Pith. sign in

REVIEW 3 major objections 5 minor 21 references

HarnessWAM: Bridging Prediction and Deliberation in World Action Models

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A model-external harness turns finite-horizon world-action models into reliable long-horizon robot agents.

desk verdict A well-specified harness for WAMs with strong internal ablations; the SOTA claim is real only if the external baselines were protocol-matched. read the letter →

arxiv 2608.09516 v1 pith:W37F6N6E submitted 2026-08-10 cs.RO

Zhaopeng Gu , Bingke Zhu , Tianxi Lin , Guibo Zhu , Yingying Chen , Kai Wang , Tingyu Yuan , Chaoyang Zhao
show 3 more authors
Zhaowen Li Peng Su Jinqiao Wang
This is my paper · ORCID
classification cs.RO
keywords worldactionmodelsprediction-deliberationgapembodiedtaskplanningrobotmanipulationclosed-loopcontrolscenebelieffailurerecoveryvision-languagemodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that World Action Models (WAMs), which jointly predict physical dynamics and generate robot actions, fail at long-horizon embodied tasks because their predictions are finite-horizon and local; the authors call this the prediction–deliberation gap. To close it, HarnessWAM adds a model-external agentic runtime that keeps an evidence-grounded scene belief and task graph, compiles open-ended language plans into WAM-executable skill sequences, and runs an event-driven dual-timescale loop of progress monitoring and semantic deliberation. On two benchmarks, RoboMemArena and RoboCerebra Ideal, the framework reports state-of-the-art full-task and subtask success rates of 59.6% and 69.9%, and an SR of 23.7%. If these results hold, the implication is that reliable multi-stage manipulation can come from orchestrating a WAM from the outside, rather than from changes to the model's weights.

What carries the argument

The load-bearing mechanism is the separation of continuous WAM control from discrete task-level deliberation, organized as a harness with four components. First, an evidence-grounded scene belief represents facts as (subject, predicate, object, value, epistemic status, confidence, visual evidence), so occluded objects are not treated as absent. Second, a structured task graph contains motor and cognitive nodes with preconditions, expected effects, termination conditions, and recovery strategies, keeping unresolved entities as symbolic variables until evidence binds them. Third, capability-conditioned executable-space projection maps open-ended VLM plans onto the validated primitive set of the WAM, checking argument types, dependencies, preconditions, single-arm holding state, and acyclicity; semantic nodes that admit a composition of supported primitives are expanded, and infeasible projections return ⊥ to trigger replanning. Fourth, an event-driven, dual-timescale loop lets a lightweight progress estimator provide high-frequency progress and completion cues while the VLM Task Manager deliberates only at milestones, with saved joint and gripper states enabling embodiment-only recovery after local failures.

What would settle it

Re-run PrediMem and GPT-4o Planner + OpenVLA under HarnessWAM's exact protocol (20 rollouts, same initial states, seeds, observation interfaces, and task-level budgets) and check whether HarnessWAM's 59.6% and 23.7% margins persist; if they shrink or reverse, the state-of-the-art claim fails.

Watch

Extended reading notes

Core claim

The central claim is that finite-horizon prediction and action generation are insufficient for tasks that need global planning, cross-stage state maintenance, execution verification, and failure recovery, and that this gap can be closed by a structured runtime external to the WAM. HarnessWAM maintains a scene belief whose facts carry epistemic status (observed, inferred, unknown) and a task graph with symbolic variables bound only when visual evidence supports them. A deterministic projection compiles VLM-proposed semantic plans into the subset of primitives the WAM can actually execute, enforcing dependencies, argument types, and embodiment constraints. During execution, a lightweight progress estimator feeds a fast loop while a VLM Task Manager deliberates at milestones, deciding to advance, observe, replan, recover, or terminate; recovery restores only the robot embodiment, keeping scene knowledge and task memory. The paper reports that this design achieves the best scores on RoboMemArena and RoboCerebra Ideal, and its ablations attribute the largest gain to executable-space projection.

Load-bearing premise

The published baseline numbers are taken from the benchmarks' own evaluations and are assumed comparable to HarnessWAM's 20-rollout protocol with matched initial states, seeds, observation interfaces, and task-level budgets.

