arxiv: 2604.05484 · v1 · submitted 2026-04-07 · 💻 cs.RO · cs.CV

Recognition: no theorem link

CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment

Li Kang , Yutao Fan , Rui Li , Heng Zhou , Yiran Qin , Zhemeng Zhang , Songtao Huang , Xiufeng Song

show 6 more authors

Zaibin Zhang Bruno N.Y. Chen Zhenfei Yin Dongzhan Zhou Wangmeng Zuo Lei Bai

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:23 UTC · model grok-4.3

classification 💻 cs.RO cs.CV

keywords multi-agent collaborationembodied AIcompositional environmentmulti-arm manipulationsim-to-real transfervision-language modelsshared workspacerobot coordination

0 comments

The pith

A compositional environment blending real and simulated components lets multiple robots coordinate safely in shared workspaces through scene reconstruction, vision-language planning, and validated transfer.

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

The paper presents CoEnv as a framework for multi-agent embodied systems facing difficulties with spatial coordination, temporal reasoning, and workspace awareness. It creates a compositional environment that merges physical scenes with simulation so agents can explore strategies safely before real execution, modeled after how humans separate planning from action. The process runs in three stages: digitizing the real workspace into simulation, generating actions via vision-language models in either quick high-level or detailed code-based modes, and moving plans back to reality after collision checks. A reader would care if this leads to more reliable performance on joint manipulation tasks that exceed what single robots can handle.

Core claim

The authors establish that a synergistic integration of real-world and simulation components, called the compositional environment, creates a unified decision-making space in which multiple robotic agents perceive intentions and coordinate actions. This integration is implemented via real-to-sim scene reconstruction, VLM-driven action synthesis for both real-time high-level interfaces and iterative code-based trajectory generation, and validated sim-to-real transfer that includes collision detection, yielding high task success rates and execution efficiency on multi-arm manipulation benchmarks.

What carries the argument

The compositional environment, defined as the integration of real-world and simulation components that forms a single space for agents to perceive intentions and decide jointly.

If this is right

High success rates on challenging multi-arm manipulation benchmarks.
Improved execution efficiency during collaborative tasks in shared spaces.
Safe exploration of strategies inside simulation before physical deployment.
Support for both quick high-level planning and detailed iterative trajectory generation in the same framework.

Where Pith is reading between the lines

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

If extended, the same three-stage structure might apply to collaborative navigation or assembly tasks outside the tested manipulation benchmarks.
The framework could be evaluated with larger robot teams or different hardware to check whether the unified space scales without added coordination overhead.
This separation of planning from execution might allow existing single-robot systems to gain awareness benefits by temporarily borrowing simulated companions.
Broader adoption could reduce custom coding needs for new multi-agent tasks if the vision-language synthesis generalizes across environments.

Load-bearing premise

Vision-language models can produce reliable real-time high-level plans and iterative code-based trajectories for complex spatial coordination and temporal reasoning without generating unsafe or infeasible actions.

What would settle it

A deployment trial in which simulation-validated plans produce collisions or task failures when executed by the physical robots, or measured success rates on the multi-arm benchmarks fall well below the reported levels.

Figures

Figures reproduced from arXiv: 2604.05484 by Bruno N.Y. Chen, Dongzhan Zhou, Heng Zhou, Lei Bai, Li Kang, Rui Li, Songtao Huang, Wangmeng Zuo, Xiufeng Song, Yiran Qin, Yutao Fan, Zaibin Zhang, Zhemeng Zhang, Zhenfei Yin.

**Figure 1.** Figure 1: Motivation of CoEnv. Physical-world execution offers high fidelity but risks costly collisions, while the digital world enables cost-effective and safe testing. CoEnv composes both worlds through pose and action alignment, forming a compositional environment (left) that supports real-to-sim reconstruction, simulation-conditioned action synthesis, and safe real-world deployment (right). single-agent syste… view at source ↗

**Figure 2.** Figure 2: Overview of the CoEnv framework. Top-left: [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Task demonstrations. We evaluate CoEnv on five multi-agent manipulation tasks with increasing coordination complexity. Top three rows: two-agent tasks (Franka × 2) including Cube Stacking, Ball Pickup, and Transfer Cylinder. Bottom two rows: three-agent tasks (Franka + AgileX Piper) including Place Cucumber and Brush Box. Each row shows keyframes from a successful real-world execution. of which require com… view at source ↗

**Figure 4.** Figure 4: Sim-to-real qualitative results. Side-by-side comparison of simulation planning and real-world execution across four representative tasks. The high visual correspondence validates that CoEnv’s compositional environment faithfully bridges the sim-to-real gap for multi-agent collaboration. Failure analysis. We observe three recurring failure modes. (i) Minor simto-real offsets in object poses occasionally… view at source ↗

