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arxiv: 2604.23620 · v1 · submitted 2026-04-26 · 💻 cs.RO

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

Haoming Xu , Lei Lei , Jie Gu , Chu Tang , Jingmin Chen , Ruiqi Wang This is my paper

Pith reviewed 2026-05-08 05:57 UTC · model grok-4.3

classification 💻 cs.RO
keywords robotic manipulationbehavioral phasingmove-then-operatedual-expert policyphase selectorvision language actionhigh-precision manipulationRoboTwin benchmark
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The pith

Decoupling robotic manipulation into distinct move and operate phases improves success rates and training efficiency.

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

The paper proposes Move-Then-Operate, a vision-language-action framework that splits robotic manipulation into a coarse relocation phase and a contact-critical interaction phase. This is implemented through a dual-expert policy controlled by a learnable phase selector, with phase labels created automatically by an MLLM system using end-effector velocity and task breakdowns. The separation provides an inductive bias that helps the system handle the different dynamics of each phase more effectively than single-policy approaches. On the RoboTwin2 benchmark, this leads to higher success rates while requiring less data and fewer training steps, suggesting that modeling behavioral phases explicitly can make robot learning more like human task execution.

Core claim

By introducing an explicit separation between the move phase for coarse relocation and the operate phase for precise interactions, the Move-Then-Operate framework uses a dual-expert policy with a learnable selector to achieve an average success rate of 68.9 percent on manipulation tasks, outperforming monolithic baselines and models trained on substantially more data.

What carries the argument

A dual-expert policy architecture routed by a learnable phase selector, supported by automatically generated phase labels from an MLLM pipeline conditioned on end-effector velocity and subtask decomposition.

If this is right

  • The method achieves 24% higher success than the monolithic baseline on RoboTwin2.
  • It matches or exceeds performance of models trained on 10 times more data.
  • Peak performance is reached in 40% fewer training steps.
  • The structural inductive bias from phase disentanglement aids high-precision manipulation tasks.

Where Pith is reading between the lines

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

  • This approach may generalize to other robot tasks involving distinct coarse and fine motor stages.
  • Automatic phase labeling could lower the cost of preparing training data for complex behaviors.
  • Testing the phase selector on real-world robots with varying speeds might reveal limits in the velocity-based labeling.
  • The framework could inspire similar disentanglements in other AI domains like game playing or autonomous driving.

Load-bearing premise

The phase labels generated by the MLLM pipeline based on velocity and subtask cues accurately reflect human motor patterns and provide reliable training signals.

What would settle it

A controlled experiment showing no significant difference in success rates or training efficiency when using a single expert policy instead of the dual-expert setup with phase selector would indicate that the behavioral phasing is not the key factor.

Figures

Figures reproduced from arXiv: 2604.23620 by Chu Tang, Haoming Xu, Jie Gu, Jingmin Chen, Lei Lei, Ruiqi Wang.

Figure 1
Figure 1. Figure 1: Schematic of robotic manipulation phases via equidistant sampling. The sequence comprises a fast and rapid move phase characterized by large displacements, and an operate phase focused on fine-grained and tenuous precision. low-level actions under a unified objective (Bjorck et al., 2025). Long-range motion and contact-rich dexterity are thus tightly coupled within such a formulation. Human manipulation, h… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Schematic representation of a two-phase process superimposed at uniform intervals and frequencies from human manipulation videos. The upper panel depicts a rapid approach toward the object, while the lower panel illustrates slow and meticulous adjustments. (b) A schematic diagram of our motivation. The amplitudes vary across different phases; specifically, joint learning and normalization cause actions… view at source ↗
Figure 3
Figure 3. Figure 3: Overall model architecture. Dashed lines indicate data flow. And the rightmost images show the varying manipulation scales of the two experts. Only a single expert is active during actual inference. a structural decoupling can let specialized experts master move and operate without end to end compromise. 3. Preliminaries 3.1. Problem Formulation We consider the problem of language-vision-conditioned roboti… view at source ↗
Figure 4
Figure 4. Figure 4: Data flow during the training process. relevant expert: vpred(σ, xσ, Ct) = X z∈Z I[z = yt] · vθzˆ (σ, xσ, Ct). (6) This formulation ensures ∇θzˆ is non-zero only for the matched expert, effectively orthogonalizing parameter up￾dates. The loss is computed as the batch-level masked MSE against the target vector field ut = at − x0: Laction = Eσ,x0  ∥Mt ⊙ (vpred − ut)∥ 2 2 ∥Mt∥1 + ϵ  , (7) where Mt handles v… view at source ↗
Figure 5
Figure 5. Figure 5: Success rates of our model at 60k, 80k, and 100k training steps during the multi-task pre-training phase. π0 Baseline represents the reported performance of the monolithic baseline. we maintain an equal sampling ratio between the move and operate phases. This training stage proceeds for 100,000 steps. Subsequently, we fine-tune the model individually on 8 specific tasks using the same trajectory data, trai… view at source ↗
Figure 6
Figure 6. Figure 6: Success rates during task-specific fine-tuning at 5k, 10k, 15k, and 20k steps. The ’w/o finetune’ column denotes the performance of the pre-trained multi-task model. tuned for 20k steps. Similar efficiency is observed in Press Stapler, which reaches 91% early in the training process. This accelerated learning suggests that the decoupled Move￾Then-Operate architecture simplifies the joint optimization of tr… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of task click alarmclock, click bell and press stapler view at source ↗
Figure 8
Figure 8. Figure 8: Visualization of place bread basket. We visualize the entire execution process for task in the test set in view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of place burger fries and place cans plasticbox. 12 view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of move pillbottle pad and place empty cup. 13 view at source ↗
Figure 11
Figure 11. Figure 11: The foundational prompt used to instruct the MLLM for zero-shot phase segmentation. Refinement Prompt (Triggered on Validation Failure) Trigger Condition: The Validator V detects a structural or physical violation in the previous output. Injected User Message: Previous attempt issues: <Error Log from Validator>. Fix by ensuring: 1. Do not duplicate a phase type inside a subtask; instead start a new subtas… view at source ↗
Figure 12
Figure 12. Figure 12: The dynamic feedback prompt. The <Error Log> is replaced by the actual exception message (e.g., ”Subtask 2 end frame exceeds total frames”), guiding the model to fix the specific issue. B. Detail of Auto-Labeling During the annotation process, we utilized Seed 1.6 Vision as the backbone model. We applied a sampling rate of 5 fps for all samples, with each trajectory capped at a maximum of 64 frames. This … view at source ↗
read the original abstract

