Flex-$\pi$: A Multi-Stream World-Action Model with Compute Flexibility

Dieter Fox; Ge Yan; Jesse Zhang; Jinghao Liu; Lei Cai; Minwen Liao; Yuzhi Fan

REVIEW 5 major objections 6 minor 59 references

Flex-$\pi$: A Multi-Stream World-Action Model with Compute Flexibility

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper shows that a frozen video-generation VAE that encodes RGB also encodes 3D pointmaps almost losslessly, letting a world-action model be supervised on RGB, 3D geometry, and semantics with no extra sensors or latency.

desk verdict A serious WAM systems paper with a plausible free-lunch claim, but the two load-bearing assertions—VAE pointmap encoding and stream-attribution—need stronger support before the 2-7x margins can be taken at face value. read the letter →

arxiv 2608.10860 v1 pith:THPHAJID submitted 2026-08-11 cs.RO cs.CV

Ge Yan , Jinghao Liu , Yuzhi Fan , Lei Cai , Minwen Liao , Jesse Zhang , Dieter Fox This is my paper · ORCID

classification cs.ROcs.CV

keywords world-actionmodelmulti-streamsupervisionpointmapencodingfrozenvideoVAEDINOsemanticsMixture-of-Transformersflowmatchingbimanualmanipulation

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 claims a free lunch: the frozen VAE from a video-generation model, trained only on RGB pixels, also encodes 3D pointmaps almost losslessly, with no pointmap-specific training. This lets Flex-π, a 6B-parameter world-action model, be supervised on future RGB, 3D pointmaps, and DINO object semantics together with actions, at no cost in new sensors, new pre-training, or added inference latency. Because every visual signal lives in one shared latent space, a single checkpoint can run in any input/output mode, from fast action-only to full joint generation of all three visual futures. The reported result is a policy that beats strong baselines by 2-7x on precise, real-world bimanual tasks both in and out of distribution, while running faster than π0.5 in action-only mode. The paper's point is that geometric and semantic grounding is essentially free for a video-pretrained world-action model.

What carries the argument

The load-bearing object is the shared latent space of the frozen Wan-2.2 video VAE, which maps both RGB frames and 3D pointmaps (dense per-pixel 3D coordinate images) to the same token space. Around it, Flex-π layers a Mixture-of-Transformers backbone—a shared visual transformer with a separate, narrower action expert—that jointly denoises all four streams under a flow-matching loss, with stream-specific adapters mapping DINO features and actions into and out of the trunk. The training regime that makes compute flexibility possible is per-stream dropout with cross-modality forcing: input presence masks and output attention masks are drawn independently per sample, so a stream absent from the input is still generated, and a single checkpoint can be invoked at inference with any subset of visual inputs and outputs.

What would settle it

The decisive check is to run the Figure 11a ablation at full scale: fine-tune the 6B model with video-only supervision, with video plus DINO, and with all three streams, using the same AGIBOT World pre-training and real-world fine-tuning protocol. If the success gap from adding pointmaps and DINO does not persist at this scale, the paper's attribution of Flex-π's gains to multi-stream world-action supervision would not be supported.

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Extended reading notes

Core claim

The central discovery is that the latent space of a video-generation VAE is not specific to RGB: the frozen Wan-2.2 VAE used by Flex-π reconstructs 3D pointmaps with PSNR around 38 and MSE around 0.0001, even though it was only ever trained to reconstruct images. Because pointmaps are image-shaped, the same encoder, the same latent tokens, and the same decoder handle both modalities in a shared space, so a world-action model can be trained to denoise future pointmaps alongside future RGB latents and DINO semantic tokens without any modality-specific autoencoder. Flex-π couples this with per-stream input dropout and cross-modality forcing: each visual stream is dropped from the input half the time but is still always denoised at the output, so the model must predict every modality from the others. The paper argues that this produces a stronger internal representation for action generation, and that the resulting policy is exceptionally demonstration-efficient and robust to distribution shift.

Load-bearing premise

The paper assumes that the gains from the DINO and pointmap streams, measured in small from-scratch RoboTwin ablations, transfer to the full 6B model pre-trained on AGIBOT World and fine-tuned on real bimanual tasks.