**Figure 5.** Figure 5: Scalable data collection pipeline. CoEnv first synthesizes and validates manipulation strategies in simulation, then transfers them to real robots for physical execution, collecting real-world multi-agent trajectory data that can be used to train generalist policies, providing an alternative to manual teleoperation. collaborative strategies before committing to physical execution. Experiments on five manip… view at source ↗

**Figure 6.** Figure 6: Representative failure cases. From left to right: (1) Cube Stacking: the stacked cube slips off due to sim-to-real positional offset at placement; (2) Transfer Cylinder : the receiving arm fails to align with the handing arm’s gripper during handover, causing the cylinder to drop; (3) Place Cucumber : the pot lid is grasped at an unstable point (red circle), causing it to fall during the holding phase; (4… view at source ↗

read the original abstract

Multi-agent embodied systems hold promise for complex collaborative manipulation, yet face critical challenges in spatial coordination, temporal reasoning, and shared workspace awareness. Inspired by human collaboration where cognitive planning occurs separately from physical execution, we introduce the concept of compositional environment -- a synergistic integration of real-world and simulation components that enables multiple robotic agents to perceive intentions and operate within a unified decision-making space. Building on this concept, we present CoEnv, a framework that leverages simulation for safe strategy exploration while ensuring reliable real-world deployment. CoEnv operates through three stages: real-to-sim scene reconstruction that digitizes physical workspaces, VLM-driven action synthesis supporting both real-time planning with high-level interfaces and iterative planning with code-based trajectory generation, and validated sim-to-real transfer with collision detection for safe deployment. Extensive experiments on challenging multi-arm manipulation benchmarks demonstrate CoEnv's effectiveness in achieving high task success rates and execution efficiency, establishing a new paradigm for multi-agent embodied AI.

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

CoEnv's three-stage pipeline for multi-agent robot collaboration integrates familiar sim-to-real and VLM ideas into a practical workflow, but thin experimental details leave the performance claims hard to evaluate.

read the letter

The paper introduces CoEnv as a way to handle collaboration among embodied agents by creating a compositional environment that blends real and simulated elements. The three stages—digitizing the workspace, synthesizing actions via VLMs for both quick decisions and detailed trajectories, and then validating the transfer with collision checks—form the main structure. This setup addresses real issues like agents needing to understand each other's intentions in a shared space without bumping into things. Using simulation for exploration while keeping real deployment safe is a sensible split, and the VLM handling different planning granularities could help with both speed and precision. What works is the practical framing. It draws from human collaboration where planning is separate from execution, and applies that to robots. The benchmarks on multi-arm manipulation sound relevant for things like assembly or logistics. The main weakness is in the supporting evidence. Claims of high task success rates and better efficiency come without any specific numbers, baselines, or tests showing what happens when you remove parts of the pipeline. The VLM's ability to avoid unsafe actions in coordinated tasks isn't backed by error analysis or examples of how prompts or guards prevent bad outputs. If those details aren't in the full paper, the results could be overstated. This kind of work is aimed at people building systems for real-world robot teams. It could give them a template to try, especially if they already use VLMs and sims. I think it deserves peer review. The approach has enough structure to be worth checking the experiments and seeing if the claims hold up under scrutiny.

Referee Report

2 major / 1 minor

Summary. The paper introduces the concept of a compositional environment integrating real-world and simulation components for multi-agent robotic collaboration, and presents the CoEnv framework operating in three stages: real-to-sim scene reconstruction, VLM-driven action synthesis (for both real-time high-level planning and iterative code-based trajectory generation), and validated sim-to-real transfer with collision detection. It claims that extensive experiments on challenging multi-arm manipulation benchmarks demonstrate high task success rates and execution efficiency, establishing a new paradigm for multi-agent embodied AI.

Significance. If the experimental results hold with proper quantitative validation, the framework's separation of cognitive planning (via VLMs and simulation) from physical execution could provide a practical method for improving safety and coordination in shared workspaces, advancing embodied multi-agent systems beyond current sim-to-real approaches.

major comments (2)

[Abstract] Abstract: The claim that 'extensive experiments on challenging multi-arm manipulation benchmarks demonstrate CoEnv's effectiveness in achieving high task success rates and execution efficiency' provides no numerical results, baselines, error bars, ablation studies, or statistical details. This is load-bearing for the central effectiveness claim, as success rates cannot be attributed to the compositional environment or VLM synthesis without such evidence.
[Method (VLM-driven action synthesis)] VLM-driven action synthesis description: The framework asserts that VLM supports both real-time planning with high-level interfaces and iterative planning with code-based trajectory generation while avoiding unsafe or infeasible actions in shared workspaces, but reports no VLM error rates, prompt details, or ablation removing the collision-detection guard. This directly impacts the reliability of sim-to-real transfer and the attribution of results to the proposed concept.