Editorial extensions

If this is right

  • Static decomposition of a task at initialization is not enough: WAM + Static Plan beats WAM + Whole Task by 3.5 points full-task, but HarnessWAM adds another 11.7 points, so persistent task state and closed-loop management are necessary.
  • Executable-space projection is the largest single contributor; removing it drops full-task success from 59.6% to 18.5%, and plan-level diagnostics show it raises plan executability from 42.3% to 72.9%.
  • Progress-only switching, without semantic verification of outcomes, keeps subtask success similar but lowers full-task success from 59.6% to 55.4%, with a notable drop on sequential execution.
  • Local recovery that preserves scene knowledge and task memory improves average full-task success by 5.4 points, with the largest effect on sequential tasks.
  • The framework works on a fully observable benchmark (RoboCerebra Ideal) as well as a memory-heavy one, indicating benefits beyond explicit memory recovery.

Reading between the lines

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

  • This harness design could generalize to other finite-horizon visuomotor policies beyond WAMs, though the paper only tests it with one underlying WAM, so the generality is an open implication rather than a demonstrated result.
  • Capability-conditioned executable-space projection could serve as a pre-execution vetting step for VLM planners in robotics generally; the paper's plan-level diagnostics show raw VLM plans are only 13.8% executable, suggesting many wasted rollouts could be prevented before execution.
  • The dual-timescale loop may yield compute savings in addition to reliability, since the VLM Task Manager is invoked only at milestones rather than at every step; the paper does not report compute cost, making this a testable extension.
  • The epistemic-status representation (observed vs inferred vs unknown) could transfer to other partially observable settings such as household robots or autonomous inspection, where distinguishing 'not observed' from 'observed to be false' is critical.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. HarnessWAM proposes a model-external agentic framework that wraps a World Action Model (WAM) with an evidence-grounded scene belief, a structured task graph, a capability-conditioned executable-space projection, and an event-driven dual-timescale execution loop. The Task Manager (a VLM) maintains task state and decides when to advance, observe, replan, recover, or terminate, while a lightweight progress estimator supplies continuous execution evidence. Experiments on RoboMemArena and RoboCerebra Ideal report state-of-the-art full-task and subtask success rates, and same-WAM ablations (WAM + Whole Task, WAM + Static Plan) together with ablation Table 3 indicate that the harness components, especially executable-space projection and progress-conditioned event control, are responsible for large parts of the improvement over the raw WAM.

Significance. If the results hold, the paper makes a valuable contribution: it demonstrates that a finite-horizon WAM can be extended into a persistent embodied agent through a structured external runtime, without modifying the WAM architecture. The internal evidence is strong: the same-WAM baselines isolate the effect of the harness from the underlying policy, and the plan-level diagnostics in Table 4 directly quantify how projection improves syntax, dependency satisfaction, binding, and executability. The deterministic projection and the history-invariance constraint are clean, falsifiable design elements. The main weakness is that the abstract's state-of-the-art claim is not fully verifiable as written because the external baselines in Tables 1 and 2 are taken from benchmark evaluations rather than a matched protocol, and no uncertainty estimates are reported.

major comments (3)
  1. [Section 4.1, Tables 1 and 2] The evaluation protocol is not matched to the external baselines. Section 4.1 states that HarnessWAM and the same-WAM diagnostic variants use 20 rollouts per task with matched initial states, seeds, observation interfaces, and task-level execution budgets, while published baseline numbers are 'taken from the corresponding benchmark evaluations.' If those baselines used different rollout counts, initial-state distributions, observation interfaces, or budgets, the reported gains over PrediMem (+21.1/+14.7 on RoboMemArena) and over GPT-4o Planner + OpenVLA (+1.78 on RoboCerebra Ideal) could reflect protocol differences rather than capability. This is particularly acute on RoboCerebra Ideal, where the margin is small and no error bars are provided. The state-of-the-art claim in the abstract is therefore not verifiable from the manuscript as written.
  2. [Tables 1-3] No confidence intervals, standard errors, or significance tests are reported for any of the success rates in Tables 1-3, despite the 20-rollout evaluation protocol. This matters for the small-margin RoboCerebra Ideal comparison (23.70 vs 21.92, a 1.78-point edge) and for ablation differences such as progress-only switching versus full HarnessWAM on full-task success (55.4 vs 59.6). With 20 rollouts per task, binomial sampling noise can easily exceed these margins, so the reader cannot assess whether the reported ordering is stable. The paper should report per-task variance, confidence intervals, or paired statistical tests for at least the same-WAM comparisons and the main RoboCerebra comparison.
  3. [Section 3.3, Eq. (11)-(12)] The executable primitive set P_W is defined as primitives that 'have been empirically validated' under the WAM, but the validation procedure is never described, nor is the actual set of validated primitives used for LingBot-VA reported. Because the executable-space projection is central to the paper's contribution and its ablation (Table 3) shows a 41.1-point drop when removed, the reader cannot assess whether the benefit comes from the projection mechanism itself or from the particular choice of supported primitives. Please specify how skills are validated, list the resulting P_W, and state whether the same set is used across benchmarks and ablations.
minor comments (5)
  1. [Throughout] The notation 'WAM' and 'W AM' is used inconsistently (e.g., the abstract and Section 1 contain both forms). Please standardize the spelling.
  2. [Section 4.1] The sentence 'We report macro-averages across tasks and rollouts' is ambiguous; clarify whether the macro-average is taken over task-level success rates first and then averaged over tasks, or over all task-rollout pairs.
  3. [Section 3.4, Eq. (15)] The six loss weights (lambda_r, lambda_b, lambda_rank, lambda_e, lambda_s, lambda_m) in the progress-estimator objective are introduced but their values or tuning procedure are not reported. Please state the settings or note that they are held fixed, and report how sensitive the event-triggering behavior is to these weights.
  4. [Figure 3] The keyframes would benefit from explicit event labels (e.g., 'evidence binding', 'recovery', 'task advance') so that the qualitative rollout directly illustrates the evidence-retention and recovery claims rather than requiring the reader to infer them from the narrative.
  5. [General] No code, checkpoints, or data-release statement is provided. Adding an availability statement would support reproducibility, especially for the plan-level diagnostics in Table 4.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central claim is empirically supported by external simulator success predicates and controlled same-WAM ablations.