Editorial extensions

If this is right

One trained checkpoint covers the whole latency–accuracy frontier: action-only runs at about 60 ms per call, while full joint generation costs about 193 ms and adds roughly 24 points of success in the RoboTwin ablation sweep.
Demonstration efficiency improves sharply: at 50 demonstrations per task in RoboTwin, Flex-π reaches 78.8% success versus 31.4% for π0.5, and the gap narrows as data increases.
Depth sensing is optional at deployment: withholding the pointmap input on Put Plate on Rack costs only about 3 points, because cross-modality forcing lets the model generate geometry from RGB and DINO.
Additional visual streams matter mainly through training supervision, not at test time: removing cross-modality forcing drops RoboTwin success by about 21%, and removing the pointmap stream from training costs about 20%.
Out-of-distribution robustness improves: under unseen objects and distractors, Flex-π drops about 4–5 points while the strongest baseline, ManiFlow, drops 26.7 points despite having depth input.

Reading between the lines

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

If the free-lunch VAE phenomenon is general, other video-generation VAEs may also encode depth, normal maps, or segmentation maps with little retraining; that would make multi-modal world-action supervision a cheap add-on for existing video-pretrained policies.
The compute-flexibility design suggests an adaptive deployment policy: a robot could run action-only for routine subtasks and switch on joint generation for high-risk phases, reallocating latency only where it buys safety.
A testable extension, not stated in the paper, is to use the pointmap stream as a self-supervised 3D pretraining signal for vision-language-action models that currently require explicit depth encoders, potentially replacing them with a frozen RGB VAE.
The from-scratch ablation scale leaves open whether pre-training data volume, rather than the multi-stream objective, explains part of the real-robot gains; a full-scale controlled comparison would settle which factor dominates.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Your take is close to mine. The interesting thing here is the free lunch: a frozen video VAE, trained only on RGB, reconstructs 3D pointmaps almost losslessly. That's a real empirical discovery if it holds up, and the multi-stream design—RGB, pointmap, DINO in a shared frozen-VAE latent with per-stream dropout and cross-modality forcing—is new relative to UWM, DreamZero, Fast-WAM, and the other cited WAMs. To its credit, the paper fixes loss weights, dropout probabilities, and flow shifts a priori, so there's no post-hoc constant fitting. The experiments are thorough: RoboTwin data scaling, LIBERO and LIBERO-Plus, real bimanual tasks with partial-credit rubrics, baselines retrained at low data budgets, and a careful inference-optimization appendix. The paper is honest about limitations: longer convergence, joint generation slower than VLAs, and two baselines beating it on LIBERO-Plus.

Now the soft spots, in proportion. The free-lunch claim rests on a single qualitative reconstruction (Figure 3, PSNR 38 on one example) with no dataset-wide error metrics. This is load-bearing—the abstract says "almost losslessly"—and it needs systematic evaluation across scenes and cameras. Second, the ablations that attribute the policy gains to the DINO and pointmap streams (Section 4.5) are from scratch on five RoboTwin tasks, while the headline results use the 6B model pre-trained on AGIBOT World and fine-tuned. The stress-test note is right: the transfer of those ablations to the pretrained checkpoint is assumed, not shown. A pretrained-initialized ablation would settle it. Third, the real-world results are 10–20 rollouts per condition with no significance tests; the margins on the hard tasks are large (2–7x), but we can't fully separate signal from noise. Fourth, there's no code, data, or checkpoints, which makes the latency numbers and RoboTwin comparisons hard to verify independently.

These are all addressable, and the central idea is likely right. I'd send this to referees and ask for the pointmap reconstruction metrics, a pretrained-initialized stream ablation, and either more rollouts or confidence intervals. The paper is for anyone working on world-action models or using video-pretrained latents for robot learning, and it deserves a proper review.

Referee Report

5 major / 6 minor

Summary. The paper introduces FLEX-π, a 6B-parameter world-action model that jointly denoises latent streams for RGB, 3D pointmaps, and DINO semantic features alongside actions. The core empirical claim is that a frozen video-generation VAE (Wan-2.2) encodes 3D pointmaps almost losslessly even though it was trained only on RGB, so the same latent space can supervise geometry and semantics without new sensors, new pre-training, or added inference latency. The model uses per-stream dropout and cross-modality forcing to make a single checkpoint switchable between action-only and full-joint generation at deployment. Experiments cover RoboTwin, LIBERO, LIBERO-Plus, and five real-world bimanual tasks, with claims of large gains over VLA and WAM baselines, particularly in low-data regimes. The paper also provides a detailed latency-optimization ladder showing that action-only inference is faster than π0.5 and that joint generation trades latency for success.

