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arxiv: 2605.24350 · v1 · pith:KN3EREQ2new · submitted 2026-05-23 · 💻 cs.RO · cs.HC

PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration

Chengbo He , Sheng Li , Chenyang Ma , Bochao Zou , Li Sun , Jiansheng Chen , Junliang Xing , Yuanchun Shi
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Huimin Ma
This is my paper

Pith reviewed 2026-06-30 13:31 UTC · model grok-4.3

classification 💻 cs.RO cs.HC
keywords proactive askinghuman-robot collaborationcontinual task assistanceclarification utilityask-or-act frameworkreinforcement learningmulti-day scenariospartial observations
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The pith

PACT lets robots decide when to ask for clarification using past interactions to improve long-term assistance accuracy.

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

The paper introduces PACT as an ask-or-act framework for robots assisting humans across multiple days under partial information. It evaluates whether current observations plus interaction history provide enough context before deciding to seek clarification or proceed with action. This addresses cases where user routines are initially unknown and passive inference alone proves ineffective. A reinforcement learning version is implemented and tested, along with a new metric for clarification utility that weighs accuracy gains against the cost of extra questions. Multi-day embodied experiments show consistent gains over passive baselines in both accuracy and utility.

Core claim

PACT is an ask-or-act framework that determines whether clarification should be sought before taking action by leveraging current observations together with accumulated interaction history to evaluate contextual sufficiency, enabling the robot to provide more reliable assistance and progressively adapt to the user over time; its primary learned instantiation uses reinforcement learning, and experiments in multi-day embodied collaboration scenarios show it improves both assistance accuracy and clarification utility compared with passive inference baselines.

What carries the argument

The ask-or-act decision that evaluates contextual sufficiency from observations and history to choose between seeking clarification or acting.

If this is right

  • Robots achieve higher assistance accuracy than passive inference methods in partial-observation settings.
  • Clarification utility improves by balancing accuracy gains against the frequency of requests.
  • The robot adapts progressively to unknown user traits across multiple days of collaboration.
  • Alternative decision mechanisms can be compared directly under the same ask-or-act structure.

Where Pith is reading between the lines

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

  • The same decision structure could reduce repeated errors in other uncertain collaborative settings such as home assistants.
  • Over longer time scales, fewer incorrect actions might increase user willingness to rely on the robot.
  • The framework could be tested with changing user preferences to check whether history still supports good ask-or-act choices.

Load-bearing premise

Current observations together with accumulated interaction history are sufficient to judge contextual sufficiency for reliable ask-or-act decisions.

What would settle it

A multi-day trial in which PACT's ask decisions produce equal or lower assistance accuracy than a passive inference baseline that never asks.

Figures

Figures reproduced from arXiv: 2605.24350 by Bochao Zou, Chengbo He, Chenyang Ma, Huimin Ma, Jiansheng Chen, Junliang Xing, Li Sun, Sheng Li, Yuanchun Shi.