We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coarse relocation (move) and contact-critical interaction (operate). Unlike monolithic policies that conflate these heterogeneous regimes, our architecture employs a dual-expert policy routed by a learnable phase selector, introducing a structural inductive bias that isolates phase-specific dynamics. Phase labels are automatically generated via an MLLM-based pipeline conditioned on lightweight contextual cues such as end-effector velocity and subtask decomposition to ensure alignment with human motor patterns. Evaluated on the RoboTwin2 benchmark, our method achieves an average success rate of $68.9\%$, outperforming the monolithic $\pi_0$ baseline by $24\%$. It matches or exceeds models trained on $10\times$ more data and reaches peak performance in $40\%$ fewer training steps, demonstrating that architectural disentanglement of move and operate phases is a highly effective and efficient strategy for mastering high-precision manipulation.

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.

Referee Report

2 major / 1 minor

Summary. The paper introduces Move-Then-Operate, a vision-language-action framework for robotic manipulation that decouples the task into 'move' (coarse relocation) and 'operate' (contact-critical interaction) phases. It uses a dual-expert policy architecture with a learnable phase selector, where phase labels are generated automatically by an MLLM pipeline using cues like end-effector velocity and subtask decomposition. Evaluated on RoboTwin2, it reports an average success rate of 68.9%, a 24% improvement over the monolithic π0 baseline, performance comparable to models trained on 10 times more data, and convergence in 40% fewer steps.

Significance. If the empirical claims hold after proper validation, the work demonstrates that explicit behavioral phasing can serve as an effective inductive bias in VLA policies, yielding both higher success rates and substantially improved sample efficiency on high-precision manipulation benchmarks. This could encourage broader adoption of phase-disentangled architectures in robotics, particularly where human-like motor patterns are hypothesized to aid learning.

major comments (2)
  1. [Abstract] Abstract: The central performance claims (68.9% success, +24% over π0, 40% fewer steps, parity with 10× data models) are presented without any description of the evaluation protocol, number of trials per task, statistical significance tests, variance across seeds, or ablation studies that isolate the contribution of the phase selector versus the dual-expert structure alone. These omissions make it impossible to assess whether the reported gains are attributable to behavioral phasing or to other unablated factors.
  2. [Method (phase label generation)] Phase-label pipeline (method description): The claim that MLLM-generated labels (conditioned on end-effector velocity and subtask cues) align with human motor patterns and supply a reliable supervisory signal for the learnable phase selector is load-bearing for the paper's interpretation of the results. No quantitative validation against human annotations, inter-annotator agreement, or label-accuracy metrics is provided; without this, systematic misalignment remains a plausible alternative explanation for any observed gains.
minor comments (1)
  1. [Abstract] Abstract: The RoboTwin2 benchmark is referenced but no breakdown by task category, difficulty, or number of tasks is given, which would help contextualize the aggregate 68.9% figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help improve the clarity and rigor of our work. We provide point-by-point responses to the major comments and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claims (68.9% success, +24% over π0, 40% fewer steps, parity with 10× data models) are presented without any description of the evaluation protocol, number of trials per task, statistical significance tests, variance across seeds, or ablation studies that isolate the contribution of the phase selector versus the dual-expert structure alone. These omissions make it impossible to assess whether the reported gains are attributable to behavioral phasing or to other unablated factors.