Significance. If the central claims hold, the paper would make a valuable empirical contribution: it identifies a genuinely unexpected property of a frozen RGB-trained VAE, and it shows a practical way to inject geometric and semantic supervision into world-action models without any extra deployment cost. The paper is unusually careful in several respects: the training hyperparameters are fixed a priori and not fitted to the headline results; the K=4 Euler-step choice is selected by an explicit sweep on a separate benchmark, not tuned on the real-world tasks; the latency comparisons use a transparent optimization ladder with numerical fidelity checks; and the appendices give enough implementation detail to reproduce the main pipeline. The real-world evaluation is also broader than typical, with partial-credit rubrics and out-of-distribution conditions.

major comments (5)

[Sec. 4.5, Figure 11a] The paper's central policy-level claim—that the DINO and pointmap streams causally produce the large simulation and real-world gains—relies on ablations run from scratch on five RoboTwin tasks with 50 demonstrations and 5 epochs, while the headline results use a 6B model pre-trained on AGIBOT World and then fine-tuned. Nothing in the paper shows that the +6.8% (DINO) and +20% (pointmap) stepwise gains, or the 21% cross-modality-forcing effect in Figure 12, survive initialization from the pretrained checkpoint. If the pretrained model already encodes geometry and semantics through its video prior, the extra streams may contribute little, and the 2–7× real-world margins could be driven by pre-training scale, the MoT architecture, or fine-tuning protocol rather than by multi-stream supervision. This is a load-bearing assumption for the abstract's causal framing. A pretrained-initialized ablation on the same five-task recipe, or an explicit demonstration that from-scratch transfer is established elsewhere in the literature, would settle the point.
[Sec. 3.1, Figure 3] The free-lunch claim—that the frozen Wan-2.2 VAE encodes 3D pointmaps 'almost losslessly' with no pointmap-specific training—is supported only by a single reconstructed pointmap image with PSNR 38 and MSE 0.0001. There is no dataset-wide error metric, no comparison against a pointmap-trained VAE, and no report of how the metric is computed (e.g., which views, which depth range, which spatial resolution). Because this claim is the foundation for the whole multi-stream supervision scheme, it needs a quantitative evaluation across a representative sample of the actual pre-training data (AGIBOT World) or a benchmark such as RoboTwin, including failure modes such as depth discontinuities and occluded regions. Without that, 'almost losslessly' is not established beyond a single anecdote.
[Sec. 4.5, Figure 12] The cross-modality-forcing ablation is described by a single sentence: removing it 'hurt success rates by 21%.' The paper does not specify what 'without cross-modality forcing' means operationally—whether the output mask becomes tied to the input mask, whether a dropped input stream is also dropped from the loss, or whether the dropout probabilities change. Since this mechanism is one of the two key design choices (the other being the multi-stream supervision itself), the ablation needs a precise description of the control condition and ideally a second variant that dissociates the attention-mask effect from the loss-mask effect. The current presentation makes the 21% figure difficult to interpret or reproduce.
[Sec. 4.2, Figure 6] The real-world comparison is weakened by selective baseline coverage: Fast-WAM is not evaluated on Self-Repair Gripper or Soft-Bag Zipping, with the justification that its performance on the other three tasks indicated it would not reach a scoreable level. This is a reasonable practical decision, but it means the 2–7× margins in the abstract are computed over different baseline sets on different tasks. The paper should state explicitly, for each task, which baselines contribute to the reported margins, and should report the corresponding per-task baseline set in the summary figure or a table. The current Figure 6 is visually clear but does not make this dependence transparent.
[Sec. 4.4, Table 2] The LIBERO results are presented with a footnote-style distinction between FLEX-π and FLEX-π*, where the asterisk denotes fine-tuning without stream dropout. The headline claim that 'one FLEX-π checkpoint outperforms all existing VLA or WAM methods' is only true for FLEX-π* (99.2%), not for the flexible-dropout variant (98.5%), which is tied with or below several baselines. The abstract's phrase 'outperforms all existing VLA or WAM methods' should be qualified to distinguish the fixed-mode variant from the flexible-mode variant, or the claim should be restricted to the specific variant that actually achieves it.