Figure 1
Figure 1. Figure 1: Overview of PACT. PACT studies cross-day proactive asking for continual task assistance in human-robot collaboration. The robot interacts with the human partner across days and observes multiple within-day episodes, such as morning and afternoon activities. At each time point, it combines the current human context with accumulated cross-day interaction history to infer the human’s intent and task needs. Be… view at source ↗
Figure 2
Figure 2. Figure 2: PACT framework and architecture. PACT uses current observations and accumulated cross-day interaction history to infer intent and task needs before acting. A shared multimodal backbone provides representations to the intent, task, and ask heads; the ask head decides whether clarification is needed at the intent or task level. After assistance execution, interaction outcomes and human feedback are written b… view at source ↗
Figure 3
Figure 3. Figure 3: Intent-level performance under day-spanning human-robot collaboration. Intent-level F1 evaluates whether the assistant infers the human partner’s high-level intent. Rows correspond to the two collaboration types, and columns correspond to the four human-scene settings. Solid curves denote ASK-based PACT instantiations, while dashed curves denote non-clarification variants [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 4
Figure 4. Figure 4: Task-level performance under day-spanning human-robot collaboration. Task-level F1 evaluates whether the assistant infers the concrete assistance need before acting. Rows correspond to the two collaboration types, and columns correspond to the four human-scene settings. Solid curves denote ASK-based PACT instantiations, while dashed curves denote non-clarification variants. Non-clarification baselines and … view at source ↗
Figure 5
Figure 5. Figure 5: Clarification efficiency in continual human-robot assistance. This figure complements the intent- and task-level results in Figs. 3 and 4 by comparing downstream task accuracy with clarification frequency. Left: average task accuracy versus ASK rate across PACT instantiations and baselines. Right: daily Clarification Utility, computed as task accuracy divided by 1+ASK rate. Higher utility indicates stronge… view at source ↗
Figure 6
Figure 6. Figure 6: Simplified intent-stage prompt structure for the single-step prompt ASK baselines. The [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sub-question selection prompt for ASK-ProactiveCoT. The companion solve prompt then [PITH_FULL_IMAGE:figures/full_fig_p026_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Branch scoring prompt for ASK-ToT. For each of the [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Verification-step generation prompt for ASK-UoT. After this call, each candidate step [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Intent-level accuracy across collaboration settings. This figure provides an accuracy￾based counterpart to [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Task-level accuracy across collaboration settings. This figure provides an accuracy￾based counterpart to [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Task accuracy versus ASK rate by collaboration setting. Each point corresponds to one method under one collaboration type and human-scene setting. The x-axis reports ASK rate as the percentage of interaction slots with clarification, and the y-axis reports task-level accuracy. Points closer to the upper-left region indicate better clarification efficiency: higher assistance accuracy with fewer questions … view at source ↗
Figure 13
Figure 13. Figure 13: Per-setting Clarification Utility across collaboration settings. Each subplot reports Clarification Utility for ASK-based PACT instantiations under one collaboration type and one human￾scene setting. The same-human settings are shown over days, whereas the cross-human settings are shown over day–human segments. Higher values indicate stronger task assistance under a lower clarification burden [PITH_FULL_… view at source ↗
Figure 14
Figure 14. Figure 14: ASK usage for collaboration type 1, setting 1. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: ASK usage for collaboration type 1, setting 2. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p030_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: ASK usage for collaboration type 1, setting 3. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: ASK usage for collaboration type 1, setting 4. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: ASK usage for collaboration type 2, setting 1. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: ASK usage for collaboration type 2, setting 2. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p031_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: ASK usage for collaboration type 2, setting 3. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p031_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: ASK usage for collaboration type 2, setting 4. The three panels report intent-level ASK decisions, task-level ASK decisions, and overall ASK decisions over the interaction sequence using 13-step moving averages [PITH_FULL_IMAGE:figures/full_fig_p031_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: ASK policy quality for collaboration type 1, setting 1. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: ASK policy quality for collaboration type 1, setting 2. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence [PITH_FULL_IMAGE:figures/full_fig_p032_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: ASK policy quality for collaboration type 1, setting 3. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence [PITH_FULL_IMAGE:figures/full_fig_p032_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: ASK policy quality for collaboration type 1, setting 4. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: ASK policy quality for collaboration type 2, setting 1. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence [PITH_FULL_IMAGE:figures/full_fig_p033_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: ASK policy quality for collaboration type 2, setting 2. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence [PITH_FULL_IMAGE:figures/full_fig_p033_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: ASK policy quality for collaboration type 2, setting 3. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence. 33 [PITH_FULL_IMAGE:figures/full_fig_p033_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: ASK policy quality for collaboration type 2, setting 4. The two panels report cumulative intent-level and task-level ASK rates over the interaction sequence [PITH_FULL_IMAGE:figures/full_fig_p035_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: ASK impact for collaboration type 1, setting 1. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p035_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: ASK impact for collaboration type 1, setting 2. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p035_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: ASK impact for collaboration type 1, setting 3. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p035_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: ASK impact for collaboration type 1, setting 4. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: ASK impact for collaboration type 2, setting 1. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p036_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: ASK impact for collaboration type 2, setting 2. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p036_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: ASK impact for collaboration type 2, setting 3. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions [PITH_FULL_IMAGE:figures/full_fig_p036_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: ASK impact for collaboration type 2, setting 4. The four panels report semantic similarity gain, intent F1 gain, task F1 gain, and average Clarification Utility gain after ASK decisions. 36 [PITH_FULL_IMAGE:figures/full_fig_p036_37.png] view at source ↗
read the original abstract

Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. However, human traits and routines are often unknown at the beginning of collaboration, making passive infer-then-act assistance ineffective and inefficient. To address this challenge, we study a cross-day proactive asking setting for continual task assistance and propose PACT (Proactive Asking for Continual Task Assistance), an ask-or-act framework that determines whether clarification should be sought before taking action. PACT leverages current observations together with accumulated interaction history to evaluate contextual sufficiency, enabling the robot to provide more reliable assistance and progressively adapt to the user over time. We implement its primary learned instantiation using reinforcement learning and evaluate alternative instantiations under the same framework. To assess such behavior, we further introduce a clarification utility metric that quantifies the trade-off between assistance accuracy and the frequency of clarification requests. Experiments in multi-day embodied collaboration scenarios demonstrate that, compared with passive inference baselines, PACT consistently improves both assistance accuracy and clarification utility, highlighting the importance of proactive asking in continual human-robot collaboration.

