Pith. sign in

REVIEW 3 major objections 3 minor 1 cited by

Harness VLA claims that a memory-augmented LLM planner can turn a frozen VLA into a retryable contact-rich primitive, matching or surpassing fine-tuned end-to-end policies on perturbed manipulation benchmarks without any VLA fine-tuning.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-02 07:50 UTC pith:S53W3UDI

load-bearing objection Promising framing and the right controls, but the verify-and-retry loop reads the benchmark success signal mid-episode, contradicting the paper's own formalization; headline numbers can't be taken at face value until that is resolved. the 3 major comments →

arxiv 2607.08448 v3 pith:S53W3UDI submitted 2026-07-09 cs.RO

Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents

Yixian Zhang , Huanming Zhang , Feng Gao , Xiao Li , Zhihao Liu , Chunyang Zhu , Jiaxing Qiu , Yuchen Yan
show 8 more authors
Jiyuan Liu Wenhao Tang Zhengru Fang Yi Nie Changxu Wei Yu Wang Wenbo Ding Chao Yu
This is my paper
classification cs.RO
keywords vision-language-action modelsfrozen policyLLM plannerprimitive librarymemory-guided agentrobot manipulationfew-shot re-groundingLIBERO-Pro
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper is trying to establish that the right way to deploy a pretrained vision-language-action model is not to fine-tune it or give it more skills, but to surround it with a planner that learns how to use a fixed, small set of primitives. It claims that by keeping the VLA frozen and invoking it only for local contact-rich phases—grasping, placing, fixture actuation—while an LLM planner handles semantic grounding, staging, transport, and re-staging, a system can operate far outside the VLA's original trajectory distribution. The reported evidence is a series of large gains on perturbed tabletop, kitchen, and bimanual benchmarks, with the same frozen backbone improving from 50.0% to 82.4% on LIBERO-Pro and from 50.4% to 58.4% on RoboTwin C2R. A sympathetic reader would care because the result suggests that robustness to deployment shifts may be a planner problem rather than a policy problem.

Core claim

The central discovery is an asymmetric division of labor: a pretrained VLA is strong exactly where analytic controllers are weak—irregular grasping, constrained placement, articulated objects—but it fails when asked to absorb semantic redirection, spatial relocation, and long-horizon composition. Harness VLA therefore exposes the frozen VLA through a single primitive, VLA ACT, which the planner invokes in short, retryable bursts, and handles everything else with a fixed library of analytic primitives. Two memories make this work: Task Specific Memory stores a parameterized trace of a successful primitive sequence from a single reference seed, and Global Memory stores reusable success rules a

What carries the argument

The load-bearing machinery is the agentic harness: a turn-based loop in which an LLM planner issues one structured JSON primitive call per step, the environment executes it and returns refreshed RGB-D and proprioceptive observations, and the planner decides whether to continue, re-stage, or retry. The primitive library is fixed and small: analytic primitives for transport, posture, navigation, and release, plus VLA ACT, which wraps the frozen VLA and lets the planner supply a prompt, a chunk budget, and a stop predicate. Task Specific Memory and Global Memory supply the planner with a reusable procedural skeleton and cross-task operating rules, so the planner learns the operating range of ea

Load-bearing premise

The load-bearing premise is that the benchmark's completion signal can be read and checked mid-episode to drive retries, even though the paper formally describes it as a sparse end-of-episode signal.

What would settle it

Log every read of the benchmark success predicate during recorded rollouts. If the planner never evaluates it before episode termination, the described mid-episode verify-and-retry mechanism cannot be operating, and the gains must be explained by something else (e.g., replaying a stored trace). Alternatively, rerun a LIBERO-Pro cell with a seed-0 trace taken from a different task; if the 82.4% collapses to near the 50.0% direct-VLA baseline, the gain is task-specific memory rather than generic primitive composition.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the claim holds, frozen VLA checkpoints can be reused across task distributions they were never trained on, with robustness coming from orchestration rather than retraining.
  • The same frozen policy can be shared across embodiments and benchmarks, because only the planner and the analytic primitives need to adapt to new environments.
  • Failure recovery becomes a planner-level operation: a failed grasp can be re-staged and retried without terminating the rollout, localizing errors to the contact-rich subtask.
  • The approach suggests that a small, interpretable primitive vocabulary is sufficient for long-horizon manipulation when the planner knows the operating conditions of each primitive.
  • Task-specific memory from a single reference seed can be re-grounded under new layouts and instructions, offering few-shot robustness without additional demonstrations.

Where Pith is reading between the lines

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

  • A reader might infer that the reported gains depend on per-task reference memory: the seed-0 trace for each perturbed cell provides task-specific structure, so the method is few-shot rather than truly zero-shot; the paper's own zero-shot LIBERO-Pro Goal numbers (31.0% on position swaps vs 87.0% few-shot) support this reading.
  • If mid-episode access to the success predicate is unavailable in real deployments, the verify-and-retry loop would need a learned or perceptual success estimator in place of the benchmark oracle; whether the planner's retry decisions remain effective under noisy verification is an open question.
  • The fixed-vocabulary idea suggests a natural next test: hold the primitive library constant and vary only the memory contents across tasks, to see whether Global Memory alone can transfer operating-range knowledge without any task-specific trace.
  • One could also test the division-of-labor hypothesis directly by measuring the frozen VLA's success on contact-only subtasks with oracle staging; if the VLA still fails there, the claimed 'local contact specialist' capability would be weakened.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper proposes Harness VLA, an agentic framework in which an LLM planner (Codex or Claude Code) orchestrates a frozen VLA model as a single contact-rich primitive (VLA ACT) together with a small fixed library of analytic primitives, using Task Specific Memory and Global Memory to learn the operating range of that vocabulary. The authors report large gains over prior baselines on perturbed LIBERO-Pro (82.4% vs RATS 43.8%), RoboCasa365 (55.4% vs RLDX-1 30.0%), and RoboTwin C2R (58.4% vs the same frozen LingBot-VLA backend at 50.4%), plus a zero-shot LIBERO-Pro GOAL study. The central claim is that lifting semantic re-grounding and non-contact execution to the planner, while reserving the frozen VLA for local contact-rich phases, extends pretrained VLAs beyond their trajectory distribution without fine-tuning. The paper includes detailed appendices specifying a file-mediated REPL, prompt templates, primitive usage statistics, and per-benchmark evaluation protocols.