    Authors: We agree that the abstract would benefit from additional context on the experimental setup to better support the performance claims. In the revised version, we will modify the abstract to include a concise description of the evaluation protocol on the RoboTwin2 benchmark, the number of evaluation trials per task, and references to the variance and statistical analyses reported in the main body. We will also highlight that ablations isolating the phase selector's contribution are detailed in the experiments section of the manuscript. revision: yes

  2. Referee: [Method (phase label generation)] Phase-label pipeline (method description): The claim that MLLM-generated labels (conditioned on end-effector velocity and subtask cues) align with human motor patterns and supply a reliable supervisory signal for the learnable phase selector is load-bearing for the paper's interpretation of the results. No quantitative validation against human annotations, inter-annotator agreement, or label-accuracy metrics is provided; without this, systematic misalignment remains a plausible alternative explanation for any observed gains.

    Authors: We recognize the importance of validating the MLLM-generated phase labels against human judgments to confirm their alignment with human motor patterns. The current approach uses established cues such as end-effector velocity thresholds and subtask decomposition, which have been shown in prior robotics work to correlate with phase transitions. However, we agree that explicit quantitative validation is lacking. In the revised manuscript, we will add an analysis comparing the labels to human annotations on a sampled set of trajectories, including accuracy and agreement metrics. This will strengthen the interpretation of the results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on external benchmark comparisons without self-referential derivations

full rationale

The paper describes an architectural approach to robotic manipulation that separates move and operate phases, with phase labels generated by an MLLM pipeline and performance measured via success rates on the RoboTwin2 benchmark against external baselines like π0. No equations, derivations, or analytical predictions appear in the provided text. Claims of improved success rates (68.9%, +24% over baseline) and faster convergence are supported by empirical results rather than any reduction of outputs to fitted inputs or self-citations by construction. The method's inductive bias is presented as a design choice, not a derived necessity, and label generation serves as training supervision without circular self-definition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract supplies no explicit free parameters, mathematical axioms, or external benchmarks beyond the high-level description of the dual-expert architecture and MLLM labeling pipeline.

axioms (1)
  • domain assumption MLLM-generated phase labels conditioned on velocity and subtask cues align with human motor patterns
    This assumption underpins the automatic creation of training targets for the phase selector.
invented entities (1)
  • learnable phase selector routing dual-expert policies no independent evidence
    purpose: To isolate phase-specific dynamics and introduce structural inductive bias
    New architectural component introduced to decouple move and operate regimes

pith-pipeline@v0.9.0 · 5480 in / 1354 out tokens · 90593 ms · 2026-05-08T05:57:57.290341+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

8 extracted references

  1. [1]

    Segment the entire video into consecutive subtasks that fully cover [0, total frames-1] without gaps/overlaps

  2. [2]

    • A subtask may have ONE phase OR TWO phases (exactly one move and one operate)

    For each subtask, output phases (temporal slices within the subtask): •phase typein{move,operate}. • A subtask may have ONE phase OR TWO phases (exactly one move and one operate). • Do NOT repeat the samephase typewithin a subtask. If you observe another movement, start a NEW subtask

  3. [3]

    Identify theprimary armand give a concise English description

  4. [4]

    Predict normalized coordinates fortarget object axisand gripper ends

  5. [5]

    subtask": 1,

    Ensure the entire video contains AT LEAST ONEmovephase. Output Schema:Output STRICTLY a JSON array. No extra text. [ { "subtask": 1, "subtask_description": "...", "primary_arm": "left/right/both/unknown", "phases": [ { "phase_type": "move", "start_frame_idx": 0, "end_frame_idx": 45, ... }, { "phase_type": "operate", "start_frame_idx": 46, "end_frame_idx":...

  6. [6]

    Do not duplicate aphase typeinside a subtask; instead start a new subtask for the extra action

  7. [7]

    Each subtask has 1 phase (move/operate) or exactly 2 (one move + one operate) in real temporal order

  8. [8]

    Return JSON array only

    The entire video contains at least onemovephase. Return JSON array only. Figure 12.The dynamic feedback prompt. The <Error Log> is replaced by the actual exception message (e.g., ”Subtask 2 end frame exceeds total frames”), guiding the model to fix the specific issue. B. Detail of Auto-Labeling During the annotation process, we utilized Seed 1.6 Vision as...