CV 2026-05 unverdicted novelty 5.0

    Embody4D generates high-fidelity, view-consistent novel views from monocular videos for embodied scenarios via 3D-aware data synthesis, adaptive noise injection, and interaction-aware attention.

  34. STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation

    cs.RO 2026-04 unverdicted novelty 5.0

    STARRY uses unified diffusion to align spatial-temporal world predictions with action generation plus GASAM for geometry-aware attention, reaching 93.82%/93.30% success on 50 bimanual tasks in simulation and raising r...

  35. Cortex 2.0: Grounding World Models in Real-World Industrial Deployment

    cs.RO 2026-04 unverdicted novelty 5.0

    Cortex 2.0 introduces world-model-based planning that generates and scores future trajectories to outperform reactive vision-language-action baselines on industrial robotic tasks including pick-and-place, sorting, and...

  36. StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement

    cs.RO 2026-04 unverdicted novelty 5.0

    StableIDM stabilizes inverse dynamics models under manipulator truncation by combining robot-centric masking, directional spatial feature aggregation, and temporal dynamics refinement, yielding 12.1% higher strict act...

  37. A Progressive Training Strategy for Vision-Language Models to Counteract Spatio-Temporal Hallucinations in Embodied Reasoning

    cs.AI 2026-04 unverdicted novelty 5.0

    A progressive training framework using spatiotemporal chain-of-thought data reduces the forward-backward temporal query performance gap in VLMs from over 70% to 6.53%.

  38. Causal World Modeling for Robot Control

    cs.CV 2026-01 unverdicted novelty 5.0

    LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.

  39. Motus: A Unified Latent Action World Model

    cs.CV 2025-12 unverdicted novelty 5.0

    Motus unifies understanding, video generation, and action in one latent world model via MoT experts and optical-flow latent actions, reporting gains over prior methods in simulation and real robots.

  40. World Action Models: The Next Frontier in Embodied AI

    cs.RO 2026-05 unverdicted novelty 4.0

    The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.

  41. JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy

    cs.RO 2026-04 unverdicted novelty 4.0

    JoyAI-RA is a multi-source pretrained VLA model that claims to bridge human-to-robot embodiment gaps via data unification and outperforms prior methods on generalization-heavy robotic tasks.

  42. World Model for Robot Learning: A Comprehensive Survey

    cs.RO 2026-04 unverdicted novelty 3.0

    A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datase...

  43. Robot Learning from Human Videos: A Survey

    cs.RO 2026-04 unverdicted novelty 2.0

    The survey organizes human-video-based robot learning into task-, observation-, and action-oriented transfer pathways, reviews associated datasets, and outlines challenges for scalable embodied AI.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · cited by 42 Pith papers · 4 internal anchors

  1. [1]

    GPT-4 Technical Report

    OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    SAM 2: Segment Anything in Images and Videos

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R¨adle, C. Rolland, L. Gustafson, et al., “SAM 2: Segment anything in images and videos,” arXiv preprint arXiv:2408.00714 , 2024. 1

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  3. [3]

    Diffusion Policy: Visuomotor policy learning via action diffusion,

    C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion Policy: Visuomotor policy learning via action diffusion,” in RSS, 2023. 1, 2

    work page 2023

  4. [4]

    OpenVLA: An open-source vision-language-action model,

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, et al. , “OpenVLA: An open-source vision-language-action model,” in CoRL, 2024. 1, 2, 3, 6

    work page 2024

  5. [5]

    Toward next-generation learned robot manipu- lation,

    J. Cui and J. Trinkle, “Toward next-generation learned robot manipu- lation,” in Science Robotics , 2021. 1

    work page 2021

  6. [6]

    Open X- Embodiment: Robotic learning datasets and RT-X models,

    A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan, et al. , “Open X- Embodiment: Robotic learning datasets and RT-X models,” in ICRA,

    work page

  7. [7]

    DROID: A large-scale in-the-wild robot manipulation dataset,

    A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karam- cheti, S. Nasiriany, M. K. Srirama, L. Y . Chen, K. Ellis, et al. , “DROID: A large-scale in-the-wild robot manipulation dataset,” in RSS, 2024. 2, 3

    work page 2024

  8. [8]