minor comments (1)

[Introduction] The distinction between the introduced 'compositional environment' and standard sim-to-real pipelines could be formalized with a precise definition or diagram to clarify novelty.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important opportunities to strengthen the presentation of quantitative evidence and methodological transparency. We address each major comment below and commit to revisions that improve clarity and rigor without altering the core contributions.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that 'extensive experiments on challenging multi-arm manipulation benchmarks demonstrate CoEnv's effectiveness in achieving high task success rates and execution efficiency' provides no numerical results, baselines, error bars, ablation studies, or statistical details. This is load-bearing for the central effectiveness claim, as success rates cannot be attributed to the compositional environment or VLM synthesis without such evidence.

Authors: We agree that the abstract would be strengthened by including specific quantitative results to support the effectiveness claims. The full manuscript reports detailed success rates, baseline comparisons, and efficiency metrics in the experiments section, but these were not summarized numerically in the abstract. In the revised version, we will update the abstract to concisely include key results (e.g., task success rates on multi-arm benchmarks, comparisons to baselines, and efficiency gains) while maintaining brevity. This directly addresses the concern about attribution to the compositional environment and VLM components. revision: yes
Referee: [Method (VLM-driven action synthesis)] VLM-driven action synthesis description: The framework asserts that VLM supports both real-time planning with high-level interfaces and iterative planning with code-based trajectory generation while avoiding unsafe or infeasible actions in shared workspaces, but reports no VLM error rates, prompt details, or ablation removing the collision-detection guard. This directly impacts the reliability of sim-to-real transfer and the attribution of results to the proposed concept.

Authors: We acknowledge that additional details on the VLM component would improve transparency. The manuscript describes the dual planning modes and collision-detection mechanism in the method section, but does not isolate VLM-specific error rates or provide an explicit ablation on the guard. In revision, we will add the exact prompts used for high-level planning and code generation to the appendix. We will also report observed VLM failure cases from the experimental trials and include a targeted ablation removing the collision-detection guard to quantify its role in safe transfer. These changes will be placed in the method and experiments sections to better support attribution. revision: partial

Circularity Check

0 steps flagged

Framework proposal is self-contained with no circular derivations

full rationale

The paper describes a conceptual three-stage pipeline (real-to-sim reconstruction, VLM-driven action synthesis, sim-to-real transfer) for multi-agent collaboration without any equations, fitted parameters, or mathematical derivations. No load-bearing steps reduce claims to self-referential inputs by construction, and the experimental validation on benchmarks is presented as empirical evidence rather than tautological output. Any self-citations (if present in the full text) do not form the central premise or forbid alternatives, keeping the derivation chain independent.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the new concept of compositional environment and the reliability of VLM planning; no explicit free parameters or invented physical entities are introduced beyond the framework itself.

axioms (2)

domain assumption VLMs can generate safe and effective action plans for multi-agent robotic tasks when provided high-level interfaces or code-based trajectories
Invoked in the VLM-driven action synthesis stage of the framework.
domain assumption Real-to-sim scene reconstruction accurately digitizes physical workspaces for strategy exploration
Stated as the first stage enabling safe planning.

invented entities (1)

compositional environment no independent evidence
purpose: Synergistic integration of real-world and simulation components for unified multi-agent decision-making
New concept introduced to separate cognitive planning from physical execution.

pith-pipeline@v0.9.0 · 5509 in / 1329 out tokens · 36991 ms · 2026-05-10T19:23:10.303145+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

95 extracted references · 39 canonical work pages · 11 internal anchors

[1]

In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part

Agassounon, W., Martinoli, A.: Efficiency and robustness of threshold-based dis- tributed allocation algorithms in multi-agent systems. In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part
[2]

1090–1097 (2002)

pp. 1090–1097 (2002)

2002
[3]

AgileX Robotics: Piper sdk.https://github.com/agilexrobotics/piper_sdk (2024)

2024
[4]

Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., Topcu, U.,

Ahn, M., Dwibedi, D., Finn, C., Arenas, M.G., Gopalakrishnan, K., Hausman, K., Ichter, B., Irpan, A., Joshi, N., Julian, R., et al.: Autort: Embodied foun- dation models for large scale orchestration of robotic agents. arXiv preprint arXiv:2401.12963 (2024)

work page arXiv 2024
[5]

Anthropic: Claude code.https://claude.ai/product/claude-code(2025)

2025
[6]

Towards a unified understanding of robot ma- nipulation: A comprehensive survey,

Bai, S., Song, W., Chen, J., Ji, Y., Zhong, Z., Yang, J., Zhao, H., Zhou, W., Zhao, W., Li, Z., et al.: Towards a unified understanding of robot manipulation: A comprehensive survey. arXiv preprint arXiv:2510.10903 (2025)

work page arXiv 2025
[7]

