arxiv: 2603.10126 · v2 · submitted 2026-03-10 · 💻 cs.RO · cs.AI

Recognition: 3 theorem links

· Lean Theorem

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models

Yutong Hu , Jan-Nico Zaech , Nikolay Nikolov , Yuanqi Yao , Sombit Dey , Giuliano Albanese , Renaud Detry , Luc Van Gool

show 1 more author

Danda Paudel

Authors on Pith no claims yet

Pith reviewed 2026-05-15 12:46 UTC · model grok-4.3

classification 💻 cs.RO cs.AI

keywords autoregressive action expertvision-language-action modelsrobot manipulationcontext-aware policiesre-anchoring mechanismspatio-temporal consistencylong-lived memory

0 comments

The pith

An autoregressive action expert generates continuous causal action sequences in vision-language-action models by maintaining long-lived memory and re-anchoring for perception delays.

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

The paper proposes a standalone autoregressive Action Expert that produces actions as a continuous causal sequence conditioned on refreshable vision-language prefixes. Unlike existing VLA models and diffusion policies that reset temporal context with each observation, this expert keeps its own history through long-lived memory and remains inherently context-aware. The structure tackles the frequency mismatch between fast control and slow reasoning, supports independent pretraining of the action component, and integrates modularly with heavy perception backbones. A re-anchoring mechanism synchronizes the modalities by accounting for perception staleness during training and inference. Experiments on simulated and real-robot tasks show the method can replace chunk-based heads while delivering superior history awareness and smoother trajectories at comparable success rates.

Core claim

We introduce a true autoregressive Action Expert that generates actions as a continuous causal sequence while conditioning on refreshable vision-language prefixes. In contrast to reactive models that reset context with each new observation, the expert maintains its own history through a long-lived memory and is inherently context-aware. A re-anchoring mechanism mathematically accounts for perception staleness to synchronize asynchronous hybrid modalities. This design enables efficient independent pretraining of kinematic syntax and modular integration with perception backbones, naturally ensuring spatio-temporally consistent action generation across frames.

What carries the argument

The autoregressive Action Expert, which generates actions as a continuous causal sequence conditioned on refreshable vision-language prefixes and maintains history via long-lived memory, synchronized by a re-anchoring mechanism that compensates for perception staleness.

If this is right

The action expert can be pretrained independently on kinematic data before modular integration with any perception backbone.
Action trajectories become inherently smoother and more spatio-temporally consistent because context is preserved across frames rather than reset.
The same expert architecture works for both specialist policies and generalist VLAs without task-specific redesign of the action head.
Re-anchoring during both training and inference allows the model to handle asynchronous vision-language and control rates without explicit synchronization modules.

Where Pith is reading between the lines

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

Longer-horizon tasks may become feasible because the causal memory structure avoids the quadratic cost of ever-growing context windows in perception models.
The separation of action syntax from perception semantics could support transfer of the pretrained expert across robot embodiments with minimal fine-tuning.
In real-world deployment the re-anchoring step might be extended to include uncertainty estimates from the perception model to further reduce error accumulation.

Load-bearing premise

The re-anchoring mechanism can reliably compensate for perception staleness across varying control frequencies and dynamic scenes without introducing cumulative errors or requiring extensive per-task tuning.

What would settle it

Measure whether action smoothness and task success rates degrade when control frequency increases substantially beyond training conditions or when scenes change rapidly enough to make perception prefixes stale for multiple steps.

Figures

Figures reproduced from arXiv: 2603.10126 by Danda Paudel, Giuliano Albanese, Jan-Nico Zaech, Luc Van Gool, Nikolay Nikolov, Renaud Detry, Sombit Dey, Yuanqi Yao, Yutong Hu.

**Figure 1.** Figure 1: (a) The prevalent approach in Vision-Language-Action models predicts action chunks based only on the current snapshot of information. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Performance Overview. (a) Quantitative Results: In both generalist (left) and specialist (right) benchmarks, AR-VLA achieves competitive or superior performance compared to state-of-the-art policies, including OpenVLA, Flow-Matching (FM), ACT, and Diffusion Policy (DP), details in Sec.IV-A. (b) Trajectory Quality: Qualitative visualization of joint trajectories over time reveals that AR-VLA produces signif… view at source ↗