    Data scaling laws in imitation learning for robotic manipulation,

    F. Lin, Y . Hu, P. Sheng, C. Wen, J. You, and Y . Gao, “Data scaling laws in imitation learning for robotic manipulation,” in ICLR, 2025. 2

    work page 2025

  9. [9]

    Learning fine-grained bimanual manipulation with low-cost hardware,

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” in RSS, 2023. 2

    work page 2023

  10. [10]

    RDT-1B: a diffusion foundation model for bimanual manipulation,

    S. Liu, L. Wu, B. Li, H. Tan, H. Chen, Z. Wang, K. Xu, H. Su, and J. Zhu, “RDT-1B: a diffusion foundation model for bimanual manipulation,” in ICLR, 2025. 2, 3, 6, 7

    work page 2025

  11. [11]

    RoboNet: Large-scale multi-robot learning,

    S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, S. Levine, and C. Finn, “RoboNet: Large-scale multi-robot learning,” in CoRL, 2019. 3

    work page 2019

  12. [12]

    Bridge data: Boosting gener- alization of robotic skills with cross-domain datasets,

    F. Ebert, Y . Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine, “Bridge data: Boosting gener- alization of robotic skills with cross-domain datasets,” in RSS, 2022. 2, 3

    work page 2022

  13. [13]

    BC-Z: Zero-shot task generalization with robotic imitation learning,

    E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “BC-Z: Zero-shot task generalization with robotic imitation learning,” in CoRL, 2022. 3

    work page 2022

  14. [14]

    RT-1: Robotics transformer for real-world control at scale,

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al. , “RT-1: Robotics transformer for real-world control at scale,” in RSS, 2023. 2, 3

    work page 2023

  15. [15]

    RH20T: A robotic dataset for learning diverse skills in one-shot,

    H.-S. Fang, H. Fang, Z. Tang, J. Liu, J. Wang, H. Zhu, and C. Lu, “RH20T: A robotic dataset for learning diverse skills in one-shot,” in RSS Workshops, 2023. 3

    work page 2023

  16. [16]

    RoboAgent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,

    H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V . Ku- mar, “RoboAgent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,” in ICRA, 2024. 3

    work page 2024

  17. [17]

    BridgeData v2: A dataset for robot learning at scale,

    H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen- Estruch, A. W. He, V . Myers, M. J. Kim, M. Du, et al. , “BridgeData v2: A dataset for robot learning at scale,” in CoRL, 2023. 3

    work page 2023

  18. [18]

    arXiv preprint arXiv:2412.13877 (2024) 14

    K. Wu, C. Hou, J. Liu, Z. Che, X. Ju, Z. Yang, M. Li, Y . Zhao, et al., “RoboMIND: Benchmark on multi-embodiment intelligence norma- tive data for robot manipulation,” arXiv preprint arXiv:2412.13877 ,

    work page arXiv

  19. [19]

    RoboTurk: A crowdsourcing platform for robotic skill learning through imitation,

    A. Mandlekar, Y . Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, S. Savarese, and L. Fei- Fei, “RoboTurk: A crowdsourcing platform for robotic skill learning through imitation,” in CoRL, 2018. 2

    work page 2018

  20. [20]

    The colosseum: A benchmark for evaluating generalization for robotic manipulation,

    W. Pumacay, I. Singh, J. Duan, R. Krishna, J. Thomason, and D. Fox, “The colosseum: A benchmark for evaluating generalization for robotic manipulation,” in RSS, 2024. 3

    work page 2024

  21. [21]

    Learning universal policies via text-guided video generation,

    Y . Du, S. Yang, B. Dai, H. Dai, O. Nachum, J. Tenenbaum, D. Schu- urmans, and P. Abbeel, “Learning universal policies via text-guided video generation,” in NeurIPS, 2024. 3

    work page 2024

  22. [22]