minor comments (6)

[Abstract and Sec. 4.2] The '2–7×' improvement is reported ambiguously: it is a ratio of success rates or task-completion scores, not a ratio of episode completions with a common baseline set. The abstract should specify what the denominator is, or the paper should cite the exact figure and baseline pairing.
[Sec. 3] There are several typographical and formatting issues: 'readibility' appears in the problem statement, and the mathematical notation around Eq. (1) has an awkward nested expectation with a misplaced line break. These are minor but worth cleaning up.
[Sec. 3.2, Figure 4] The 'cross-modality forcing' example in Figure 4 is difficult to parse: the caption says the pointmap input is not attended to, yet the joint generation 'still conditions on generated pointmap futures.' The arrow convention should be clarified, ideally with a one-sentence definition of what it means for a generated future to condition on another generated future that was not observed as input.
[Sec. 4.1] The paper says Fast-WAM is 'not competitive on any of these tasks' and therefore omits it from the two most difficult real-world tasks. This is a legitimate choice, but the sentence should also state that Fast-WAM was nevertheless trained and evaluated on the other three tasks under the same protocol, so readers do not infer that it was skipped after an informal pilot.
[Sec. B] The pre-training description says the pointmaps are annotated with Depth Anything 3 and 'tiled into the same three-view composite canvas.' It is not clear whether the same tiling and canvas resolution are used for the pointmap stream as for the RGB stream, which matters for the VAE encoding. A one-sentence clarification of the exact resolution and tiling would remove the ambiguity.
[Sec. I.1] The K=1 column in Table 11 is described as a latency floor rather than an operating point, and the paper notes that success collapses to 51–53%. This is a useful honesty check, but the caption should also mention that the 49 ms at K=1 is not a deployable configuration, so readers do not misread it as part of the speed–accuracy frontier.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 'free lunch' is an empirical observation, and the policy attribution rests on independent ablations rather than fitted or self-cited inputs.

full rationale

The paper's central claim—that the frozen Wan VAE encodes pointmaps—is an empirical measurement (Figure 3), not a consequence of how the VAE or pointmaps are defined: pointmaps come from the external Depth Anything 3 model and are not part of the VAE's training distribution, so the reconstruction result is not true by construction. The multi-stream policy gains are supported by ablations (Figures 11a, 12) that vary inputs and training rules while holding the task set fixed; these are independent measurements rather than fitted parameters renamed as predictions. Loss weights are fixed at 1, stream dropout probabilities are fixed at 0.5, and the K=4 Euler step count is a latency/success trade-off chosen by sweep, not a constant fit to the headline real-world results. The only self-citation is the ManiFlow baseline, which is used comparatively and was deliberately strengthened ('we ran ManiFlow both ways and found RGB plus pointmaps performs much better'), so it does not supply a load-bearing premise. Concerns that the from-scratch ablations may not transfer to the pre-trained model are external-validity issues, not circularity.

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

The paper introduces no new physical entities. The pointmap and DINO streams are derived from existing off-the-shelf models (Depth Anything 3, DINOv3). The central empirical claim rests on domain assumptions about those models and on the transferability of from-scratch ablations, plus a small number of hand-set hyperparameters.

free parameters (6)

Per-stream input dropout probability p(observed) = 0.5
Chosen by hand for each visual stream (Sec 3.2, Table 12); not tuned, but shapes the set of training masks.
Per-stream output attention probability p(read by action) = 0.5
Chosen by hand (Sec 3.2, Table 12); controls cross-modality forcing and inference modes.
Loss weights λa, λo, λd, λp = 1.0 each
Set to 1 in every experiment (Sec 3.3 Eq. 3); no tuning reported.
Flow-matching shift = 6.0 visual, 1.0 action
Hyperparameters in Table 12; affect training dynamics but are not fitted to the main results.
Pointmap depth clip = 2 m
Pointmaps clipped at 2 meters (Sec A.5, Table 12); controls the geometry supervision range.
Euler steps K = 4
Deployment-time sweep; K=4 peaks and is used throughout (Sec I.1, Table 11). This is a hand-chosen value, not a fitted constant of the claim.