Significance. If the claims hold, Harness VLA is a significant demonstration that a small, fixed primitive vocabulary plus memory-guided planning can rehabilitate a frozen VLA under semantic and spatial perturbations. The same-backbone control (π_RLinf direct 50.0% vs 82.4% harnessed) and the zero-shot LIBERO-Pro GOAL comparison are well-designed controls that isolate the planner's contribution, and the appendices provide unusual transparency (full prompt, memory schemas, usage statistics). However, a load-bearing inconsistency between the formalization and the implementation—mid-episode access to the benchmark success predicate—means that the current headline numbers cannot be interpreted as deployment-realistic evidence for the central claim. The paper's value is contingent on resolving that discrepancy by re-running without the in-loop oracle or repositioning the claims accordingly.

major comments (3)
  1. [§2.1 vs App. A / App. E] §2.1 formalizes G as 'exposed solely as a sparse success signal at episode termination,' but the implementation gives the planner access to G after every primitive: Appendix A Table 7 lists 'benchmark success signal' in state_NN.json at each step index; the shared prompt (E.1, Module 1) says 'Optimize {SUCCESS_SIGNAL}'; Global Memory (E.4) says 'Check the benchmark success signal and the latest execution record'; the TSM summary says 'verify placement with the benchmark success signal'; and Appendix B permits VLA ACT's stop predicate to be 'a benchmark predicate.' Since the completion predicate is also the evaluation criterion, the retry decisions in Key Finding 2 and Figure 4 are steered by an in-loop evaluation oracle. Either the formal model or the implementation must change; as written, the reported gains cannot be read as deployment-realistic. Please re-evaluate with mid-episode G r
  2. [Table 3] The headline +38.6 percentage points versus RATS compares Harness VLA's aggregate over all eight cells with RATS's aggregate over only the six non-LIBERO-10 cells. Because L10-T/L10-S are omitted from the baseline aggregate, the improvement is not an apples-to-apples comparison. The same-backbone π_RLinf row (50.0% over all eight cells) is a cleaner control and should be the primary comparison; if RATS remains the headline baseline, report its performance on the missing cells or compute a matched aggregate.
  3. [App. C, Tables 9–12] For LIBERO-Pro and RoboCasa365, Task Specific Memory is constructed on seed 0 of the same perturbed task, and evaluation re-grounds that trace on held-out seeds. Thus the LIBERO-Pro and RoboCasa numbers are per-task few-shot results, not zero-shot 'beyond original trajectory distribution.' The abstract and Section 1 should state this clearly. The zero-shot GOAL comparison in Table 5 shows a large drop in the position-swap condition (31.0% vs 87.0% few-shot), so the distinction materially affects the generalization claim.
minor comments (3)
  1. [Appendix C] The text repeatedly uses 'seeds 0 (seed 0)' and 'Seeds 0 is not counted'; this should be 'seed 0' for clarity.
  2. [Figure 4] Please clarify how the cap on VLA primitive invocations is enforced and whether the benchmark success signal remains visible to the planner in the capped curves; otherwise the saturation curve is hard to interpret as evidence about planner-staged retry rather than oracle access.
  3. [Appendix B, VLA ACT] The VLA ACT stop predicate 'may correspond to ... a benchmark predicate,' and the exact τ, max_chunks, analytic tolerances, and step limits are benchmark-specific. Because these predicates determine when control returns and thus the retry pattern, a complete listing of τ and tolerance values is needed for reproducibility.

Circularity Check

2 steps flagged

Benchmark success predicate is fed to the planner mid-episode, making the verify-and-retry gain an in-loop oracle; per-task seed-0 TSM further qualifies the 'beyond distribution' claim.

specific steps
  1. self definitional [§2.1 vs Appendix A Table 7, Appendix E.1/E.4, Appendix B]
    "A task is defined by a natural-language description ℓ alongside a binary completion predicate G, exposed solely as a sparse success signal at episode termination. ... state_NN.json — Step-indexed task language, robot proprioception, and benchmark success signal. ... Closed-loop control; optimize the benchmark predicate, not a visual guess. ... Do not terminate from visual proximity alone. Check the benchmark success signal and the latest execution record."

    The paper's formal model says G is visible only at episode termination, but the implementation exposes G in state_NN.json after every primitive and instructs the planner to 'optimize the benchmark predicate' and 'Check the benchmark success signal' before terminating. The retry/re-staging loop credited for the +38.6/+25.4 pp gains (Key Finding 2, Figure 4) is therefore driven by the same predicate used to score success. The reported 'prediction' of success under perturbations is not an independent outcome; it is produced by an agent that is told, mid-episode, whether the evaluation criterion has fired, so retry-until-success behavior is partly constructed by the test oracle rather than by the harness's own grounding or memory.