full rationale

The derivation chain in HarnessWAM does not reduce its headline claims to its own construction. Full-task and subtask success are defined by external simulator verification predicates in Eqs. (18)-(20), not by the progress estimator or Task Manager's own outputs. The progress estimator F_phi is a trained module, but the paper explicitly states that 'progress predictions alone never advance the task graph'; task transitions require semantic verification by the Task Manager, and final success is determined by environment predicates via VERIFYGOAL. The executable-space projection is a deterministic plan compiler enforcing capability, dependency, binding, and embodiment constraints; it does not define success. The controlled same-WAM baselines (WAM + Whole Task and WAM + Static Plan) and ablations in Tables 1 and 3 isolate the harness contribution using identical LingBot-VA weights, initial states, and seeds, so the central design claim is not statistically forced by a fitted input. The plan-level diagnosis uses reference decompositions only offline and explicitly states they 'are never provided to HarnessWAM during inference.' No load-bearing self-citation is apparent: the underlying WAM and benchmark citations do not overlap with the present author list, and no uniqueness theorem or prior author-side result is invoked to forbid alternatives. The main legitimate concern is external: published baseline numbers are 'taken from the corresponding benchmark evaluations' and may not be protocol-matched to HarnessWAM's 20-rollout evaluation, but that is an external-validity risk about comparability, not circularity in the derivation.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim relies on domain assumptions about VLM reliability, WAM skill coverage, progress-estimator calibration, and baseline comparability rather than on newly postulated physical entities. The free parameters listed are configuration choices that are not reported in sufficient detail to reproduce the system exactly. No invented physical entities such as new forces, fields, or particles are introduced.

free parameters (5)
  • Progress-estimator loss weights (lambda_r, lambda_b, lambda_rank, lambda_e, lambda_s, lambda_m) = not reported
    Hand-chosen weighting of the six supervision terms in Eq. 15. The progress estimator is load-bearing for event triggering, but the values and any sensitivity analysis are absent.
  • Task-level execution budget Omega = not reported
    The bounded budget in Algorithm 1 controls termination and recovery decisions. No value is given, so the failure definitions cannot be exactly reproduced.
  • Per-node skill execution budget T_k = not reported
    Defined in Section 3.5 as the threshold for emitting failure-handling events. Its value changes how often recovery is triggered, and it is not specified.
  • WAM action horizon H = not reported (inherited from LingBot-VA)
    Action chunk length in Eq. 1 determines how frequently progress estimates are generated. It is treated as a fixed WAM property rather than tuned here.
  • Progress-estimator frame window L = 5 timesteps
    F_phi consumes the latest five dual-view RGB frames. This hyperparameter is stated but not ablated.
assumptions (6)
  • domain assumption Human and robotic manipulation has low-dimensional repetitive primitive structure (Bullock et al.; Moro et al.; Santello et al.; Morrow and Khosla).
    Invoked in Section 3.3 to justify the finite ontology P_star and the capability-conditioned projection. If manipulation is not well covered by these primitive classes, the projection can filter out feasible plans.
  • domain assumption A validated executable primitive set P_W exists and is known for LingBot-VA.
    Eq. 11 assumes the WAM's skills can be enumerated and 'empirically validated'. No procedure or skill-validation results are provided, yet projection correctness depends on this set.
  • domain assumption Qwen3-VL-32B-Instruct can reliably update scene belief, bind variables, and verify outcomes from RGB observations alone.
    The task manager performs all semantic state updates and outcome verification in Sections 3.2 and 3.4. No error analysis or calibration of the VLM's judgments is reported.
  • domain assumption The progress estimator F_phi generalizes from its training distribution to the evaluation episodes and aligns with the benchmark success predicates.
    Events are triggered from F_phi outputs in Eq. 14. If completion likelihood is miscalibrated, the dual-timescale loop still works but at a different operating point; the paper gives no calibration analysis.
  • domain assumption Published external baselines used protocols comparable to the HarnessWAM 20-rollout evaluation.
    The SOTA comparison in Section 4.2 rests entirely on this assumption because the baselines were not rerun under the HarnessWAM protocol.
  • domain assumption Scene facts remain valid when occluded, i.e., the environment does not change except through robot actions.
    Scene belief in Section 3.2 stores closed-drawer facts as observed and retains them after occlusion. This is reasonable in simulation, but it becomes load-bearing in the occlusion task family.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HarnessWAM: Bridging Prediction and Deliberation in World Action Models." pith.science (2026). https://pith.science/paper/W37F6N6E

