arxiv: 2605.10942 · v1 · submitted 2026-05-11 · 💻 cs.RO

Recognition: 1 theorem link

· Lean Theorem

HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models

Qiuxuan Feng , Jiale Yu , Jiaming Liu , Yueru Jia , Zhuangzhe Wu , Hao Chen , Zezhong Qian , Shuo Gu

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Peng Jia Siwei Ma Shanghang Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:20 UTC · model grok-4.3

classification 💻 cs.RO

keywords world action modelsrobot manipulationzero-shot generalizationadaptive gatingpredictive controlreactive controlvideo prediction

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The pith

HarmoWAM unifies predictive and reactive control in world action models to deliver both generalization and precision for robot tasks in new settings.

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

World action models split into two camps that trade off strengths: one uses video prediction for broad movement across new scenes but struggles with exact interactions, while the other produces fine actions yet stays limited by what it saw in training. HarmoWAM combines both by letting a world model supply physical priors that shape a predictive expert for step-by-step actions and a reactive expert for direct responses to visual changes. A gating process decides the switch points during a task so the model can first explore wider possibilities and then lock in accuracy. Tests on real robots in six tasks with new backgrounds, positions, and objects show clear gains over earlier approaches.

Core claim

HarmoWAM conditions a predictive expert that iterates actions from latent dynamics and a reactive expert that infers actions from predicted visual evolution, both guided by the world model's spatio-temporal priors, while a Process-Adaptive Gating Mechanism selects the active expert at each moment to support generalizable transit and precise manipulation in one end-to-end model.

What carries the argument

The Process-Adaptive Gating Mechanism, which uses world-model priors to decide the timing and location for switching between the predictive expert and the reactive expert.

If this is right

The world model expands the reactive expert's exploration space beyond the original training distribution.
The predictive expert supplies precise control once the task reaches interaction stages.
Zero-shot performance holds across changes in background, object position, and object semantics on real robots.
End-to-end training removes the need for separate video-prediction and action modules.

Where Pith is reading between the lines

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

The same gating logic could be added to other robot policies that already combine planning and immediate response.
Longer sequences of tasks would test whether repeated gating decisions preserve coherence without drift.
Adding explicit physical properties such as mass or friction to the world model priors might sharpen the reactive expert further.

Load-bearing premise

The world model supplies accurate spatio-temporal physical priors and the gating mechanism correctly identifies when and where to switch experts during a task.

What would settle it

A controlled test where the gating mechanism switches at the wrong stage on a precision-critical step in an unseen environment, producing a measurable drop in success rate compared with the reported margins.

Figures

Figures reproduced from arXiv: 2605.10942 by Hao Chen, Jiale Yu, Jiaming Liu, Peng Jia, Qiuxuan Feng, Shanghang Zhang, Shuo Gu, Siwei Ma, Yueru Jia, Zezhong Qian, Zhuangzhe Wu.

**Figure 1.** Figure 1: Overview. We propose HarmoWAM, an end-to-end WAM that jointly achieves generalizable transit and precise manipulation through a world model that provides physical dynamics priors and adaptively coordinates a predictive action expert and a reactive action expert. HarmoWAM achieves SOTA performance in ID settings and exhibits a substantial advantage in OOD scenarios. Driven by the inherent differences betwe… view at source ↗

**Figure 2.** Figure 2: Framework. HarmoWAM adopts an adaptive framework that tightly integrates a generative world model with two complementary action experts. The world model provides both explicit future predictions and implicit latent representations. Conditioned on current latent features, the predictive expert generates structured actions for precise manipulation, while the reactive expert leverages future predicted frames … view at source ↗

**Figure 3.** Figure 3: The upper part presents attention map visualizations from the last-layer features of the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Generalization experiments. Red boxes highlight unseen objects, background variations, and manipulated object positions, while blue boxes indicate original training configurations. a) HarmoWAM Structure b) Efficacy of Process-Adaptive Gating c) World Model Latent Features 74 95 93 56 61 87 73 14 90 60 57 90 W/O Predictive W/O Reactive HarmoWAM S u c c e s s R a t e 76 82 59 64 44 57 59 65 Averaging Key Ave… view at source ↗