assumptions (5)

domain assumption Depth Anything 3 produces metric pointmaps accurate enough to supervise policies.
Used for AGIBOT pre-training and LIBERO fine-tuning (Sec B, E); no quantitative validation against ground truth is provided.
domain assumption DINOv3 features provide object-centric semantics that improve manipulation policy learning.
Supported only by the from-scratch ablation (Figure 11a); the mechanism is not verified in the full pipeline.
ad hoc to paper From-scratch ablations transfer to the pre-trained model.
The main experiments use a pre-trained 6B model, while the ablations in Sec 4.5 are from scratch; the paper assumes the observed benefit remains after pre-training.
domain assumption The frozen Wan-2.2 VAE latent space is well-conditioned for pointmap inputs.
Central to the free-lunch claim; evidenced by a single reconstruction (Figure 3) and by downstream policy performance.
standard math Flow-matching Euler integration with K steps is a sufficient approximation of the generative path.
Standard practice; the paper sweeps K but does not analyze discretization error.

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Cite this review

Pith. "Pith review of Flex-$\pi$: A Multi-Stream World-Action Model with Compute Flexibility." pith.science (2026). https://pith.science/paper/THPHAJID

@misc{pith2026260810860,
  author       = {Pith},
  title        = {Pith review of: Flex-$\pi$: A Multi-Stream World-Action Model with Compute Flexibility},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/THPHAJID}},
  note         = {Machine review of arXiv:2608.10860}
}

abstract

World-action models (WAMs) predict the future to act better, but nearly all of them predict only RGB latents, trained purely for pixel reconstruction, with no explicit signal for the 3D geometry or object semantics manipulation needs. We find a surprising free lunch: the same frozen video-generation VAE that encodes RGB also encodes 3D pointmaps almost losslessly, with no pointmap-specific training at all. This lets us supervise Flex-$\pi$, a 6B-parameter WAM, on 3D geometry and object-centric DINO semantics alongside RGB, at no cost in new sensors, new pre-training, or inference latency. Every visual signal is projected into this shared latent space and denoised jointly with actions inside a Mixture-of-Transformers backbone; per-stream dropout with cross-modality forcing then lets a single trained checkpoint run on any subset of these streams, from a fast action-only mode to full joint generation. The result is a policy that is exceptionally demonstration-efficient and generalizes well, beating the strongest baselines by up to 2-7$\times$ on dexterous, precise, real-world bimanual manipulation tasks both in and out of distribution, all while running faster than $\pi_{0.5}$. Our project website: https://flex-pi.github.io/

Figures

Figures reproduced from arXiv: 2608.10860 by the authors.

**Figure 1.** FLEX-π is a multi-stream world-action model which can take in RGB, 3D, and DINO visual features to jointly generate both latent future visual features and actions. After training, it supports flexible inference modes that let end users trade off latency and performance. Specifically, FLEX-π uses a single, frozen VAE from a pre-trained video generation model (Wan et al., 2025) to encode both RGB images and 3D pointma… view at source ↗

**Figure 2.** FLEX-π architecture. At time t, RGB ot and 3D pointmaps pt are encoded by a pretrained Wan-2.2 VAE into latent token streams z o t , z p t ; DINOv3 produces semantic tokens dt. A stream presence mask min selects which visual streams are attended to in a shared Visual Transformer Backbone. A smaller Action Expert cross-attends to visual streams to produce an action chunk at:t+H. Conditioning (st, l) is shared acros… view at source ↗

**Figure 3.** Wan-VAE pointmap reconstruction closely matches the ground truth, despite only being trained on RGB images. Visual Input Encoding. We use the frozen encoder and decoder from the VAE of the Wan-2.2-5B (Wan et al., 2025) video generation model. Surprisingly, we find that directly encoding and then decoding pointmaps with this frozen VAE—trained only on images—yields very accurate reconstructions, making its latent … view at source ↗

Figures from the paper (16 more)