@misc{pith2026260809516,
  author       = {Pith},
  title        = {Pith review of: HarnessWAM: Bridging Prediction and Deliberation in World Action Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W37F6N6E}},
  note         = {Machine review of arXiv:2608.09516}
}
read the original abstract

World Action Models (WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon prediction and action generation are insufficient for complex embodied tasks that require global planning, cross-stage state maintenance, execution verification, and failure recovery. We refer to this mismatch as the prediction-deliberation gap of WAMs. To address this gap, we propose HarnessWAM, an agentic framework for WAMs. HarnessWAM employs a vision-language-model-based Task Manager to maintain an evidence-grounded scene belief and a structured task graph. A capability-conditioned executable-space projection further constrains open-ended semantic plans into sequences of atomic skills that satisfy task dependencies, embodiment-state constraints, and the capability boundary of the underlying WAM. During execution, HarnessWAM operates through an event-driven, dual-timescale feedback loop: a lightweight progress estimator continuously provides high-frequency execution evidence, while the Task Manager deliberates at salient milestones by jointly considering the current observation, task state, and interaction history to determine whether to advance the task, acquire additional observations, revise the plan, or initiate local recovery. This mechanism enables the robot to recover its state after a subtask failure and resume execution without discarding previously acquired scene knowledge. HarnessWAM achieves state-of-the-art full-task and subtask success rates of 59.6% and 69.9% on RoboMemArena, and an SR of 23.7% on RoboCerebra Ideal. These results demonstrate that model-external structured state maintenance and closed-loop agentic decision making can effectively extend the local control capabilities of WAMs into embodied task execution that is plannable, verifiable, and recoverable.

Figures

Figures reproduced from arXiv: 2608.09516 by the authors.

Figure 1
Figure 1. Prediction–deliberation gap in a memory-dependent task. A conventional WAM leaves the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of HarnessWAM. The VLM Task Manager converts a global instruction and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Selected keyframes from a representative HarnessWAM rollout on RoboMemArena Task 4. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 3 canonical work pages

  1. [1]

    Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress.arXiv preprint arXiv:2410.04640,

    Christopher Agia, Rohan Sinha, Jingyun Yang, Zi-ang Cao, Rika Antonova, Marco Pavone, and Jean- nette Bohg. Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress.arXiv preprint arXiv:2410.04640,

    arXiv

  2. [5]

    Robomme: Benchmarking and understanding memory for robotic generalist policies.arXiv preprint arXiv:2603.04639,

    Yinpei Dai, Hongze Fu, Jayjun Lee, Yuejiang Liu, Haoran Zhang, Jianing Yang, Chelsea Finn, Nima Fazeli, and Joyce Chai. Robomme: Benchmarking and understanding memory for robotic generalist policies.arXiv preprint arXiv:2603.04639,

    arXiv

  3. [10]

    Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246,

    13 HarnessW AM: Bridging Prediction and Deliberation in World Action Models Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246,

    arXiv

  4. [11]

    Robomemarena: A comprehensive and challenging robotic memory benchmark.arXiv preprint arXiv:2605.10921,

    Huashuo Lei, Wenxuan Song, Huarui Zhang, Jieyuan Pei, Jiayi Chen, Haodong Yan, Han Zhao, Pengxiang Ding, Zhipeng Zhang, Lida Huang, et al. Robomemarena: A comprehensive and challenging robotic memory benchmark.arXiv preprint arXiv:2605.10921,

    arXiv

  5. [12]