**Figure 5.** Figure 5: Ablation Study. We investigate (a) HarmoWAM Structure, (b) Efficacy of Process-Adaptive Gating, and (c) Impact of world model latent features on both action experts. The “-vid” suffix indicates that video latent features are excluded from the action expert’s conditioning. 4.4 Ablation Study To validate the effectiveness of each component, we conduct detailed ablation studies on the Put Flowers in Vase and … view at source ↗

**Figure 6.** Figure 6: Real-World robot setup and experimental assets. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Representative failure cases of the two World Action Models paradigms under OOD [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

**Figure 8.** Figure 8: Visualization of complete execution sequences on six real-world manipulation tasks. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

**Figure 9.** Figure 9: Visualization of the complete examples under three OOD settings: unseen background, [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

**Figure 10.** Figure 10: Visual comparison of generated videos under different denoising steps. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗

**Figure 11.** Figure 11: Failure case visualization of HarmoWAM. We visualize representative failures in real-world Franka experiments, with red boxes highlighting the key error regions. of the stem and make the insertion pose harder to align. Since this task requires bimanual handover, accurate relative pose recovery, and tight-tolerance insertion, it remains challenging for all evaluated models. Even when HarmoWAM maintains the… view at source ↗

read the original abstract

World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics. Current WAMs generally follow two paradigms: the "Imagine-then-Execute" approach, which uses video prediction to infer actions via inverse dynamics, and the "Joint Modeling" approach, which jointly models actions and video representations. Based on systematic experiments, we observe a fundamental trade-off between these paradigms: the former explicitly leverages world models for generalizable transit but lacks interaction precision, whereas the latter enables fine-grained, temporally coherent action generation but is constrained by the exploration space of the training distribution. Motivated by these findings, we propose HarmoWAM, an end-to-end WAM that fully leverages a world model to unify predictive and reactive control, enabling both generalizable transit and precise manipulation. Specifically, the world model provides spatio-temporal physical priors that condition two complementary action experts: a predictive expert that leverages latent dynamics for iterative action generation, and a reactive expert that directly infers actions from predicted visual evolution. To enable adaptive coordination, a Process-Adaptive Gating Mechanism is proposed to automatically determine the timing and location of switching between them. This allows the world model to drive the reactive expert to expand the exploration space and the predictive expert to perform precise interactions across different stages of a task. For evaluation, we construct three training-unseen test environments across six real-world robotic tasks, covering variations in background, position, and object semantics. Notably, HarmoWAM achieves strong zero-shot generalization across these scenarios, significantly outperforming prior state-of-the-art VLA models and WAMs by margins of 33% and 29%, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

HarmoWAM frames a real trade-off between two WAM styles and offers a gating fix, but the abstract supplies no evidence to back the 33% and 29% claims.

read the letter

The main takeaway is that this paper spots a practical tension in world action models for robotics: one style generalizes well across new scenes but lacks precision, while the other produces accurate actions but stays stuck inside the training distribution. HarmoWAM tries to combine them by letting a world model feed priors to a predictive expert and a reactive expert, then uses a new Process-Adaptive Gating Mechanism to decide when and where to switch between the two during a task. That coordination step is the concrete addition beyond the two paradigms they cite. The high-level design makes sense for tasks that need both broad transit and fine contact, and the zero-shot setup across background, position, and object changes is a reasonable testbed for real-world manipulation. The abstract does a clean job stating the motivation from their own experiments and sketching how the world model drives both experts. The soft spots are straightforward. No experimental protocol, baselines, error bars, or statistical tests appear, so the reported margins cannot be checked for fairness or robustness. It is also unclear whether the gating logic rests on independent principles or was shaped to match the outcomes. Without equations or pseudocode, the assumptions about spatio-temporal priors and correct switching timing stay unexamined. This work is for robotics groups already working on world models or vision-language-action systems. A reader hunting for architecture ideas might borrow the dual-expert plus gating sketch, but anyone planning to build on it or cite the numbers will need the full methods and data. The paper deserves peer review because the problem framing is honest and the proposed unification targets a documented limitation in the field, even if the current evidence is thin.