  2. fitted input called prediction [Appendix C, seed-0 protocol (Tables 9–11)]
    "LIBERO, LIBERO-Pro, and RoboCasa365 use a few-shot protocol in which seeds 0 (seed 0) for each task serves only as the exploratory reference seed for Task Specific Memory construction... Reported evaluation rollouts are run on held-out seeds that retrieve and re-ground the corresponding Task Specific Memory under new initial states."

    For each LIBERO-Pro cell (SPATIAL-T, OBJECT-S, etc.), the same 10 perturbed tasks are bootstrapped on their own seed 0 before evaluation on seeds 1–10; RoboCasa365 similarly uses per-task seed 0. Thus the headline 'extends pretrained VLAs beyond their original trajectory distribution' is not a zero-shot extension to a perturbed family: each evaluated task/perturbation contributes a reference solution found on that exact task's seed 0. Held-out seeds test re-grounding of a within-task trace, not transfer to unseen perturbations. This is a legitimate few-shot protocol but materially weakens the beyond-distribution framing; it is not a full reduction because the held-out seeds are new instances.

full rationale

The central derivation chain is: frozen VLA + fixed primitive library + planner + memory yields success on perturbed benchmarks. The dependent variable is the benchmark completion predicate G. The appendix-level implementation makes G an independent variable: state_NN.json carries the benchmark success signal at every primitive index, the shared prompt says to optimize the benchmark predicate, Global Memory instructs the planner to check the benchmark success signal before terminating, and the VLA ACT stop predicate may itself be a benchmark predicate. Consequently, the verify-and-retry behavior that explains most of the headline gains is an oracle-conditioned loop, not an autonomous extension of the frozen VLA. This is a genuine partial circularity: the measured outcome is partly generated by the evaluation criterion itself. A second, softer circularity is the seed-0 protocol: each evaluated perturbed task receives a reference trace from its own seed 0, so the few-shot 'beyond distribution' numbers are largely within-task reuse. The paper does contain non-circular evidence that keeps this from being a total reduction: standard LIBERO (96.0% vs 95.3% frozen), zero-shot LIBERO-Pro Goal (79.0% on Task-T without TSM), and RoboTwin C2R clean-to-randomized (58.4% vs 50.4% direct LingBot-VLA) use held-out or cross-setting evaluation and the same frozen backends. Those results give the central mechanism independent content, but the headline few-shot gains and Key Finding 2 remain contaminated by the in-loop oracle and per-task reference seeds. Score 6 reflects partial circularity rather than a fully forced derivation.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

No new physical entities are postulated; Task Specific Memory and Global Memory are software artifacts rather than invented entities. The free parameters are hand-set budgets, tolerances, the reference-seed trace, and the best-of-two-planner reporting choice — none fitted by a regression, but all influential on the headline magnitudes.

free parameters (4)
  • per-benchmark planner backbone for headline = CC for LIBERO-Pro/RoboTwin; Codex for RoboCasa365
    Headline deltas pick the better of the two LLM planners per benchmark (82.4 vs 72.1; 55.4 vs 48.6; 58.4 vs 58.0) with no pre-registered selection rule (§3.2, Tables 3/4/6).
  • VLA ACT chunk budget / stop predicates = max_chunks 2-30; stop e.g. 'object_lifted'
    Hand-set invocation lengths and early-return conditions controlling how much the frozen VLA does per call (App. B/E). These bound how many contact attempts fit in an episode and thus affect the reported success rates.
  • analytic primitive tolerances and step limits = tol 0.012, max_steps 80 (example)
    Controller thresholds in the JSON examples (App. B). The planner's analytic moves only work if these tolerances are permissive enough for the local scenes.
  • reference-seed exploration budget and resulting trace = seed 0 per task; 'generous wall-clock' budget
    The single discovered seed-0 trace is the scaffold for all few-shot rollouts (App. C). Whether exploration succeeds on seed 0, and how good the discovered trace is, is a free choice that determines the reported few-shot numbers.
axioms (5)
  • domain assumption The benchmark completion predicate is correct ground truth and is readable by the harness mid-episode (App. E.4 instructs 'Check the benchmark success signal'; Table 16 exposes it as 'libero terminated'/'success'/'eval success').
    The verification and retry loop depends on in-loop predicate access, which §2.1 explicitly denies ('exposed solely as a sparse success signal at episode termination'). This tension is load-bearing.
  • domain assumption Frozen VLA failures during evaluation are staging failures that the planner can fix by re-staging within the step budget.
    Key Finding 2 presumes contact failures are re-runnable and budget-sufficient; Figure 4's saturation curve is the supporting evidence, but this is an assumption about the VLA's failure structure, not a demonstrated property.
  • domain assumption Frontier LLM planners (Claude Code, Codex) can reliably perform pixel-to-world re-grounding from RGB-D and precomputed world maps across hundreds of rollouts.
    The entire loop rests on the planner's perceptual reliability (App. E.2); the zero-shot GOAL-S result of 31% shows this is a real constraint rather than a given.
  • domain assumption A success trace from seed 0 remains a valid procedural skeleton across the other held-out evaluation seeds.
    Evaluation re-grounds one discovered trace (App. C); if the seed-0 solution is atypical, the few-shot numbers would not transfer to the reported seeds.
  • domain assumption MuJoCo/Robosuite, RoboCasa365, and RoboTwin simulator contact dynamics are faithful enough for the transfer claims.
    All results are simulation-only; there is no real-robot evaluation, so physical-transfer claims are out of scope but the framework's relevance depends on this assumption.