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

Black, K., Brown, N., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., Groom, L., Hausman, K., Ichter, B., et al.:π0: A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164 (2024)

work page internal anchor Pith review Pith/arXiv arXiv 2024
[8]

Gr-3 technical report.arXiv preprint arXiv:2507.15493,

Cheang, C., Chen, S., Cui, Z., Hu, Y., Huang, L., Kong, T., Li, H., Li, Y., Liu, Y., Ma, X., et al.: Gr-3 technical report. arXiv preprint arXiv:2507.15493 (2025)

work page arXiv 2025
[9]

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

Chen, T., Chen, Z., Chen, B., Cai, Z., Liu, Y., Li, Z., Liang, Q., Lin, X., Ge, Y., Gu, Z., et al.: Robotwin 2.0: A scalable data generator and benchmark with strong 16 L. Kang et al. domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506.18088 (2025)

work page internal anchor Pith review arXiv 2025
[10]

Computers in Biology and Medicine 163, 107121 (2023)

Chen, Z., Marzullo, A., Alberti, D., Lievore, E., Fontana, M., De Cobelli, O., Musi, G., Ferrigno, G., De Momi, E.: Frsr: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery. Computers in Biology and Medicine 163, 107121 (2023)

2023
[11]

International Journal of Robotics and Simulation6(1), 89–102 (2024)

Chukwurah, N., Adebayo, A.S., Ajayi, O.O.: Sim-to-real transfer in robotics: Ad- dressing the gap between simulation and real-world performance. International Journal of Robotics and Simulation6(1), 89–102 (2024)

2024
[12]

arXiv preprint arXiv:2410.07408 (2024) 16 Y

Dai, T., Wong, J., Jiang, Y., Wang, C., Gokmen, C., Zhang, R., Wu, J., Fei-Fei, L.: Automated creation of digital cousins for robust policy learning. arXiv preprint arXiv:2410.07408 (2024)

work page arXiv 2024
[13]

arXiv preprint arXiv:2505.07096 (2025)

Dan, P., Kedia, K., Chao, A., Duan, E.W., Pace, M.A., Ma, W.C., Choud- hury, S.: X-sim: Cross-embodiment learning via real-to-sim-to-real. arXiv preprint arXiv:2505.07096 (2025)

work page arXiv 2025
[14]

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M.S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al.: Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378 (2023)

work page internal anchor Pith review arXiv 2023
[15]

arXiv preprint arXiv:2509.20021 (2025)

Feng,T.,Wang,X.,Jiang,Y.G.,Zhu,W.:Embodiedai:Fromllmstoworldmodels. arXiv preprint arXiv:2509.20021 (2025)

work page arXiv 2025
[16]

Multi-agent embodied ai: Advances and future directions.arXiv preprint arXiv:2505.05108,

Feng, Z., Xue, R., Yuan, L., Yu, Y., Ding, N., Liu, M., Gao, B., Sun, J., Zheng, X., Wang, G.: Multi-agent embodied ai: Advances and future directions. arXiv preprint arXiv:2505.05108 (2025)

work page arXiv 2025
[17]

The International journal of robotics research23(9), 939–954 (2004)

Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. The International journal of robotics research23(9), 939–954 (2004)

2004
[18]

In: Findings of the Association for Computational Linguistics: NAACL 2024

Gong, R., Huang, Q., Ma, X., Noda, Y., Durante, Z., Zheng, Z., Terzopoulos, D., Fei-Fei, L., Gao, J., Vo, H.: Mindagent: Emergent gaming interaction. In: Findings of the Association for Computational Linguistics: NAACL 2024. pp. 3154–3183 (2024)

2024
[19]

Haldar, S., Johannsmeier, L., Pinto, L., Gupta, A., Fox, D., Narang, Y., Mandlekar, A.:Pointbridge:3drepresentationsforcrossdomainpolicylearning.arXivpreprint arXiv:2601.16212 (2026)

work page arXiv 2026
[20]

Re3 Sim: Generating High-Fidelity Simulation Data via 3D-Photorealistic Real-to-Sim for Robotic Manipulation

Han, X., Liu, M., Chen, Y., Yu, J., Lyu, X., Tian, Y., Wang, B., Zhang, W., Pang, J.: Re3sim: Generating high-fidelity simulation data via 3d-photorealistic real-to-sim for robotic manipulation. arXiv preprint arXiv:2502.08645 (2025)

work page arXiv 2025
[21]

IEEE Transactions on Robotics39(2), 1225–1243 (2022)