    Zero-shot robotic manipulation with pre-trained image-editing diffusion models,

    K. Black, M. Nakamoto, P. Atreya, H. R. Walke, C. Finn, A. Ku- mar, and S. Levine, “Zero-shot robotic manipulation with pre-trained image-editing diffusion models,” in ICLR, 2024. 3

    work page 2024

  23. [23]

    Closed-loop visuomotor control with generative expectation for robotic manipulation,

    Q. Bu, J. Zeng, L. Chen, Y . Yang, G. Zhou, J. Yan, P. Luo, H. Cui, Y . Ma, and H. Li, “Closed-loop visuomotor control with generative expectation for robotic manipulation,” in NeurIPS, 2024. 3

    work page 2024

  24. [24]

    RT-2: Vision- language-action models transfer web knowledge to robotic control,

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choro- manski, T. Ding, D. Driess, A. Dubey, C. Finn, et al. , “RT-2: Vision- language-action models transfer web knowledge to robotic control,” in CoRL, 2023. 3

    work page 2023

  25. [25]

    Octo: An open-source generalist robot policy,

    D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu, J. Luo, et al. , “Octo: An open-source generalist robot policy,” in RSS, 2024. 3

    work page 2024

  26. [26]

    $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

    K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter, et al. , “A vision- language-action flow model for general robot control,” arXiv preprint arXiv:2410.24164, 2024. 3, 6

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  27. [27]

    Latent action pretraining from videos,

    S. Ye, J. Jang, B. Jeon, S. Joo, J. Yang, B. Peng, A. Mandlekar, R. Tan, Y .-W. Chao, B. Y . Lin,et al., “Latent action pretraining from videos,” in ICLR, 2025. 3

    work page 2025

  28. [28]

    Grape: Generalizing robot policy via preference alignment.arXiv preprint arXiv:2411.19309,

    Z. Zhang, K. Zheng, Z. Chen, J. Jang, Y . Li, C. Wang, M. Ding, D. Fox, and H. Yao, “GRAPE: Generalizing robot policy via prefer- ence alignment,” arXiv preprint arXiv:2411.19309 , 2024. 4

    work page arXiv 2024

  29. [29]

    Reflexion: Language agents with verbal reinforcement learning,

    N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” NeurIPS, 2023. 4

    work page 2023

  30. [30]

    Genie: Generative interactive environments,

    J. Bruce, M. D. Dennis, A. Edwards, J. Parker-Holder, Y . Shi, E. Hughes, M. Lai, A. Mavalankar, R. Steigerwald, C. Apps, et al. , “Genie: Generative interactive environments,” in ICML, 2024. 5

    work page 2024

  31. [31]

    Spatial-temporal transformer networks for traffic flow forecasting.arXiv preprint arXiv:2001.02908, 2020

    M. Xu, W. Dai, C. Liu, X. Gao, W. Lin, G.-J. Qi, and H. Xiong, “Spatial-temporal transformer networks for traffic flow forecasting,” arXiv preprint arXiv:2001.02908 , 2020. 5

    work page arXiv 2001

  32. [32]

    Neural discrete representation learning,

    A. Van Den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,” in NeurIPS, 2017. 5

    work page 2017

  33. [33]

    Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

    Z. Chen, W. Wang, Y . Cao, Y . Liu, Z. Gao, E. Cui, J. Zhu, S. Ye, H. Tian, Z. Liu, et al. , “Expanding performance boundaries of open- source multimodal models with model, data, and test-time scaling,” arXiv preprint arXiv:2412.05271 , 2024. 6

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  34. [34]

    Towards synergistic, generalized, and efficient dual-system for robotic manipulation.arXiv preprint arXiv:2410.08001, 2024a

    Q. Bu, H. Li, L. Chen, J. Cai, J. Zeng, H. Cui, M. Yao, and Y . Qiao, “Towards synergistic, generalized, and efficient dual-system for robotic manipulation,” arXiv preprint arXiv:2410.08001 , 2024. 6 APPENDIX ACKNOWLEDGEMENT We thank Remi Cadene and the LeRobot community for their support and collaboration. In addition, we are grateful to Shu Jiang, Cheng...

    work page arXiv 2024