    Causal world modeling for robot control.arXiv preprint arXiv:2601.21998,

    Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, et al. Causal world modeling for robot control.arXiv preprint arXiv:2601.21998,

    arXiv

  6. [13]

    Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635,

    Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, et al. Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635,

    arXiv

  7. [16]

    Scaling up memory for robotic control via experience retrieval

    Ajay Sridhar, Jennifer Pan, Satvik Sharma, and Chelsea Finn. Scaling up memory for robotic control via experience retrieval. InInternational Conference on Learning Representations, volume 2026, pp. 97142–97166,

    work page 2026

  8. [17]

    Himem-wam: Hierarchical memory-gated world action models for robotic manipulation.arXiv preprint arXiv:2606.10363,

    Xiaoquan Sun, Ruijian Zhang, Chen Cao, Yihan Sun, Jiahui Chen, Zetian Xu, Bo Chen, Haijier Chen, Zhen Yang, Jiarun Zhu, et al. Himem-wam: Hierarchical memory-gated world action models for robotic manipulation.arXiv preprint arXiv:2606.10363,

    arXiv

Show all 21 references
  1. [18]

    Memorywam: Efficient world action modeling with persistent memory.arXiv preprint arXiv:2606.20562,

    Sizhe Yang, Juncheng Mu, Tianming Wei, Chenhao Lu, Xiaofan Li, Linning Xu, Zhengrong Xue, Zhecheng Yuan, Dahua Lin, Jiangmiao Pang, et al. Memorywam: Efficient world action modeling with persistent memory.arXiv preprint arXiv:2606.20562,

    arXiv

  2. [19]

    React: Synergizing reasoning and acting in language models

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InNeurIPS 2022 Foundation Models for Decision Making Workshop,

    2022

  3. [20]

    World action models are zero-shot policies.arXiv preprint arXiv:2602.15922,

    Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, et al. World action models are zero-shot policies.arXiv preprint arXiv:2602.15922,

    arXiv

  4. [21]

    Fast-wam: Do world action models need test-time future imagination?arXiv preprint arXiv:2603.16666,

    Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-wam: Do world action models need test-time future imagination?arXiv preprint arXiv:2603.16666,

    arXiv

  5. [1997]

    Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning

    Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, and Niko Suenderhauf. Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning. arXiv preprint arXiv:2307.06135,

    arXiv

  6. [1998]

    Memoryvla: Perceptual-cognitive memory in vision-language- action models for robotic manipulation

    Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, and Gao Huang. Memoryvla: Perceptual-cognitive memory in vision-language- action models for robotic manipulation. InInternational Conference on Learning Representations...

    2026

  7. [2013]

    Sarm: Stage- aware reward modeling for long horizon robot manipulation.arXiv preprint arXiv:2509.25358,

    Qianzhong Chen, Justin Yu, Mac Schwager, Pieter Abbeel, Yide Shentu, and Philipp Wu. Sarm: Stage- aware reward modeling for long horizon robot manipulation.arXiv preprint arXiv:2509.25358,

    arXiv

  8. [2017]

    Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning

    Frederik Ebert, Sudeep Dasari, Alex X Lee, Sergey Levine, and Chelsea Finn. Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning. InConference on robot learning, pp. 983–993. PMLR, 2018a. Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie...

    arXiv

  9. [2022]

    V oxposer: Composable 3d value maps for robotic manipulation with language models.arXiv preprint arXiv:2307.05973,

    Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, and Li Fei-Fei. V oxposer: Composable 3d value maps for robotic manipulation with language models.arXiv preprint arXiv:2307.05973,

    arXiv

  10. [2023]

    π0.5: a vision-language-action model with open-world generalization.arXiv preprint arXiv:2504.16054,

    Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. π0.5: a vision-language-action model with open-world generalization.arXiv preprint arXiv:2504.16054,

    arXiv

  11. [2024]

    Do as i can, not as i say: Grounding language in robotic affordances.arXiv preprint arXiv:2204.01691,

    12 HarnessW AM: Bridging Prediction and Deliberation in World Action Models Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. Do as i can, not as i say: Grounding langua...

    arXiv

  12. [2025]

    Rh20t-p: A primitive-level robotic dataset towards composable generalization agents.arXiv preprint arXiv:2403.19622,

    Zeren Chen, Zhelun Shi, Xiaoya Lu, Lehan He, Sucheng Qian, Zhenfei Yin, Wanli Ouyang, Jing Shao, Yu Qiao, Cewu Lu, et al. Rh20t-p: A primitive-level robotic dataset towards composable generalization agents.arXiv preprint arXiv:2403.19622,

    arXiv

  13. [2026]

    Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608,

    Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608,

    arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.