pith-pipeline@v1.3.0-alltime-deepseek · 28168 in / 18836 out tokens · 188064 ms · 2026-08-02T07:50:45.534047+00:00 · methodology

0 comments
read the original abstract

Language-conditioned manipulation requires both precise contact-rich control and robust reasoning over language, scenes, and long horizons. End-to-end Vision-Language-Action (VLA) models provide strong local visuomotor skills, but they are trained on in-distribution task trajectories and often fail under deployment perturbations such as semantic retargeting, goal re-binding, spatial-layout shifts, and unstable local contacts. LLM coding agents provide complementary semantic and compositional reasoning, but purely analytic primitives struggle with irregular grasping, constrained placement, and articulated-object interaction. We present Harness VLA, a memory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive and composes it with a small fixed library of analytic primitives for grounding, staging, transport, navigation, and release. Rather than expanding the skill library, the harness learns the operating range of these fixed primitives from task-specific execution traces, global success rules, and failure models. By lifting semantic re-grounding, non-contact execution, and VLA re-staging to the planner while reserving the frozen VLA for local contact-rich phases, Harness VLA extends pretrained VLAs beyond their original trajectory distribution without finetuning. Across perturbed tabletop, household kitchen, and clean-to-randomized bimanual manipulation, Harness VLA improves over the strongest relevant baselines by 38.6 and 25.4 percentage points on LIBERO-Pro and RoboCasa365, respectively, and reaches 58.4% on RoboTwin C2R.

Figures

Figures reproduced from arXiv: 2607.08448 by Changxu Wei, Chao Yu, Chunyang Zhu, Feng Gao, Huanming Zhang, Jiaxing Qiu, Jiyuan Liu, Wenbo Ding, Wenhao Tang, Xiao Li, Yi Nie, Yixian Zhang, Yuchen Yan, Yu Wang, Zhengru Fang, Zhihao Liu.

Figure 1
Figure 1. Figure 1: Harness VLA system overview. Given a task description, RGB-D observations, and robot state, the agentic planner selects structured calls from a fixed primitive library rather than emitting low-level ac￾tions directly. The library exposes the frozen VLA as VLA ACT for contact-rich behaviors and uses analytic primitives such as MOVE TO, ROTATE, and SET GRIPPER for perception-conditioned staging, transport, p… view at source ↗
Figure 2
Figure 2. Figure 2: Primitive composition extends a frozen VLA beyond its trajectory distribution. Deployment perturbations expand the possible task configurations beyond the in-distribution trajectories covered by the frozen VLA. A direct VLA rollout may attempt to bridge the perturbed space and fail before reaching the target. Harness VLA instead decomposes the task into local contact-rich VLA invocations and analytic primi… view at source ↗
Figure 3
Figure 3. Figure 3: Terminal-state frames for two LIBERO-Pro cells. The first triplet compares RLinf on the standard [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Adaptive VLA invocation improves success across benchmarks. Each panel plots cumulative task success as a function of the maximum number of VLA primitive invocations allowed per episode. The blue dashed line marks the corresponding frozen-policy baseline, while the gray dashed line marks full Harness VLA performance with all planner-selected invocations. Across LIBERO-Pro, RoboCasa365, and RoboTwin C2R, su… view at source ↗
Figure 5
Figure 5. Figure 5: Representative rollout frames for adaptive VLA invocation. Top row: a Harness VLA rollout on LIBERO-PRO OBJECT task 4. The planner repeatedly invokes VLA ACT around the milk carton after intermediate grasping or placement attempts leave the object outside or only partially inside the basket; after re-staging the end-effector and retrying the local contact-rich operation, the milk carton is finally placed s… view at source ↗
Figure 6
Figure 6. Figure 6: Task completion attribution across benchmarks. Bars show the fraction of successful rollouts whose final benchmark completion predicate fires after an analytic primitive (blue) or after a VLA primitive (orange). LIBERO Pro-family tasks are mostly finished by analytic primitives after the VLA has established stable contact, whereas RoboCasa365 and RoboTwin C2R contain more terminal contact-rich operations s… view at source ↗
Figure 6
Figure 6. Figure 6: Task completion attribution across benchmarks. Bars show the fraction of successful rollouts whose final benchmark completion predicate fires after an analytic primitive (blue) or after a VLA primitive (orange). LIBERO Pro-family tasks are mostly finished by analytic primitives after the VLA has established stable contact, whereas RoboCasa365 and RoboTwin C2R contain more terminal contact-rich operations s… view at source ↗
Figure 7
Figure 7. Figure 7: Representative rollout frames for analytic decomposition around contact-rich phases. Top row: on a LIBERO-10-PRO swap task, the agent first invokes VLA ACT and starts moving toward the basket, then detects during MOVE TO that the VLA has not actually grasped the cream-cheese box. The planner moves back, retries VLA ACT, and, after a successful grasp, completes the subtask with MOVE TO and RELEASE. Bottom r… view at source ↗
Figure 7
Figure 7. Figure 7: Representative rollout frames for analytic decomposition around contact-rich phases. Top row: on a LIBERO-10-PRO swap task, the agent first invokes VLA ACT and starts moving toward the basket, then detects during MOVE TO that the VLA has not actually grasped the cream-cheese box. The planner moves back, retries VLA ACT, and, after a successful grasp, completes the subtask with MOVE TO and RELEASE. Bottom r… view at source ↗
Figure 8
Figure 8. Figure 8: Overview of representative environments across the four benchmark families used in our evalua [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overview of representative environments across the four benchmark families used in our evalua [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗

Review history (3 revisions) →

discussion (0)

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

Forward citations

Cited by 1 Pith paper

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

  1. SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them

    cs.AI 2026-07 conditional novelty 6.0

    SpatialCLI's Call-Learn-Internalize recipe lifts Qwen3-VL-8B from 29.3% to 84.6% on MindCube with spatial tools and 73.8% without tools by distilling successful tool trajectories into direct reasoning.