**Figure 3.** Figure 3: The AR-VLA Framework. The system bridges an VLM backbone with a autoregressive Action Expert asynchronously. Atemporal features from the VLM are explicitly injected with temporal context via Dynamic Temporal Re-anchoring (DTR). Within the Hybrid KV Cache, re-anchored VL tokens (green) serve as a semantic prefix to the rolling kinematic history (orange). The Action Expert generates future action sequences … view at source ↗

**Figure 4.** Figure 4: Heterogeneous FIFO Update Rules for the Hybrid KV Cache. The framework manages memory through two distinct queueing strategies to ensure efficient context utilization. The VL Stream (green) operates as a short-lived, block-wise FIFO: In contrast, the Action Stream (orange) maintains a token-wise rolling FIFO, continuously appending the single latest action prediction while evicting the oldest kinematic st… view at source ↗

**Figure 5.** Figure 5: Simulation benchmarks setups. We do simulation evaluation spanning generalist and specialist policies, with diverse embodiment, action space, and task. Fast tokens (i.e., a reproduced Pi-0-FAST*[31]), one predicts action chunks through multi-step flow matching (Pi-0.5*[15]), and one predicts actions autoregressively with standard nextaction prediction loss (AR-VLA, Ours). We evaluate on the SimplerEnv sim… view at source ↗

**Figure 6.** Figure 6: BridgeV2 pretraining to real-world WidowX Zero-Shot Performance Comparison. As a property of VLA models, the released weights work out-of-the-box without an accurate requirement for the camera pose. We set the camera pose so that all methods reach a 100% success rate on an easy in-distribution task, then test them zero-shot on challenging tasks. Details of experiment protocol in Appendix. Paligemma 3B + AR… view at source ↗

**Figure 7.** Figure 7: Smoothness Visualization. Joint states captured from success execution for the same task. suggests that architectural simplicity may be a key factor for consistent task-agnostic performance, whereas more complex methods like Diffusion Policy excel on specific tasks but struggle on others. Our observations align with similar results that have been reported by ARP [46]. B. Efficiency and Smoothness Analysis … view at source ↗

**Figure 8.** Figure 8: History-Awareness Evaluation. PushT2 requires visiting both goals, but which goal has been visited is unobservable midway. Stack3 requires stacking cups over a battery that becomes occluded. Both task require memory of unobservable past states. H donates the context window length of AR-VLA. Details about task defination, data collection, training and execution in Appendix. D. Ablations on Design Decisions … view at source ↗

**Figure 9.** Figure 9: Three different Action Experts sharing the same architecture and V-L backbone. The same networks are trained and [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

**Figure 10.** Figure 10: AR Actor that share the exact same size and architecture of Action Chunking Transformer, the same decoders are [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Demonstration collection for history-aware tasks. (a) The [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

**Figure 12.** Figure 12: Typical cases during PushT2 task execution. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗

**Figure 13.** Figure 13: Typical cases during Stack3 task execution. [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗

**Figure 14.** Figure 14: AR-VLA Zero-shot task execution in SIMPLER simulator. [PITH_FULL_IMAGE:figures/full_fig_p018_14.png] view at source ↗

**Figure 15.** Figure 15: AR-VLA Zero-shot task execution in real world. [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗

**Figure 16.** Figure 16: AR-Actor specialist task execution. F. Discussion and Limitations While AR-VLA demonstrates significant improvements in temporal consistency and history awareness, several challenges and avenues for future research remain. Compounding Errors and OOD States. A primary limitation of the autoregressive paradigm in robotics is its sensitivity to Out-of-Distribution (OOD) trajectories. Unlike reactive policies… view at source ↗

read the original abstract

We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refreshable vision-language prefixes. In contrast to existing Vision-Language-Action (VLA) models and diffusion policies that reset temporal context with each new observation and predict actions reactively, our Action Expert maintains its own history through a long-lived memory and is inherently context-aware. This structure addresses the frequency mismatch between fast control and slow reasoning, enabling efficient independent pretraining of kinematic syntax and modular integration with heavy perception backbones, naturally ensuring spatio-temporally consistent action generation across frames. To synchronize these asynchronous hybrid V-L-A modalities, we utilize a re-anchoring mechanism that mathematically accounts for perception staleness during both training and inference. Experiments on simulated and real-robot manipulation tasks demonstrate that the proposed method can effectively replace traditional chunk-based action heads for both specialist and generalist policies. AR-VLA exhibits superior history awareness and substantially smoother action trajectories while maintaining or exceeding the task success rates of state-of-the-art reactive VLAs. Overall, our work introduces a scalable, context-aware action generation schema that provides a robust structural foundation for training effective robotic policies. Code and Videos available at https://arvla.insait.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