**Figure 4.** Figure 4: One checkpoint, any visual input and output. Each row is one example training scheme determined by sampling min, selecting which streams are observed at time t, and mout, determining what visual outputs are visible for joint prediction. ⋆ shows an example of cross-moda…

**Figure 5.** Figure 5: Illustration of selected evaluation tasks. Self-Repair Gripper rebuilds the robot’s own gripper over eight stages that must be completed in order, at ±0.25–0.5 mm of insertion clearance; Soft-Bag Zipping opens a deformable pencil case, places a pen inside, and zips it …

**Figure 6.** Figure 6: Real-world results. We report task completion for FLEX-π and three baselines on five bimanual tasks. Each bar is task completion; the hatched region at its base is the binary success rate, the fraction of rollouts satisfying the entire rubric. The rightmost panel avera…

**Figure 7.** Figure 7: Real-world speed– success frontier. Five-task mean completion against measured latency (Sec. F.3). FLEX-π outperforms on every task [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Plate on Rack generalization conditions. Next, we test generalization and demonstration-efficiency in the real world. We re-evaluate the same models on three tasks from Sec. 4.2 but with out-of-distribution objects, under extreme clutter visual clutter and distractors…

**Figure 9.** Figure 9: Out-of-distribution performance and data efficiency. (a) Unseen objects and distractors. (b) Training on 50% of the data. Each pair of bars is labelled with the change in task completion. FLEX-π stays ahead of both ManiFlow and π0.5 under distribution shift and reduc…

**Figure 10.** Figure 10: RoboTwin data scaling (domainrandomized, 50-task average). FLEX-π leads at every data scale in both modes, with the largest margin at low data; baselines only close the gap at 500 demos per task. depth, FLEX-π’s performance improvement over other baselines is explain…

**Figure 11.** Figure 11: RoboTwin ablations and system tradeoffs (Sec. G). (a): which visual streams the model observes, added cumulatively. For each ablation, all available streams are predicted at inference. (b): with RGB-only input, generating more streams trades latency for success, from…

**Figure 12.** Figure 12: Cross-modality forcing on RoboTwin. Both models observe all three streams; only the training rule differs. The benefit of cross-modality forcing is therefore not only robustness to missing sensors at inference time, but also that requiring each modality to be predict…

**Figure 13.** Figure 13: Generated streams on AGIBOT World. Examples of the visual streams a single FLEXπ checkpoint generates, at sampled frames (left to right); each panel is the three-view composite canvas, head camera above and the two wrist cameras below. The first three rows generate w…

**Figure 14.** Figure 14: The remaining evaluation tasks and generalization cases. Put Plate on Rack and Sort Utensils test bimanual coordination under sustained contact; Kitchen Organization chains four such skills into one long-horizon episode and is evaluated in a single setting. Rows are g…

**Figure 15.** Figure 15: Action-only is the fastest policy and also the most accurate; joint generation trades latency for further accuracy. Left: in-distribution task completion against single-inference latency on an RTX 5090, best stack per path (Tables 9 and 10). The vertical axis is the f…

**Figure 16.** Figure 16: FLEX-π pulls further ahead of π0.5 as the domain shift gets harder. Each row is one unseen condition, ordered by the size of the gap; the grey bar spans π0.5 to FLEX-π (full joint). A half-light, half-dark dot marks a condition on which the two FLEX-π settings score i…

**Figure 18.** Figure 18: Depth input is optional at deployment. Task completion on Put Plate on Rack, with and without the depth input. G.3 (Q1) LONG-HORIZON DEXTERITY: SELF-REPAIR GRIPPER In Self-Repair Gripper, the robot repairs its own gripper, fastens it with a screw, and finally clears…

**Figure 17.** Figure 17: How much each method loses under distribution shift. Unseen task completion against the matching seen condition, one point per task; for Put Plate on Rack the unseen coordinate averages the unseen-plate and distractor conditions. Distance below the dashed diagonal is…

**Figure 19.** Figure 19: Self-Repair Gripper: an eight-stage task, and how far each method gets. Left: the eight stages, which must be completed in order; green marks the three insertion and fastening stages, which together carry half of the 3.0 available points. Right: partial-credit score n…

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(1)) when the data dimension is large, and we also found this to empirically be the case for our setting

works better than standard velocity prediction (Eq. (1)) when the data dimension is large, and we also found this to empirically be the case for our setting. Only the folded DINO stream sits at this ratio: the video and pointmap latents are at192/3072≈0.06and the action stream...