Reference graph

Works this paper leans on

105 extracted references · 34 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Brohan, N

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al. RT-2: Vision-language-action models transfer web knowledge to robotic control. InConference on Robot Learning (CoRL), 2023

    2023

  2. [2]

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

    Pith/arXiv arXiv 2024

  3. [3]

    Black, N

    K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter, et al.π 0: A vision-language-action flow model for general robot control. InRobotics: Science and Systems (RSS), 2025

    2025

  4. [4]

    Black, N

    Physical Intelligence, K. Black, N. Brown, J. Darpinian, K. Dhabalia, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, M. Y . Galliker, D. Ghosh, L. Groom, K. Hausman, B. Ichter, S. Jakubczak, T. Jones, L. Ke, D. LeBlanc, S. Levine, A. Li-Bell, M. Mothukuri, S. Nair, K. Pertsch, A. Z. Ren, L. X. Shi, L. Smith, J. T. Springenberg, K. Stachowicz, J. Tanner...

    Pith/arXiv arXiv 2025

  5. [5]

    C.-Y . Hung, Q. Sun, P. Hong, A. Zadeh, C. Li, U. Tan, N. Majumder, S. Poria, et al. NORA: A small open-sourced generalist vision language action model for embodied tasks.arXiv preprint arXiv:2504.19854, 2025

    Pith/arXiv arXiv 2025

  6. [6]

    J. Lee, J. Duan, H. Fang, Y . Deng, S. Liu, B. Li, B. Fang, J. Zhang, Y . R. Wang, S. Lee, W. Han, W. Pumacay, A. Wu, R. Hendrix, K. Farley, E. VanderBilt, A. Farhadi, D. Fox, and R. Krishna. Mol- moAct: Action reasoning models that can reason in space.arXiv preprint arXiv:2508.07917, 2025

    Pith/arXiv arXiv 2025

  7. [7]

    J. Wang, M. Leonard, K. Daniilidis, D. Jayaraman, and E. S. Hu. Evaluatingπ 0 in the wild: Strengths, problems, and the future of generalist robot policies.Online, 2025

    2025

  8. [8]

    Liang, W

    J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng. Code as policies: Language model programs for embodied control. InIEEE International Conference on Robotics and Automation (ICRA), 2023

    2023

  9. [9]

    Singh, V

    I. Singh, V . Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg. Progprompt: Generating situated robot task plans using large language models. InIEEE International Conference on Robotics and Automation (ICRA), 2023

    2023

  10. [10]

    Gupta and A

    T. Gupta and A. Kembhavi. Visual programming: Compositional visual reasoning without training. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  11. [11]

    J. Shi, R. Yang, K. Chao, B. S. Wan, Y . S. Shao, J. Lei, J. Qian, L. Le, P. Chaudhari, K. Daniilidis, et al. Maestro: Orchestrating robotics modules with vision-language models for zero-shot generalist robots. InNeurIPS 2025 Workshop on Space in Vision, Language, and Embodied AI, 2025

    2025

  12. [12]

    M. Fu, J. Yu, K. El-Refai, E. Kou, H. Xue, H. Huang, W. Xiao, G. Wang, F.-F. Li, G. Shi, et al. CaP-X: A framework for benchmarking and improving coding agents for robot manipulation.arXiv preprint arXiv:2603.22435, 2026

    Pith/arXiv arXiv 2026

  13. [13]

    Zhang, J

    J. Zhang, J. Ge, H. Yoo, L. Fu, Z. Yang, Y . Liu, R. Saravanan, S. Yin, J. Yu, D. Niu, et al. Playful agentic robot learning.arXiv preprint arXiv:2606.19419, 2026. 17

    Pith/arXiv arXiv 2026

  14. [14]

    X. Wang, Y . Chen, L. Yuan, Y . Zhang, Y . Li, H. Peng, and H. Ji. Executable code actions elicit better LLM agents. InInternational Conference on Machine Learning (ICML), 2024

    2024

  15. [15]

    J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. R. Narasimhan, and O. Press. SWE-agent: Agent-computer interfaces enable automated software engineering. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  16. [16]

    X. Wang, B. Li, Y . Song, F. F. Xu, X. Tang, M. Zhuge, J. Pan, Y . Song, B. Li, J. Singh, H. H. Tran, F. Li, R. Ma, M. Zheng, B. Qian, Y . Shao, N. Muennighoff, Y . Zhang, B. Hui, J. Lin, R. Brennan, H. Peng, H. Ji, and G. Neubig. OpenHands: An open platform for AI software developers as generalist agents. InInternational Conference on Learning Representa...