The standalone AR action expert with long-lived memory and re-anchoring is a clean structural split worth testing, but the robustness of the re-anchoring step is still thin on evidence.

read the letter

The main new piece here is the decision to pull action generation out into its own autoregressive model that keeps a persistent memory across steps instead of resetting with each fresh vision-language prefix. The re-anchoring step is meant to correct for the staleness that comes from slower perception rates, and the authors argue this gives better history awareness and smoother trajectories than the usual chunk-based heads in VLAs or diffusion policies. They also highlight the practical upside of pretraining the action part independently before plugging in heavy perception backbones. That modular angle is the part that feels useful on its own terms. The experiments on simulated and real manipulation tasks are presented as showing comparable or better success rates with visibly smoother outputs, which lines up with the claim that the structure helps with spatio-temporal consistency. The idea sits in a space where most current work still ties action prediction tightly to the latest observation, so separating the kinematic modeling this way is a straightforward but distinct move. The soft spots sit mostly around the re-anchoring mechanism itself. It is described at a high level as mathematically handling staleness during training and inference, yet there is little visible detail on the exact state updates or how error accumulation is controlled when control rates run several times faster than perception or when the scene is changing quickly. Without those specifics or targeted ablations, it is hard to tell whether the smoother trajectories come from the autoregressive memory or from the re-anchoring simply masking issues that could surface in more dynamic regimes. The results section also stays light on exact baseline lists, statistical tests, and quantitative comparisons to long-context chunk alternatives, which leaves the performance edge less anchored than it could be. This is the kind of architectural paper that robotics and embodied-AI groups would want to read if they are already working on scalable VLA policies. A reader looking for concrete alternatives to reactive heads would get value from the template even if they end up adapting pieces of it. It deserves a serious referee because the structural separation is clear enough that proper implementation details and additional controls would let the community judge whether the re-anchoring actually delivers the claimed consistency gains.

Referee Report

3 major / 2 minor

Summary. The paper proposes AR-VLA, a Vision-Language-Action architecture centered on a standalone autoregressive Action Expert that produces actions as a continuous causal sequence conditioned on periodically refreshed vision-language prefixes. Unlike chunk-based or reactive VLA baselines, the expert maintains a long-lived internal memory for inherent temporal context, uses a re-anchoring step to compensate for perception staleness, and is claimed to resolve control-reasoning frequency mismatch while enabling modular pretraining and smoother trajectories at comparable success rates on simulated and real-robot manipulation tasks.

Significance. If the empirical claims hold after the requested clarifications, the work would supply a structurally cleaner alternative to chunked action heads in VLA models, with potential benefits for independent action pretraining and long-horizon consistency in hybrid perception-control loops.

major comments (3)

[§3.2] §3.2 (Re-anchoring mechanism): the description remains qualitative ('mathematically accounts for perception staleness') with no explicit state-update equations, error-propagation bounds, or analysis of drift under 5–10× control-to-perception rate ratios; this is load-bearing for the central claim that AR inherently outperforms long-context chunking.
[§4] §4 (Experiments): the abstract and results claim 'superior history awareness and substantially smoother trajectories' yet provide neither quantitative metrics (e.g., jerk, trajectory smoothness norms) nor statistical tests comparing against sufficiently long-context chunk baselines; the reported success-rate parity is therefore difficult to interpret.
[§4.2] §4.2 (Ablations): no ablation isolating the contribution of the long-lived memory versus the re-anchoring adjustment is presented, leaving open whether the observed smoothness arises from the autoregressive structure itself or from the additional synchronization step.

minor comments (2)

[§3.1] Notation for the action sequence length and memory horizon is introduced without a clear table or diagram relating them to control frequency.
[Figure 3] Figure 3 (trajectory visualizations) would benefit from overlaid velocity or acceleration plots to substantiate the 'smoother' claim beyond visual inspection.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We have carefully considered each major comment and provide point-by-point responses below. All requested clarifications and additions will be incorporated in the revised version to strengthen the presentation of the re-anchoring mechanism, experimental metrics, and ablations.

read point-by-point responses

Referee: [§3.2] §3.2 (Re-anchoring mechanism): the description remains qualitative ('mathematically accounts for perception staleness') with no explicit state-update equations, error-propagation bounds, or analysis of drift under 5–10× control-to-perception rate ratios; this is load-bearing for the central claim that AR inherently outperforms long-context chunking.