2025
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rows marked † in Table 7 are our own evaluations of the released checkpoints, following Fei et al

reports LIBERO-Plus, so the 27 Preprint. rows marked † in Table 7 are our own evaluations of the released checkpoints, following Fei et al. (2025), with no weights updated. The RoboTwin data-scaling experiment (Figure

2025
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C) or the five tasks of Sec

F.2 REAL-ROBOTBASELINEDEPLOYMENT No published numbers exist for the Y AM platform (Sec. C) or the five tasks of Sec. G, so ev- ery real-robot baseline —π 0.5 (Intelligence et al., 2025), ManiFlow (Yan et al., 2025b), and Fast- W AM (Yuan et al., 2026b) — is one we trained. For...

2025
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ManiFlow (Yan et al., 2025b) takes the three RGB views at the same resolution plus the three depth maps, back-projected to pointmaps on device by its own encoder

takes the three views as separate224×224tensors, matching its released checkpoint. ManiFlow (Yan et al., 2025b) takes the three RGB views at the same resolution plus the three depth maps, back-projected to pointmaps on device by its own encoder. This departs from its released ...

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This experiment uses the50random-scene RoboTwin tasks, not the five-task recipe of the ablations above

98.5 99.6 97.2 98.5 98.5 Cosmos-Policy (Kim et al., 2026b) 98.1100.098.2 97.6 98.5 FLEX-π(full joint, no depth) 99.0 99.8 98.6 96.6 98.5 FLEX-π(full joint) 99.699.8 98.6 96.0 98.5 FLEX-π ∗ (full joint, no depth) 98.8 99.6 98.698.899.0 FLEX-π ∗ (full joint) 99.2 99.899.098.699....

2025
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56.4 31.9 79.5 88.7 93.3 75.8 74.2 69.6 π0.5 (Intelligence et al., 2025)† 72.2 76.8 85.1 96.2 95.5 87.0 86.9 84.7 Qwen-RobotManip (Yuan et al., 2026a) 89.9 83.9 86.5 98.6 99.9 97.9 87.5 91.4 FLEX-π(action-only) 71.9 50.7 83.3 91.9 91.5 86.0 80.4 78.3 W AM Fast-W AM (Yuan et al...

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arXiv
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The rearrangement is exactly invertible, so no feature content is lost; the model simply predicts each2×2neighborhood jointly within one token instead of across four

(space-to-channel) fold of factorf= 2to each view’s native patch grid before the adapter: each2×2block of neighboring patches is concatenated along the channel axis, so the encoder’s14×14grid of768-dimensional tokens becomes a7×7grid of f 2 ·768 = 3072-dimensional tokens per v...

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Matthew M

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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AgiBot-World-Contributors, Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, Shu Jiang, Yuxin Jiang, Cheng Jing, Hongyang Li, Jialu Li, Chiming Liu, Yi Liu, Yuxiang Lu, Jianlan Luo, Ping Luo, Yao Mu, Yuehan Niu, Yixuan Pan, Jiangmiao Pang, Yu Qiao, Guanghui Ren, Cheng Ruan, Jiaqi Shan, Yongjian...

arXiv

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34 Preprint. Table 7:Robustness on LIBERO-Plus.Success rate (%) under the seven perturbation types;bold = best per column.Totalis the official task-count-weighted mean over all10,030perturbed tasks, not an unweighted mean of the seven categories, since the categories differ in size. Baselines are the published numbers of Fei et al. (2025). †Neither Fast-W...

work page 2025

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(Lin et al., 2026), lift the result to pointmaps with only the camera intrinsics (no calibrated extrinsics needed), and tile them into the same three-view composite canvas we use on the real robot (Sec. C). C REAL-WORLDROBOTPLATFORM(YAM) Hardware.We run all real-robot experiments on a stationary bimanual Y AM setup: two6-DoF arms, each with a single-DoF p...

work page 2026

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Modality Forcing for Scalable Spatial Generation

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arXiv