    2025

  17. [17]

    X. Ning, K. Tieu, D. Fu, T. Wei, Z. Li, Y . Bei, J. Zou, M. Ai, Z. Liu, T.-W. Li, et al. Code as agent harness.arXiv preprint arXiv:2605.18747, 2026

    Pith/arXiv arXiv 2026

  18. [18]

    G. Wang, Y . Xie, Y . Jiang, A. Mandlekar, C. Xiao, Y . Zhu, L. Fan, and A. Anandkumar. V oyager: An open-ended embodied agent with large language models.arXiv preprint arXiv:2305.16291, 2023

    Pith/arXiv arXiv 2023

  19. [19]

    B. Liu, Y . Zhu, C. Gao, Y . Feng, Q. Liu, Y . Zhu, and P. Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Systems (NeurIPS), 36, 2023

    2023

  20. [20]

    Zhou et al

    Y . Zhou et al. LIBERO-Pro: Towards realistic robotic manipulation benchmarks via systematic per- turbations.arXiv preprint arXiv:2510.03827, 2025

    Pith/arXiv arXiv 2025

  21. [21]

    Nasiriany, S

    S. Nasiriany, S. Nasiriany, A. Maddukuri, and Y . Zhu. RoboCasa365: A large-scale simulation frame- work for training and benchmarking generalist robots.arXiv preprint arXiv:2603.04356, 2026

    arXiv 2026

  22. [22]

    Y . Mu, T. Chen, Z. Chen, S. Peng, Z. Lan, Z. Gao, Z. Liang, Q. Yu, Y . Zou, M. Xu, et al. RoboTwin: Dual-arm robot benchmark with generative digital twins. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

    2025

  23. [23]

    C. Yu, Y . Wang, Z. Guo, H. Lin, S. Xu, H. Zang, Q. Zhang, Y . Wu, C. Zhu, J. Hu, et al. Rlinf: Flexible and efficient large-scale reinforcement learning via macro-to-micro flow transformation.arXiv preprint arXiv:2509.15965, 2025

    arXiv 2025

  24. [24]

    D. Kim, H. Jang, M. Koo, S. Jang, T. Kim, B. Kim, B. Yoon, C. Jang, D. Choi, D. Han, et al. Rldx-1 technical report.arXiv preprint arXiv:2605.03269, 2026

    Pith/arXiv arXiv 2026

  25. [25]

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

    Pith/arXiv arXiv 2026

  26. [26]

    X. Sun, Z. Xu, C. Cao, Z. Liu, Y . Sun, J. Pang, R. Zhang, Z. Yang, K. Pang, D. He, et al. AtomVLA: Scalable post-training for robotic manipulation via predictive latent world models.arXiv preprint arXiv:2603.08519, 2026

    arXiv 2026

  27. [27]

    Zheng, J

    J. Zheng, J. Li, Z. Wang, D. Liu, X. Kang, Y . Feng, et al. X-VLA: Soft-prompted transformer as scalable cross-embodiment vision-language-action model.arXiv preprint arXiv:2510.10274, 2025

    Pith/arXiv arXiv 2025

  28. [28]

    X. Wang, Z. Zhu, G. Huang, B. Wang, X. Chen, and J. Lu. Worlddreamer: Towards general world models for video generation via predicting masked tokens.arXiv preprint arXiv:2401.09985, 2024. 18

    Pith/arXiv arXiv 2024

  29. [29]

    Bjorck, F

    J. Bjorck, F. Casta ˜neda, N. Cherniadev, X. Da, R. Ding, L. Fan, Y . Fang, D. Fox, F. Hu, S. Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734, 2025

    Pith/arXiv arXiv 2025

  30. [30]

    Community

    S. Community. Starvla: A lego-like codebase for vision-language-action model developing.arXiv preprint arXiv:2604.05014, 2026

    Pith/arXiv arXiv 2026

  31. [31]

    Brohan, N

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al. Rt-1: Robotics transformer for real-world control at scale.arXiv preprint arXiv:2212.06817, 2022

    Pith/arXiv arXiv 2022

  32. [32]

    Ghosh, H

    Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y . Tan, D. Sadigh, C. Finn, and S. Levine. Octo: An open-source generalist robot policy.Online, 2023

    2023

  33. [33]

    Open X-Embodiment: Robotic learning datasets and RT-X mod- els.Online, 2023

    Open X-Embodiment Collaboration. Open X-Embodiment: Robotic learning datasets and RT-X mod- els.Online, 2023

    2023

  34. [34]

    Karamcheti, S

    S. Karamcheti, S. Nair, A. Balakrishna, P. Liang, T. Kollar, and D. Sadigh. Prismatic vlms: Investi- gating the design space of visually-conditioned language models.arXiv preprint arXiv:2402.07865, 2024

    Pith/arXiv arXiv 2024

  35. [35]

    Abeyruwan, J

    Gemini Robotics Team, S. Abeyruwan, J. Ainslie, J.-B. Alayrac, M. G. Arenas, T. Armstrong, A. Bal- akrishna, R. Baruch, M. Bauza, M. Blokzijl, et al. Gemini robotics: Bringing ai into the physical world.arXiv preprint arXiv:2503.20020, 2025

    Pith/arXiv arXiv 2025

  36. [36]

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

    Pith/arXiv arXiv 2024

  37. [37]

    Driess, F

    D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y . Chebotar, P. Sermanet, D. Duckworth, S. Levine, V . Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence. PaLM-E: An embodied multimodal language model. InInternational Conference on Machine Learning ...