Authors: We agree that the re-anchoring description would benefit from greater mathematical detail. In the revised manuscript we will add the explicit state-update equations that define how the Action Expert's internal hidden state is adjusted upon receipt of each refreshed vision-language prefix. We will also derive and report error-propagation bounds together with an empirical analysis of state drift for control-to-perception rate ratios between 5× and 10×, using both simulated rollouts and real-robot data. These additions will directly support the claim that the autoregressive structure with re-anchoring provides advantages over long-context chunking. revision: yes
Referee: [§4] §4 (Experiments): the abstract and results claim 'superior history awareness and substantially smoother trajectories' yet provide neither quantitative metrics (e.g., jerk, trajectory smoothness norms) nor statistical tests comparing against sufficiently long-context chunk baselines; the reported success-rate parity is therefore difficult to interpret.

Authors: We acknowledge that quantitative smoothness metrics and rigorous statistical comparisons are needed. In the revision we will report mean jerk, integrated squared jerk, and trajectory curvature norms for all methods. We will also extend the chunk-based baselines to context lengths that match the effective history maintained by the AR expert and include statistical significance tests (paired t-tests across 5 random seeds) on both success rates and smoothness metrics. These changes will allow readers to interpret the smoothness and history-awareness claims beyond success-rate parity. revision: yes
Referee: [§4.2] §4.2 (Ablations): no ablation isolating the contribution of the long-lived memory versus the re-anchoring adjustment is presented, leaving open whether the observed smoothness arises from the autoregressive structure itself or from the additional synchronization step.

Authors: We thank the referee for this suggestion. We will add a dedicated ablation study that (i) removes the long-lived memory (resetting the expert state at each prefix refresh) and (ii) disables the re-anchoring adjustment while keeping the autoregressive structure. Results will be reported on both simulated and real-robot tasks, showing the individual and joint contributions of each component to trajectory smoothness and task success. This will clarify whether the observed benefits derive primarily from the autoregressive formulation or from the synchronization mechanism. revision: yes

Circularity Check

0 steps flagged

No circularity: AR-VLA proposal is a structural architecture change grounded in standard autoregressive modeling

full rationale

The paper introduces a standalone autoregressive Action Expert with long-lived memory and a re-anchoring mechanism to address frequency mismatch between perception and control. This is framed as an architectural alternative to chunk-based reactive heads, with claims supported by experimental results on simulated and real-robot tasks rather than any reduction of outputs to fitted parameters or self-referential definitions. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are invoked in the provided text; the re-anchoring is described at a high level as mathematically compensating for staleness without equations that collapse back to the input assumptions by construction. The derivation chain remains self-contained against external benchmarks of autoregressive sequence modeling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal assumes standard properties of autoregressive modeling for sequential data and the feasibility of modular separation between perception and action components; no new entities are postulated and no free parameters are explicitly introduced in the abstract description.

axioms (1)

domain assumption Autoregressive sequence models can capture kinematic syntax sufficiently well for independent pretraining of action generation
Invoked to justify separate pretraining of the Action Expert from perception backbones.

pith-pipeline@v0.9.0 · 5553 in / 1333 out tokens · 54495 ms · 2026-05-15T12:46:38.297240+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

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

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We define the AR Actor as a sequence model where one of the prediction dependencies is the continuous kinematic history... Par(τ) = ∏ P(a_t | Φ(v_i,l), a_<t, s_<t)
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean embed_injective unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Dynamic Temporal Re-anchoring (DTR)... assigns fixed index n corresponding to the timestep when the image was captured... Score(q_m, k_VL_n) = q^T R(m-n) k_VL
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Phase 1: Action-Only Pretraining... L_Phase1 = ∑ L(x_t | x_<t)

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 1 Pith paper

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

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
cs.RO 2026-05 unverdicted novelty 6.0

RoboMemArena is a new large-scale robotic memory benchmark with real-world tasks, and PrediMem is a dual VLA system that outperforms baselines by managing memory buffers with predictive coding.

Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages · cited by 1 Pith paper · 9 internal anchors

[1]

InThe Fourteenth International Conference on Learning Representations, October 2025

LeRobot: An Open-Source Library for End-to-End Robot Learning. InThe Fourteenth International Conference on Learning Representations, October 2025

work page 2025
[2]

Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Haus- man, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ru- ano, Kyle Jeffrey, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kua...

work page 2022
[3]

PaliGemma: A versatile 3B VLM for transfer

Lucas Beyer, Andreas Steiner, Andr ´e Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer.arXiv preprint arXiv:2407.07726, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[4]

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Johan Bjorck, Fernando Casta ˜neda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[5]

InRobotics: Science and Systems, 2025

Kevin Black, Noah Brown, Danny Driess, Adnan Es- mail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al.\pi 0 : A vision-language-action flow model for general robot control. InRobotics: Science and Systems, 2025

work page 2025
[6]

Real-Time Execution of Action Chunking Flow Policies

Kevin Black, Manuel Y Galliker, and Sergey Levine. Real-time execution of action chunking flow policies. arXiv preprint arXiv:2506.07339, 2025

work page internal anchor Pith review arXiv 2025
[7]

RT-1: Robotics Transformer for Real-World Control at Scale

Anthony Brohan, Noah Brown, Justice Carbajal, Yev- gen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang- Huei Lee, Sergey Levine, Yao Lu, Utsav Malla,...

work page internal anchor Pith review Pith/arXiv arXiv 2022
[8]

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-V oss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christo- pher Hesse, Mark Chen, Eric Sigler, Mateusz Lit...

work page internal anchor Pith review Pith/arXiv arXiv 2005
[9]

Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, page 02783649241273668, 2023

Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of Robotics Research, page 02783649241273668, 2023

work page 2023
[10]

From play to policy: Conditional behavior generation from uncurated robot data.arXiv preprint arXiv:2210.10047, 2022

Zichen Jeff Cui, Yibin Wang, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. From play to policy: Conditional behavior generation from uncurated robot data.arXiv preprint arXiv:2210.10047, 2022

work page arXiv 2022
[11]

Revla: Reverting visual domain limitation of robotic foundation models, 2024

Sombit Dey, Jan-Nico Zaech, Nikolay Nikolov, Luc Van Gool, and Danda Pani Paudel. Revla: Reverting visual domain limitation of robotic foundation models, 2024. URL https://arxiv.org/abs/2409.15250

work page arXiv 2024
[12]

PaLM-E: An Embodied Multimodal Language Model

Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Ayzaan Wahid, Jonathan Tomp- son, Quan Vuong, Tianhe Yu, Wenlong Huang, et al. Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[13]

Rvt2: Learning precise manipu- lation from few demonstrations.RSS, 2024

Ankit Goyal, Valts Blukis, Jie Xu, Yijie Guo, Yu-Wei Chao, and Dieter Fox. Rvt2: Learning precise manipu- lation from few demonstrations.RSS, 2024

work page 2024
[14]

Hausknecht and Peter Stone

Matthew J. Hausknecht and Peter Stone. Deep recurrent q-learning for partially observable mdps. InAAAI Fall Symposia, volume 45, page 141, 2015

work page 2015
[15]

Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Es- mail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y . Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pert...

work page 2025
[16]

Thinking, fast and slow.Farrar, Straus and Giroux, 2011

Daniel Kahneman. Thinking, fast and slow.Farrar, Straus and Giroux, 2011

work page 2011
[17]

Vision-language-action models for robotics: A review towards real-world applications

Kento Kawaharazuka, Jihoon Oh, Jun Yamada, Ingmar Posner, and Yuke Zhu. Vision-language-action models for robotics: A review towards real-world applications. IEEE Access, 13:162467–162504, 2025. doi: 10.1109/ ACCESS.2025.3609980

work page arXiv 2025
[18]

arXiv preprint arXiv:1911.00172 , year=

Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. Generalization through memorization: Nearest neighbor language models.arXiv preprint arXiv:1911.00172, 2019

work page arXiv 1911
[19]

Open- vla: An open-source vision-language-action model

Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Open- vla: An open-source vision-language-action model. In Conference on Robot Learning, 2024

work page 2024
[20]

Fine- tuning vision-language-action models: Optimizing speed and success

Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine- tuning vision-language-action models: Optimizing speed and success. InRobotics: Science and Systems, 2025

work page 2025
[21]

Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto

Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. Behavior generation with latent actions. InInternational Conference on Machine Learning, 2024

work page 2024
[22]

Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K¨uttler, Mike Lewis, Wen-tau Yih, Tim Rockt ¨aschel, et al. Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020

work page 2020
[23]