Horváth, D., Erdős, G., Istenes, Z., Horváth, T., Földi, S.: Object detection using sim2real domain randomization for robotic applications. IEEE Transactions on Robotics39(2), 1225–1243 (2022)

2022
[22]

$\pi^{*}_{0.6}$: a VLA That Learns From Experience

Intelligence, P., Amin, A., Aniceto, R., Balakrishna, A., Black, K., Conley, K., Connors, G., Darpinian, J., Dhabalia, K., DiCarlo, J., et al.:π∗ 0.6: a VLA that learns from experience. arXiv preprint arXiv:2511.14759 (2025)

work page Pith review arXiv 2025
[23]

OpenVLA: An Open-Source Vision-Language-Action Model

Kim, M.J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al.: Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246 (2024)

work page internal anchor Pith review Pith/arXiv arXiv 2024
[24]

The International journal of robotics research32(12), 1495–1512 (2013)

Korsah, G.A., Stentz, A., Dias, M.B.: A comprehensive taxonomy for multi-robot task allocation. The International journal of robotics research32(12), 1495–1512 (2013)

2013
[25]

arXiv preprint arXiv:2406.03757 (2024) CoEnv: Multi-Agent Collaboration via Compositional Environment 17

Li, J., Chen, P., Wu, S., Zheng, C., Xu, H., Jia, J.: Robocoder: Robotic learn- ing from basic skills to general tasks with large language models. arXiv preprint arXiv:2406.03757 (2024) CoEnv: Multi-Agent Collaboration via Compositional Environment 17

work page arXiv 2024
[26]

Controlvla: Few-shot object-centric adaptation for pre-trained vision- language-action models,

Li, P., Wu, Y., Xi, Z., Li, W., Huang, Y., Zhang, Z., Chen, Y., Wang, J., Zhu, S.C., Liu, T., et al.: Controlvla: Few-shot object-centric adaptation for pre-trained vision-language-action models. arXiv preprint arXiv:2506.16211 (2025)

work page arXiv 2025
[27]

Vision-language foundation models as effective robot imitators.arXiv preprint arXiv:2311.01378, 2023

Li, X., Liu, M., Zhang, H., Yu, C., Xu, J., Wu, H., Cheang, C., Jing, Y., Zhang, W., Liu, H., et al.: Vision-language foundation models as effective robot imitators. arXiv preprint arXiv:2311.01378 (2023)

work page arXiv 2023
[28]

IEEE/CAA Journal of Automatica Sinica12(6), 1095–1116 (2025)

Li, Z., Wu, W., Guo, Y., Sun, J., Han, Q.L.: Embodied multi-agent systems: A review. IEEE/CAA Journal of Automatica Sinica12(6), 1095–1116 (2025)

2025
[29]

In: 2023 IEEE International conference on robotics and automation (ICRA)

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., Zeng, A.: Code as policies: Language model programs for embodied control. In: 2023 IEEE International conference on robotics and automation (ICRA). pp. 9493–9500. IEEE (2023)

2023
[30]

Sim- pact: Simulation-enabled action planning using vision- language models.arXiv preprint arXiv:2512.05955, 2025

Liu, H., Yao, S., Chen, H., Gao, J., Mao, J., Huang, J.B., Du, Y.: Simpact: Simulation-enabled action planning using vision-language models. arXiv preprint arXiv:2512.05955 (2025)

work page arXiv 2025
[31]

RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Liu, S., Wu, L., Li, B., Tan, H., Chen, H., Wang, Z., Xu, K., Su, H., Zhu, J.: Rdt-1b: a diffusion foundation model for bimanual manipulation. arXiv preprint arXiv:2410.07864 (2024)

work page internal anchor Pith review arXiv 2024
[32]

In: Robotics: Science and Systems

Liu, X., Li, X., Guo, D., Tan, S., Liu, H., Sun, F.: Embodied multi-agent task planning from ambiguous instruction. In: Robotics: Science and Systems. pp. 1–14 (2022)

2022
[33]

IEEE/ASME Transactions on Mechatronics (2025)

Liu, Y., Chen, W., Bai, Y., Liang, X., Li, G., Gao, W., Lin, L.: Aligning cyber space with physical world: A comprehensive survey on embodied ai. IEEE/ASME Transactions on Mechatronics (2025)

2025
[34]

In: 3rd RSS workshop on dexterous manipulation: learning and control with diverse data (2025)

Lou, H., Zhang, M., Geng, H., Zhou, H., He, S., Gao, Z., Zhao, S., Mao, J., Abbeel, P., Malik, J., et al.: Dream: Differentiable real-to-sim-to-real engine for learning robotic manipulation. In: 3rd RSS workshop on dexterous manipulation: learning and control with diverse data (2025)

2025
[35]

RSS (2024)