    2023

  38. [38]

    X. Li, M. Liu, H. Zhang, C. Yu, J. Xu, H. Wu, H. Dong, H. Hu, W. Zhan, H. Wu, Y . Han, and T. Kong. Vision-language foundation models as effective robot imitators. InInternational Conference on Learning Representations (ICLR), 2024

    2024

  39. [39]

    Huang, S

    J. Huang, S. Yong, X. Ma, X. Linghu, P. Li, Y . Wang, Q. Li, S.-C. Zhu, B. Jia, and S. Huang. An embodied generalist agent in 3d world. InInternational Conference on Machine Learning (ICML), 2024

    2024

  40. [40]

    Shridhar, L

    M. Shridhar, L. Manuelli, and D. Fox. Cliport: What and where pathways for robotic manipulation. In Conference on Robot Learning (CoRL), 2022

    2022

  41. [41]

    C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song. Diffusion policy: Visuomotor policy learning via action diffusion. InRobotics: Science and Systems (RSS), 2023

    2023

  42. [42]

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn. Learning fine-grained bimanual manipulation with low-cost hardware. InRobotics: Science and Systems (RSS), 2023

    2023

  43. [43]

    Introducing GPT-5.2.Online, 2025

    OpenAI. Introducing GPT-5.2.Online, 2025. 19

    2025

  44. [44]

    Pichai, D

    S. Pichai, D. Hassabis, and K. Kavukcuoglu. A new era of intelligence with Gemini 3.Google Blog, 2025

    2025

  45. [45]

    S. Bai, Y . Cai, R. Chen, K. Chen, X. Chen, Z. Cheng, L. Deng, W. Ding, C. Gao, C. Ge, W. Ge, Z. Guo, Q. Huang, J. Huang, F. Huang, B. Hui, et al. Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025

    Pith/arXiv arXiv 2025

  46. [46]

    Introducing Claude Sonnet 4.5.Online, 2025

    Anthropic. Introducing Claude Sonnet 4.5.Online, 2025

    2025

  47. [47]

    Llama 4 Herd.Meta Blog, 2025

    Meta. Llama 4 Herd.Meta Blog, 2025

    2025

  48. [48]

    Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    OpenAI. Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    Pith/arXiv arXiv 2024

  49. [49]

    Gemini Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, et al. Gemini: A family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    Pith/arXiv arXiv 2023

  50. [50]

    Deitke, C

    M. Deitke, C. Clark, S. Lee, R. Tripathi, Y . Yang, J. S. Park, M. Salehi, N. Muennighoff, K. Lo, L. Soldaini, et al. Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

    2025

  51. [51]

    B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia. SpatialVLM: Endowing vision-language models with spatial reasoning capabilities. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  52. [52]

    Introducing deep research.OpenAI Blog, 2025

    OpenAI. Introducing deep research.OpenAI Blog, 2025

    2025

  53. [53]

    Gemini Deep Research.Google Blog, 2024

    Google. Gemini Deep Research.Google Blog, 2024

    2024

  54. [54]

    J. Wu, Z. Deng, W. Li, Y . Liu, B. You, B. Li, Z. Ma, and Z. Liu. Mmsearch-r1: Incentivizing lmms to search.arXiv preprint arXiv:2506.20670, 2025

    Pith/arXiv arXiv 2025

  55. [55]

    X. Geng, P. Xia, Z. Zhang, X. Wang, Q. Wang, R. Ding, C. Wang, J. Wu, Y . Zhao, K. Li, et al. Webwatcher: Breaking new frontier of vision-language deep research agent.arXiv preprint arXiv:2508.05748, 2025

    Pith/arXiv arXiv 2025

  56. [56]

    Huang, Y

    W. Huang, Y . Zeng, Q. Wang, Z. Fang, S. Cao, Z. Chu, Q. Yin, S. Chen, Z. Yin, L. Chen, et al. Vision- deepresearch: Incentivizing deepresearch capability in multimodal large language models.arXiv preprint arXiv:2601.22060, 2026

    arXiv 2026

  57. [57]

    B. Jin, H. Zeng, Z. Yue, J. Yoon, S. Arik, D. Wang, H. Zamani, and J. Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning.arXiv preprint arXiv:2503.09516, 2025

    Pith/arXiv arXiv 2025

  58. [58]

    K. Li, Z. Zhang, H. Yin, L. Zhang, L. Ou, J. Wu, W. Yin, B. Li, Z. Tao, X. Wang, et al. Websailor: Navigating super-human reasoning for web agent.arXiv preprint arXiv:2507.02592, 2025

    Pith/arXiv arXiv 2025

  59. [59]

    G. R. Team, A. Abdolmaleki, S. Abeyruwan, J. Ainslie, J.-B. Alayrac, M. G. Arenas, A. Balakr- ishna, N. Batchelor, A. Bewley, J. Bingham, et al. Gemini robotics 1.5: Pushing the frontier of generalist robots with advanced embodied reasoning, thinking, and motion transfer.arXiv preprint arXiv:2510.03342, 2025

    Pith/arXiv arXiv 2025

  60. [60]

    Huang, Z

    S. Huang, Z. Jiang, H. Dong, Y . Qiao, P. Gao, and H. Li. Instruct2act: Mapping multi-modality instructions to robotic actions with large language model.arXiv preprint arXiv:2305.11176, 2023. 20

    Pith/arXiv arXiv 2023

  61. [61]

    Vemprala, R

    S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor. ChatGPT for robotics: Design principles and model abilities.arXiv preprint arXiv:2306.17582, 2023

    Pith/arXiv arXiv 2023

  62. [62]

    Y . Mu, J. Chen, Q. Zhang, S. Chen, Q. Yu, C. Ge, R. Chen, Z. Liang, M. Hu, C. Tao, P. Sun, H. Yu, C. Yang, W. Shao, W. Wang, J. Dai, Y . Qiao, M. Ding, and P. Luo. RoboCodeX: Multimodal code generation for robotic behavior synthesis.arXiv preprint arXiv:2402.16117, 2024