CogACT: A Foundational Vision- Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation, November 2024

Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, Xiaofan Wang, Bei Liu, Jianlong Fu, Jianmin Bao, Dong Chen, Yuanchun Shi, Jiaolong Yang, and Baining Guo. CogACT: A Foundational Vision- Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation, November 2024

work page 2024
[24]

CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation.arXiv preprint arXiv:2411.19650, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[25]

Evaluating Real-World Robot Manipulation Policies in Simulation, May 2024

Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating Real-World Robot Manipulation Policies in Simulation, May 2024

work page 2024
[26]

FASTer: Toward Efficient Autoregressive Vision Lan- guage Action Modeling via Neural Action Tokenization, December 2025

Yicheng Liu, Shiduo Zhang, Zibin Dong, Baijun Ye, Tianyuan Yuan, Xiaopeng Yu, Linqi Yin, Chenhao Lu, Junhao Shi, Luca Jiang-Tao Yu, Liangtao Zheng, Tao Jiang, Jingjing Gong, Xipeng Qiu, and Hang Zhao. FASTer: Toward Efficient Autoregressive Vision Lan- guage Action Modeling via Neural Action Tokenization, December 2025

work page 2025
[27]

Omnisat: Compact action token, faster auto regres- sion, 2025

Huaihai Lyu, Chaofan Chen, Senwei Xie, Pengwei Wang, Xiansheng Chen, Shanghang Zhang, and Changsheng Xu. Omnisat: Compact action token, faster auto regres- sion, 2025. URL https://arxiv.org/abs/2510.09667

work page arXiv 2025
[28]

HAMLET: Switch Your Vision-Language-Action Model into a History-Aware Policy

Myungkyu Koo, Daewon Choi, Taeyoung Kim, Kyung- min Lee, Changyeon Kim, Younggyo Seo, Jinwoo Shin. HAMLET: Switch Your Vision-Language-Action Model into a History-Aware Policy. InThe Fourteenth Interna- tional Conference on Learning Representations, October 2025

work page 2025
[29]

Octo: An open-source generalist robot policy

Octo Model Team, Dibya Ghosh, Homer Walke bit, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy. InConference on Robot Learning, 2024

work page 2024
[30]

Stabilizing transformers for reinforcement learning

Emilio Parisotto, Francis Song, Jack Rae, Razvan Pas- canu, Caglar Gulcehre, Siddhant Jayakumar, Max Jader- berg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, et al. Stabilizing transformers for reinforcement learning. InInternational Conference on Machine Learning, pages 7487–7498. PMLR, 2020

work page 2020
[31]

FAST: Efficient Action Tokenization for Vision-Language-Action Models

Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokeniza- tion for vision-language-action models.arXiv preprint arXiv:2501.09747, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[32]

SpatialVLA: Explor- ing Spatial Representations for Visual-Language-Action Model, May 2025

Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu, Bin Zhao, Dong Wang, and Xuelong Li. SpatialVLA: Explor- ing Spatial Representations for Visual-Language-Action Model, May 2025

work page 2025
[33]

Flower: Democratizing generalist robot policies with efficient vision-language-flow models

Moritz Reuss, Hongyi Zhou, Marcel R ¨uhle, ¨Omer Erdinc ¸ Ya˘gmurlu, Fabian Otto, and Rudolf Lioutikov. Flower: Democratizing generalist robot policies with efficient vision-language-flow models. In Joseph Lim, Shuran Song, and Hae-Won Park, editors,Proceedings of The 9th Conference on Robot Learning, volume 305 ofProceed- ings of Machine Learning Researc...

work page 2025
[34]

Behavior transformers: Cloning k modes with one stone

Nur Muhammad Shafiullah, Zichen Cui, Ariuntuya Arty Altanzaya, and Lerrel Pinto. Behavior transformers: Cloning k modes with one stone. InAdvances in Neu- ral Information Processing Systems, volume 35, pages 22955–22968, 2022

work page 2022
[35]

Memoryvla: Perceptual- cognitive memory in vision-language-action models for robotic manipulation.arXiv preprint arXiv:2508.19236, 2025

Hao Shi, Bin Xie, Yingfei Liu, Lin Sun bit, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xi- angyu Zhang, and Gao Huang. Memoryvla: Perceptual- cognitive memory in vision-language-action models for robotic manipulation.arXiv preprint arXiv:2508.19236, 2025

work page arXiv 2025
[36]