Ma, J., Liang, W., Wang, H.J., Zhu, Y., Fan, L., Bastani, O., Jayaraman, D.: Dreureka: Language model guided sim-to-real transfer. RSS (2024)

2024
[36]

In: 2024 IEEE International Conference on Robotics and Au- tomation (ICRA)

Mandi, Z., Jain, S., Song, S.: RoCo: Dialectic multi-robot collaboration with large language models. In: 2024 IEEE International Conference on Robotics and Au- tomation (ICRA). pp. 286–299. IEEE (2024)

2024
[37]

In: 2022 International conference on robotics and automation (ICRA)

Mandi, Z., Liu, F., Lee, K., Abbeel, P.: Towards more generalizable one-shot visual imitation learning. In: 2022 International conference on robotics and automation (ICRA). pp. 2434–2444. IEEE (2022)

2022
[38]

Journal of Guidance, Control, and Dynamics30(4), 1193–1197 (2007)

Markley, F.L., Cheng, Y., Crassidis, J.L., Oshman, Y.: Averaging quaternions. Journal of Guidance, Control, and Dynamics30(4), 1193–1197 (2007)

2007
[39]

arXiv preprint arXiv:2509.18597 (2025)

Meng, Y., Sun, Z., Fest, M., Li, X., Bing, Z., Knoll, A.: Growing with your embod- ied agent: A human-in-the-loop lifelong code generation framework for long-horizon manipulation skills. arXiv preprint arXiv:2509.18597 (2025)

work page arXiv 2025
[40]

JEPA-VLA: Video predictive embedding is needed for VLA models.arXiv preprint arXiv:2602.11832,

Miao, S., Feng, N., Wu, J., Lin, Y., He, X., Li, D., Long, M.: Jepa-vla: Video predictive embedding is needed for vla models. arXiv preprint arXiv:2602.11832 (2026)

work page arXiv 2026
[41]

Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning

Mittal, M., Roth, P., Tigue, J., Richard, A., Zhang, O., Du, P., Serrano-Munoz, A., Yao, X., Zurbrügg, R., Rudin, N., et al.: Isaac lab: A gpu-accelerated simulation framework for multi-modal robot learning. arXiv preprint arXiv:2511.04831 (2025)

work page internal anchor Pith review arXiv 2025
[42]

Maniskill: Generalizable manipulation skill bench- mark with large-scale demonstrations.arXiv preprint arXiv:2107.14483, 2021

Mu, T., Ling, Z., Xiang, F., Yang, D., Li, X., Tao, S., Huang, Z., Jia, Z., Su, H.: Maniskill: Generalizable manipulation skill benchmark with large-scale demonstra- tions. arXiv preprint arXiv:2107.14483 (2021) 18 L. Kang et al

work page arXiv 2021
[43]

Frontiers in Robotics and AI9, 799893 (2022)

Muratore, F., Ramos, F., Turk, G., Yu, W., Gienger, M., Peters, J.: Robot learning from randomized simulations: A review. Frontiers in Robotics and AI9, 799893 (2022)

2022
[44]

RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

Nasiriany, S., Maddukuri, A., Zhang, L., Parikh, A., Lo, A., Joshi, A., Mandlekar, A., Zhu, Y.: Robocasa: Large-scale simulation of everyday tasks for generalist robots. arXiv preprint arXiv:2406.02523 (2024)

work page internal anchor Pith review arXiv 2024
[45]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Qin, Y., Kang, L., Song, X., Yin, Z., Liu, X., Liu, X., Zhang, R., Bai, L.: Robofac- tory: Exploring embodied agent collaboration with compositional constraints. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10075–10085 (2025)

2025
[46]

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Ren, T., Liu, S., Zeng, A., Lin, J., Li, K., Cao, H., Chen, J., Huang, X., Chen, Y., Yan, F., et al.: Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14159 (2024)

work page Pith review arXiv 2024
[47]

arXiv preprint arXiv:2508.13073 (2025)

Shao, R., Li, W., Zhang, L., Zhang, R., Liu, Z., Chen, R., Nie, L.: Large vlm-based vision-language-action models for robotic manipulation: A survey. arXiv preprint arXiv:2508.13073 (2025)

work page arXiv 2025
[48]

OpenAI GPT-5 System Card

Singh, A., Fry, A., Perelman, A., Tart, A., Ganesh, A., El-Kishky, A., McLaughlin, A., Low, A., Ostrow, A., Ananthram, A., et al.: Openai gpt-5 system card. arXiv preprint arXiv:2601.03267 (2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[49]

In: 2025 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS)

Singh, H., Das, R.J., Han, M., Nakov, P., Laptev, I.: Malmm: Multi-agent large language models for zero-shot robotic manipulation. In: 2025 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS). pp. 20386–20393. IEEE (2025)