Referee Report

1 major / 1 minor

Summary. The paper claims that existing World Action Models exhibit a trade-off between generalizable transit in the 'Imagine-then-Execute' paradigm and precise manipulation in the 'Joint Modeling' paradigm. Motivated by this, HarmoWAM is proposed as an end-to-end WAM that leverages a world model to provide spatio-temporal physical priors conditioning a predictive expert (using latent dynamics for iterative action generation) and a reactive expert (directly inferring actions from predicted visual evolution), with a Process-Adaptive Gating Mechanism to adaptively switch between them. This enables both generalization and precision. On three training-unseen test environments across six real-world robotic tasks, it reports strong zero-shot generalization, outperforming prior SOTA VLA models and WAMs by 33% and 29% respectively.

Significance. If the results and method hold under detailed scrutiny, the work could be significant for robot learning by resolving a key trade-off in WAMs through adaptive unification of predictive and reactive control. The gating mechanism offers a potential new tool for task-stage adaptation. However, the provided manuscript supplies no supporting evidence, equations, or validation, so the significance cannot be assessed beyond the high-level motivation.

major comments (1)

[Abstract] Abstract: The abstract reports performance margins of 33% and 29% on unseen test environments but supplies no experimental details, baselines, error bars, statistical analysis, task descriptions, or implementation specifics; the central claim of superior zero-shot generalization cannot be verified or stress-tested from the given information.

minor comments (1)

[Abstract] Abstract: New components such as the 'Process-Adaptive Gating Mechanism' and 'spatio-temporal physical priors' are named without definitions, equations, or pseudocode, which reduces immediate clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for clarity in the abstract. We address the single major comment point-by-point below.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract reports performance margins of 33% and 29% on unseen test environments but supplies no experimental details, baselines, error bars, statistical analysis, task descriptions, or implementation specifics; the central claim of superior zero-shot generalization cannot be verified or stress-tested from the given information.

Authors: We agree that the abstract is intentionally concise and omits granular experimental details, as is conventional to respect length constraints. The full manuscript contains dedicated sections detailing the three training-unseen test environments, the six real-world robotic tasks (with variations in background, position, and object semantics), the baselines (prior SOTA VLA models and WAMs), error bars, statistical analysis, and implementation specifics that underpin the reported 33% and 29% improvements. These elements directly support the zero-shot generalization claims. If the referee recommends, we can revise the abstract to include one additional sentence summarizing the evaluation protocol and task coverage. revision: partial

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The abstract presents a high-level motivation from observed trade-offs in prior paradigms, followed by a proposed architecture using world-model priors and a Process-Adaptive Gating Mechanism. No equations, fitted parameters, or derivation steps are provided that reduce to self-definition or construction from inputs. The unification claim and performance results are stated as outcomes rather than tautological predictions. With only the abstract available and no self-citation chains or ansatz smuggling visible, the derivation chain remains self-contained and independent of its own results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Review is abstract-only so the ledger reflects only components explicitly named; full parameter counts and assumptions are inaccessible.

axioms (1)

domain assumption World models can supply useful spatio-temporal physical priors for conditioning action generation.
Invoked to justify conditioning the two action experts on the world model.

invented entities (1)

Process-Adaptive Gating Mechanism no independent evidence
purpose: Automatically determines timing and location for switching between predictive and reactive action experts.
New component introduced to enable adaptive coordination across task stages.

pith-pipeline@v0.9.0 · 5615 in / 1436 out tokens · 50464 ms · 2026-05-12T03:20:08.039964+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

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

IndisputableMonolith/Cost/FunctionalEquation.lean; IndisputableMonolith/Foundation/DimensionForcing.lean; IndisputableMonolith/Foundation/ArithmeticFromLogic.lean reality_from_one_distinction; washburn_uniqueness_aczel; alexander_duality_circle_linking unclear

?