    Pith/arXiv arXiv 2024

  63. [63]

    Sur ´ıs, S

    D. Sur ´ıs, S. Menon, and C. V ondrick. ViperGPT: Visual inference via Python execution for reasoning. InIEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  64. [64]

    Huang, C

    W. Huang, C. Wang, R. Zhang, Y . Li, J. Wu, and L. Fei-Fei. V oxposer: Composable 3d value maps for robotic manipulation with language models.arXiv preprint arXiv:2307.05973, 2023

    Pith/arXiv arXiv 2023

  65. [65]

    Goldberg, K

    A. Goldberg, K. Kondap, T. Qiu, Z. Ma, L. Fu, J. Kerr, H. Huang, K. Chen, K. Fang, and K. Goldberg. Blox-net: Generative design-for-robot-assembly using VLM supervision, physics simulation, and a robot with reset. InIEEE International Conference on Robotics and Automation (ICRA), 2025

    2025

  66. [66]

    R. Li, Y . Zhou, Y . Zhu, K. Chen, J. Wang, S. Wang, K. Hu, M. Yu, B. Jiang, Z. Su, J. Ma, X. He, Y . Shen, Y . Yang, G. Ren, M. Yao, W. Wang, and Y . Mu. RoboClaw: An agentic framework for scalable long-horizon robotic tasks.arXiv preprint arXiv:2603.11558, 2026

    arXiv 2026

  67. [67]

    R. Lu, Y . Wu, E. Kou, L. Fu, W. Xiao, A. Mandlekar, Y . Xu, G. Shi, K. Goldberg, A. Chen, et al. ASPIRE: Agentic/skills discovery for robotics.arXiv preprint arXiv:2607.00272, 2026

    Pith/arXiv arXiv 2026

  68. [68]

    Shinn, F

    N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao. Reflexion: Language agents with verbal reinforcement learning. InAdvances in Neural Information Processing Systems (NeurIPS), volume 36, 2023

    2023

  69. [69]

    Madaan, N

    A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y . Yang, et al. Self-refine: Iterative refinement with self-feedback. InAdvances in Neural Information Processing Systems (NeurIPS), volume 36, 2023

    2023

  70. [70]

    X. Chen, M. Lin, N. Sch ¨arli, and D. Zhou. Teaching large language models to self-debug. InInterna- tional Conference on Learning Representations (ICLR), 2024

    2024

  71. [71]

    Yoneda, J

    T. Yoneda, J. Fang, P. Li, H. Zhang, T. Jiang, S. Lin, B. Picker, D. Yunis, H. Mei, and M. R. Walter. Statler: State-maintaining language models for embodied reasoning and planning. InIEEE Interna- tional Conference on Robotics and Automation (ICRA), 2024

    2024

  72. [72]

    M. Ahn, A. Brohan, N. Brown, Y . Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. Jauregui Ruano, K. Jef- frey, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y . Kuang, K.-H. Lee, S. Levine, Y . Lu, L. Luu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Re...

    2022

  73. [73]

    Huang, F

    W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y . Cheb- otar, et al. Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608, 2022. 21

    Pith/arXiv arXiv 2022

  74. [74]

    Y . Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y . Qiao, and P. Luo. Embod- iedGPT: Vision-language pre-training via embodied chain of thought. InAdvances in Neural Informa- tion Processing Systems (NeurIPS), volume 36, 2023

    2023

  75. [75]

    A. Zeng, M. Attarian, B. Ichter, K. Choromanski, A. Wong, S. Welker, F. Tombari, A. Purohit, M. Ryoo, V . Sindhwani, J. Lee, V . Vanhoucke, and P. Florence. Socratic models: Composing zero- shot multimodal reasoning with language. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  76. [76]

    B. Liu, Y . Jiang, X. Zhang, Q. Liu, S. Zhang, J. Biswas, and P. Stone. LLM+P: Empowering large language models with optimal planning proficiency.arXiv preprint arXiv:2304.11477, 2023

    Pith/arXiv arXiv 2023

  77. [77]

    Y . Chen, J. Arkin, C. Dawson, Y . Zhang, N. Roy, and C. Fan. AutoTAMP: Autoregressive task and motion planning with LLMs as translators and checkers. InIEEE International Conference on Robotics and Automation (ICRA), 2024

    2024

  78. [78]

    K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf. SayPlan: Grounding large language models using 3D scene graphs for scalable task planning. InConference on Robot Learning (CoRL), 2023

    2023

  79. [79]

    task":"put the black bowl on the wooden tray

    Z. Mandi, S. Jain, and S. Song. RoCo: Dialectic multi-robot collaboration with large language models. InIEEE International Conference on Robotics and Automation (ICRA), 2024. 22 A File-Mediated REPL Protocol The harness of Section 2.2 implements the execution loop of Section 2.1 as a synchronous file-mediated Read-Eval-Print Loop (REPL). A long-running en...

    2024

  80. [80]

    On this seed, the agent searches for a successful primitive sequence and stores the resulting audit summary and JSONL command trace as Task Specific Memory

    for each task serves only as the exploratory reference seed for Task Specific Memory construction. On this seed, the agent searches for a successful primitive sequence and stores the resulting audit summary and JSONL command trace as Task Specific Memory. Seeds 0 is not counted in reported evaluation. Reported evaluation rollouts are run on held-out seeds...

Showing first 80 references.