2025
[50]

Geovla: Em- powering 3d representations in vision-language-action models,

Sun, L., Xie, B., Liu, Y., Shi, H., Wang, T., Cao, J.: Geovla: Empowering 3d representations in vision-language-action models. arXiv preprint arXiv:2508.09071 (2025)

work page arXiv 2025
[51]

Available: https://arxiv.org/abs/2505.03673

Tan, H., Hao, X., Chi, C., Lin, M., Lyu, Y., Cao, M., Liang, D., Chen, Z., Lyu, M., Peng, C., et al.: Roboos: A hierarchical embodied framework for cross-embodiment and multi-agent collaboration. arXiv preprint arXiv:2505.03673 (2025)

work page arXiv 2025
[52]

Maniskill3: Gpu parallelized robotics simulation and rendering for generalizable embodied ai.arXiv preprint arXiv:2410.00425, 2024

Tao, S., Xiang, F., Shukla, A., Qin, Y., Hinrichsen, X., Yuan, X., Bao, C., Lin, X., Liu, Y., Chan, T.k., et al.: Maniskill3: Gpu parallelized robotics simulation and rendering for generalizable embodied ai. arXiv preprint arXiv:2410.00425 (2024)

work page arXiv 2024
[53]

Team, G.A.: Gen-0: Embodied foundation models that scale with physical interac- tion.GeneralistAIBlog(2025),https://generalistai.com/blog/nov-04-2025-GEN-0

2025
[54]

Octo: An Open-Source Generalist Robot Policy

Team, O.M., Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., et al.: Octo: An open-source generalist robot policy. arXiv preprint arXiv:2405.12213 (2024)

work page internal anchor Pith review arXiv 2024
[55]

Interndata-a1: Pioneering high-fidelity synthetic data for pre-training generalist policy.arXiv preprint arXiv:2511.16651, 2025

Tian, Y., Yang, Y., Xie, Y., Cai, Z., Shi, X., Gao, N., Liu, H., Jiang, X., Qiu, Z., Yuan, F., et al.: Interndata-a1: Pioneering high-fidelity synthetic data for pre- training generalist policy. arXiv preprint arXiv:2511.16651 (2025)

work page arXiv 2025
[56]

In: 2012 IEEE/RSJ international conference on intelligent robots and systems

Todorov, E., Erez, T., Tassa, Y.: Mujoco: A physics engine for model-based control. In: 2012 IEEE/RSJ international conference on intelligent robots and systems. pp. 5026–5033. IEEE (2012)

2012
[57]

In: 9th Annual Confer- ence on Robot Learning (2025)

Wan, W., Fu, J., Yuan, X., Zhu, Y., Su, H.: Lodestar: long-horizon dexterity via synthetic data augmentation from human demonstrations. In: 9th Annual Confer- ence on Robot Learning (2025)

2025
[58]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Wang, Y., Zhu, H., Liu, M., Yang, J., Fang, H.S., He, T.: Vq-vla: Improving vision-language-action models via scaling vector-quantized action tokenizers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11089–11099 (2025) CoEnv: Multi-Agent Collaboration via Compositional Environment 19

2025
[59]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Wen, B., Yang, W., Kautz, J., Birchfield, S.: Foundationpose: Unified 6d pose esti- mation and tracking of novel objects. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 17868–17879 (2024)

2024
[60]

arXiv preprint arXiv:2601.18692 (2026)

Wu, W., Lu, F., Wang, Y., Yang, S., Liu, S., Wang, F., Zhu, Q., Sun, H., Wang, Y., Ma, S., et al.: A pragmatic vla foundation model. arXiv preprint arXiv:2601.18692 (2026)

work page arXiv 2026
[61]

In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., et al.: Sapien: A simulated part-based interactive environment. In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11097–11107 (2020)

2020
[62]

Neurocomputing638, 129963 (2025)

Xiao, X., Liu, J., Wang, Z., Zhou, Y., Qi, Y., Jiang, S., He, B., Cheng, Q.: Robot learning in the era of foundation models: A survey. Neurocomputing638, 129963 (2025)

2025
[63]

Robotic Control via Embodied Chain-of-Thought Reasoning

Zawalski,M.,Chen,W.,Pertsch,K.,Mees,O.,Finn,C.,Levine,S.:Roboticcontrol via embodied chain-of-thought reasoning. arXiv preprint arXiv:2407.08693 (2024)

work page internal anchor Pith review arXiv 2024
[64]

Building cooperative embodied agents modularly with large language models

Zhang, H., Du, W., Shan, J., Zhou, Q., Du, Y., Tenenbaum, J.B., Shu, T., Gan, C.: Building cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307.02485 (2023)

work page arXiv 2023
[65]