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

the world model provides spatio-temporal physical priors that condition two complementary action experts: a predictive expert that leverages latent dynamics for iterative action generation, and a reactive expert that directly infers actions from predicted visual evolution. ... Process-Adaptive Gating Mechanism

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023

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

mimic-video: Video-action models for generalizable robot control beyond vlas.arXiv preprint arXiv:2512.15692,

Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, and Elvis Nava. mimic-video: Video-action models for generalizable robot control beyond vlas.arXiv preprint arXiv:2512.15692, 2025

work page arXiv 2025
[42]

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

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

work page internal anchor Pith review Pith/arXiv arXiv 2025
[43]

Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

work page 2020
[44]

Perceiver-actor: A multi-task transformer for robotic manipulation

Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Perceiver-actor: A multi-task transformer for robotic manipulation. InConference on Robot Learning, pages 785–799. PMLR, 2023

work page 2023
[45]

AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

Hengkai Tan, Yao Feng, Xinyi Mao, Shuhe Huang, Guodong Liu, Zhongkai Hao, Hang Su, and Jun Zhu. Anypos: Automated task-agnostic actions for bimanual manipulation.arXiv preprint arXiv:2507.12768, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[46]

Octo: An Open-Source Generalist Robot Policy

Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy.arXiv preprint arXiv:2405.12213, 2024. 13

work page internal anchor Pith review Pith/arXiv arXiv 2024
[47]

Wan: Open and Advanced Large-Scale Video Generative Models

Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[48]

arXiv preprint arXiv:2412.03293 , year=

Junjie Wen, Minjie Zhu, Yichen Zhu, Zhibin Tang, Jinming Li, Zhongyi Zhou, Chengmeng Li, Xiaoyu Liu, Yaxin Peng, Chaomin Shen, et al. Diffusion-vla: Scaling robot foundation models via unified diffusion and autoregression.arXiv preprint arXiv:2412.03293, 2024

work page arXiv 2024
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Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation.IEEE Robotics and Automation Letters, 2025

Junjie Wen, Yichen Zhu, Jinming Li, Minjie Zhu, Zhibin Tang, Kun Wu, Zhiyuan Xu, Ning Liu, Ran Cheng, Chaomin Shen, et al. Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation.IEEE Robotics and Automation Letters, 2025

work page 2025
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Dual-stream diffusion for world-model augmented vision-language-action model, 2025

John Won, Kyungmin Lee, Huiwon Jang, Dongyoung Kim, and Jinwoo Shin. Dual- stream diffusion for world-model augmented vision-language-action model.arXiv preprint arXiv:2510.27607, 2025

work page arXiv 2025
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Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing large-scale video generative pre-training for visual robot manipulation.arXiv preprint arXiv:2312.13139, 2023

work page internal anchor Pith review arXiv 2023
[52]

Robomind: Benchmark on multi-embodiment intelligence normative data for robot manipulation,

Kun Wu, Chengkai Hou, Jiaming Liu, Zhengping Che, Xiaozhu Ju, Zhuqin Yang, Meng Li, Yinuo Zhao, Zhiyuan Xu, Guang Yang, et al. Robomind: Benchmark on multi-embodiment intelligence normative data for robot manipulation.arXiv preprint arXiv:2412.13877, 2024

work page arXiv 2024
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Gigaworld-policy: An efficient action- centered world–action model, 2026

Angen Ye, Boyuan Wang, Chaojun Ni, Guan Huang, Guosheng Zhao, Hao Li, Hengtao Li, Jie Li, Jindi Lv, Jingyu Liu, et al. Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026

work page arXiv 2026
[54]

World action models are zero-shot policies, 2026

Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, Ayaan Malik, Kyungmin Lee, William Liang, Nadun Ranawaka, Jiasheng Gu, Yinzhen Xu, Guanzhi Wang, Fengyuan Hu, Avnish Narayan, Johan Bjorck, Jing Wang, Gwanghyun Kim, Dantong Niu, Ruijie Zheng, Yuqi Xie, Jimmy Wu, Qi ...