Perceiver-actor: A multi-task transformer for robotic ma- nipulation

Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Perceiver-actor: A multi-task transformer for robotic ma- nipulation. InProceedings of the 6th Conference on Robot Learning (CoRL), 2022

work page 2022
[37]

Hume: Introducing system-2 thinking in visual-language-action model.arXiv preprint arXiv:2505.21432, 2025

Haoming Song, Delin Qu, Yuanqi Yao, Qizhi Chen, Qi Lv, Yiwen Tang, Modi Shi, Guanghui Ren, Maoqing Yao, Bin Zhao, et al. Hume: Introducing system-2 thinking in visual-language-action model.arXiv preprint arXiv:2505.21432, 2025

work page arXiv 2025
[38]

Generalist robot manipulation beyond action labeled data

Alexander Spiridonov, Jan-Nico Zaech, Nikolay Nikolov, Luc Van Gool, and Danda Pani Paudel. Generalist robot manipulation beyond action labeled data. In9th Annual Conference on Robot Learning, 2025. URL https://openreview.net/forum?id=ZqBXnR6ppz

work page 2025
[39]

RoFormer: Enhanced Transformer with Rotary Position Embedding

Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. Roformer: Enhanced trans- former with rotary position embedding, 2023. URL https://arxiv.org/abs/2104.09864

work page internal anchor Pith review Pith/arXiv arXiv 2023
[40]

End-to-end memory networks.Advances in Neural Information Processing Systems, 28, 2015

Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. End-to-end memory networks.Advances in Neural Information Processing Systems, 28, 2015

work page 2015
[41]

Octo: An Open-Source Generalist Robot Policy, May 2024

Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Pannag San- keti, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo: An Open-Source Generalist Robot Policy, May 2024

work page 2024
[42]

BridgeData V2: A Dataset for Robot Learning at Scale, January 2024

Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen- Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, and Sergey Levine. BridgeData V2: A Dataset for Robot Learning at Scale, January 2024

work page 2024
[43]

Instructvla: Vision-language-action instruction tuning from understanding to manipulation,

Shuai Yang, Hao Li, Yilun Chen, Bin Wang, Yang Tian, Tai Wang, Hanqing Wang, Feng Zhao, Yiyi Liao, and Jiangmiao Pang. Instructvla: Vision-language-action instruction tuning from understanding to manipulation,

work page
[44]

URL https://arxiv.org/abs/2507.17520

work page arXiv
[45]

Latent action pretraining from videos

Seonghyeon Ye, Joel Jang, Byeongguk Jeon, Se June Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, Lars Liden, Kimin Lee, Jianfeng Gao, Luke Zettlemoyer, Dieter Fox, and Minjoon Seo. Latent action pretraining from videos. InThe Thirteenth International Conference on Learn- ing Representations, 2025. URL https://ope...

work page 2025
[46]

Robotic control via embodied chain-of-thought reasoning

Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine. Robotic control via embodied chain-of-thought reasoning. In8th Annual Conference on Robot Learning, 2024

work page 2024
[47]

Autoregressive ac- tion sequence learning for robotic manipulation, 2025

Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, and Abdeslam Boularias. Autoregressive ac- tion sequence learning for robotic manipulation, 2025. URL https://arxiv.org/abs/2410.03132

work page arXiv 2025
[48]

Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn

Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning Fine-Grained Bimanual Manip- ulation with Low-Cost Hardware, April 2023

work page 2023
[49]

Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn

Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipu- lation with low-cost hardware. InRobotics: Science and Systems, 2023

work page 2023
[50]

BEAST: Efficient tokenization of b-splines encoded action sequences for imitation learning

Hongyi Zhou, Weiran Liao, Xi Huang, Yucheng Tang, Fabian Otto, Xiaogang Jia, Xinkai Jiang, Simon Hilber, Ge Li, Qian Wang, ¨Omer Erdinc ¸ Ya˘gmurlu, Nils Blank, Moritz Reuss, and Rudolf Lioutikov. BEAST: Efficient tokenization of b-splines encoded action sequences for imitation learning. InThe Thirty-ninth Annual Confer- ence on Neural Information Process...

work page 2025
[51]

eggplant in sink

Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language- action models transfer web knowledge to robotic control. InConference on Robot Learning, pages 2165–2183. PMLR, 2023. Appendix Please check the supplementary material for videos for different task, and a...

work page 2023