High-fidelity simulated data generation for real-world zero-shot robotic manipulation learning with gaussian splatting,

Zhao, H., Zeng, C., Zhuang, L., Zhao, Y., Xue, S., Wang, H., Zhao, X., Li, Z., Li, K., Huang, S., et al.: High-fidelity simulated data generation for real-world zero-shot robotic manipulation learning with gaussian splatting. arXiv preprint arXiv:2510.10637 (2025)

work page arXiv 2025
[66]

In: Proceedings of the Computer Vision and Pattern Recognition Conference

Zhao, Q., Lu, Y., Kim, M.J., Fu, Z., Zhang, Z., Wu, Y., Li, Z., Ma, Q., Han, S., Finn, C., et al.: Cot-vla: Visual chain-of-thought reasoning for vision-language- action models. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 1702–1713 (2025)

2025
[67]

3D-VLA: A 3D Vision-Language-Action Generative World Model

Zhen, H., Qiu, X., Chen, P., Yang, J., Yan, X., Du, Y., Hong, Y., Gan, C.: 3d-vla: A 3d vision-language-action generative world model. arXiv preprint arXiv:2403.09631 (2024)

work page internal anchor Pith review arXiv 2024
[68]

Roborefer: Towards spatial referring with reasoning in vision-language models for robotics.arXiv preprint arXiv:2506.04308, 2025

Zhou, E., An, J., Chi, C., Han, Y., Rong, S., Zhang, C., Wang, P., Wang, Z., Huang, T., Sheng, L., et al.: Roborefer: Towards spatial referring with reasoning in vision-language models for robotics. arXiv preprint arXiv:2506.04308 (2025)

work page arXiv 2025
[69]

In: Proceedings of the Computer Vision and Pattern Recognition Conference

Zhou, E., Su, Q., Chi, C., Zhang, Z., Wang, Z., Huang, T., Sheng, L., Wang, H.: Code-as-monitor: Constraint-aware visual programming for reactive and proactive robotic failure detection. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 6919–6929 (2025)

2025
[70]

IEEE Robotics and Automation Letters (2025)

Zhu, S., Mou, L., Li, D., Ye, B., Huang, R., Zhao, H.: Vr-robo: A real-to-sim-to- real framework for visual robot navigation and locomotion. IEEE Robotics and Automation Letters (2025)

2025
[71]

Viola: Imitation learning for vision- based manipulation with object proposal priors

Zhu, Y., Joshi, A., Stone, P., Zhu, Y.: Viola: Imitation learning for vision-based manipulation with object proposal priors. arXiv preprint arXiv:2210.11339 (2022)

work page arXiv 2022
[72]

In: Conference on Robot Learning

Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., Wu, J., Wohlhart, P., Welker, S., Wahid, A., et al.: Rt-2: Vision-language-action models transfer web knowledge to robotic control. In: Conference on Robot Learning. pp. 2165–2183. PMLR (2023) 20 L. Kang et al. Supplementary Material A Task Descriptions Table 4 summarizes the five evaluation tasks ...

2023
[73]

type": "CAMERA_ORBIT

Request a different view (RECOMMENDED): {"type": "CAMERA_ORBIT", "params": {"yaw": X.XX, "pitch": X.XX}, "reason": "why this angle helps"}
[74]

Or declare planning complete: PLANNING_COMPLETE <key_observations> Critical findings for execution: - Object positions and orientations - Chosen grasp strategy - Key constraints (clearances, collision avoidance) </key_observations> <checkpoints> Steps requiring verification before proceeding: - CP1: what to verify Position: xyz within 0.02m? CoEnv: Multi-...
[75]

[Robot X] MOVE + ROTATE - reason
[76]

[Robot X] GRASP - CHECKPOINT CP1
[77]

The output format is given in Prompt 4

[Robot X] MOVE (lift) + MOVE (transport) + RELEASE Multi-robot coordination: MERGE synchronized actions into ONE step SEPARATE for different actions or verification </execution_plan> </next_action> Prompt 2:Planning Output Format Execution Phase Prompt.The following box shows the full execution prompt skeleton. The output format is given in Prompt 4. You ...
[78]

Position: error > 0.02m -> MOVE to correct first
[79]

Orientation: error > 0.1 rad -> ROTATE first
[80]

Do NOT combine when visual verification is needed

Visual: CAMERA_ORBIT to confirm object between fingers # Multiple Actions in One Output Combine when safe (e.g., ROTATE -> MOVE -> GRASP). Do NOT combine when visual verification is needed. # Output Format => See Prompt 4 Prompt 3:Execution Phase: Full Prompt Structure <observation> What you see: - Where are the target object and gripper? - Cross-check: d...

Showing first 80 references.