work page 2026
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Fast-wam: Do world action models need test-time future imagination?, 2026

Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-wam: Do world action models need test-time future imagination?, 2026

work page 2026
[56]

Sigmoid loss for language image pre-training

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. InProceedings of the IEEE/CVF international conference on computer vision, pages 11975–11986, 2023

work page 2023
[57]

Flare: Robot learning with implicit world modeling, 2025

Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, et al. Flare: Robot learning with implicit world modeling. arXiv preprint arXiv:2505.15659, 2025

work page arXiv 2025
[58]

Act2goal: From world model to general goal-conditioned policy, 2025

Pengfei Zhou, Liliang Chen, Shengcong Chen, Di Chen, Wenzhi Zhao, Rongjun Jin, Guanghui Ren, and Jianlan Luo. Act2goal: From world model to general goal-conditioned policy.arXiv preprint arXiv:2512.23541, 2025

work page arXiv 2025
[59]

Ro- bodreamer: Learning compositional world models for robot imagination.arXiv preprint arXiv:2404.12377, 2024

Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, and Chuang Gan. Ro- bodreamer: Learning compositional world models for robot imagination.arXiv preprint arXiv:2404.12377, 2024

work page arXiv 2024
[60]

Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets, 2025

Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets, 2025

work page 2025
[61]

Rt-2: Vision-language-action models transfer web knowledge to robotic control

Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In7th Annual Conference on Robot Learning, 2023. 14 Appendix A Real-World Set-up Single-Arm Configuration.As shown in Figure 6, our single-arm plat...

work page 2023
[62]

S1: grasp and place banana; S2: grasp and place carrot

Pick Fruit to Plate.The robot sequentially picks up a banana and a carrot and places them onto a plate. S1: grasp and place banana; S2: grasp and place carrot. The average length is approximately 280 steps and is sequentially evaluated. A stage is considered successful if the robot successfully grasps the corresponding object and places it stably on the p...

work page
[63]

S1: place the second can beside the first; S2: place the third can on top

Stack Coke Cans.The robot stacks three cans one by one, demanding highly precise spatial alignment. S1: place the second can beside the first; S2: place the third can on top. The average length is approximately 290 steps and is sequentially evaluated. S1 is considered successful if the robot places the second can beside the first can with stable contact a...

work page
[64]

S1: grasp bottle; S2: pour into beaker

Pour Coke into Beaker.The robot grasps a bottle and pours its contents into a beaker, testing fine- grained rotational control. S1: grasp bottle; S2: pour into beaker. The average length is approximately 310 steps and is sequentially evaluated. S1 is considered successful if the robot securely grasps and 15 lifts the bottle, and S2 is considered successfu...

work page
[65]

Yes”.The robot picks up a marker and writes “Y

Write “Yes”.The robot picks up a marker and writes “Y”, “e”, “s” on a whiteboard in sequence. S1: write “Y”; S2: write “e”; S3: write “s”. The average length is approximately 310 steps and is sequentially evaluated. A stage is considered successful if the robot writes the corresponding character legibly on the whiteboard. Dual-Arm Tasks

work page
[66]

S1: pick flower; S2: bimanual handover; S3: insert into vase

Put Flowers in Vase.The left arm picks a flower and hands it to the right arm, which inserts it into a vase, requiring precise bimanual coordination and tight-tolerance insertion. S1: pick flower; S2: bimanual handover; S3: insert into vase. The average length is approximately 280 steps and is sequentially evaluated. S1 is considered successful if the lef...

work page
[67]

S1 →S 2: pick up item and place into bag; S3 →S 4 →S 5: one arm grips the bag to hold it steady, the other grips and pulls the zipper to close

Put Items to Bag and Zip.Both arms collaborate to place items into a bag and zip it closed, which is the longest-horizon task. S1 →S 2: pick up item and place into bag; S3 →S 4 →S 5: one arm grips the bag to hold it steady, the other grips and pulls the zipper to close. The average length is approximately 400 steps and is sequentially evaluated. S1 is con...

work page