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

3 major / 2 minor

Summary. The paper proposes PACT, an ask-or-act framework for proactive clarification in cross-day human-robot collaboration. It uses RL (and alternative instantiations) to decide whether to seek clarification or act, based on current observations plus accumulated interaction history, to handle unknown user traits. A clarification utility metric is introduced to quantify accuracy-clarification trade-offs. Experiments in multi-day embodied scenarios claim consistent gains in assistance accuracy and utility over passive inference baselines.

Significance. If the experimental support holds after clarification, the work addresses a practical gap in long-term HRC where passive inference fails under partial information and unknown routines. The proactive framework and utility metric could inform more adaptive robot policies; the multi-day setting is a strength for continual collaboration claims.

major comments (3)
  1. [§3, §4.1] §3 (PACT framework) and §4.1 (RL instantiation): the state representation fed to the RL policy is not specified, nor is the reward function that encodes the accuracy-vs-clarification trade-off. These omissions are load-bearing because the central claim that 'current observations together with accumulated interaction history' suffice for reliable ask-or-act decisions cannot be evaluated or reproduced without them.
  2. [§4.3, Table 2] §4.3 (experiments) and Table 2: no ablation isolates the contribution of cross-day history versus single-day observations, and no statistical tests or confidence intervals are reported for the claimed 'consistent improvement.' This directly undermines the strongest claim that PACT outperforms passive baselines in multi-day scenarios.
  3. [§4.2] §4.2 (baselines): the passive inference baselines are described only at a high level; without explicit state representations or learning procedures matching those used in PACT, it is impossible to determine whether reported gains arise from the proactive mechanism or from unstated differences in feature engineering or data requirements.
minor comments (2)
  1. [§3.3] The clarification utility metric is introduced without an explicit equation; adding a numbered equation would improve clarity when comparing to accuracy alone.
  2. [Figure 3] Figure 3 (example trajectories) would benefit from explicit annotation of ask versus act decisions to illustrate the policy behavior.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback. We address each of the major comments below and plan to revise the manuscript to improve clarity and rigor.

read point-by-point responses

  1. Referee: [§3, §4.1] §3 (PACT framework) and §4.1 (RL instantiation): the state representation fed to the RL policy is not specified, nor is the reward function that encodes the accuracy-vs-clarification trade-off. These omissions are load-bearing because the central claim that 'current observations together with accumulated interaction history' suffice for reliable ask-or-act decisions cannot be evaluated or reproduced without them.

    Authors: We agree that explicit details on the state representation and reward function are essential for reproducibility. In the revised manuscript, we will provide a precise definition of the state as a vector combining current observations and interaction history features, along with the reward function that balances assistance accuracy with a cost for clarification requests. This will be added to Sections 3 and 4.1. revision: yes

  2. Referee: [§4.3, Table 2] §4.3 (experiments) and Table 2: no ablation isolates the contribution of cross-day history versus single-day observations, and no statistical tests or confidence intervals are reported for the claimed 'consistent improvement.' This directly undermines the strongest claim that PACT outperforms passive baselines in multi-day scenarios.

    Authors: We acknowledge these omissions in the experimental analysis. We will add an ablation study to isolate the effect of cross-day history and include statistical tests with confidence intervals in the revised Table 2 and Section 4.3 to support the claims of consistent improvement. revision: yes

  3. Referee: [§4.2] §4.2 (baselines): the passive inference baselines are described only at a high level; without explicit state representations or learning procedures matching those used in PACT, it is impossible to determine whether reported gains arise from the proactive mechanism or from unstated differences in feature engineering or data requirements.

    Authors: We will expand the description of the passive inference baselines in Section 4.2 to include their specific state representations, learning procedures, and how they process interaction history, ensuring they are comparable to PACT's setup. revision: yes

Circularity Check

0 steps flagged

No circularity; framework claims rest on experimental evaluation without derivation reductions

full rationale

The provided abstract and description present PACT as an RL-learned ask-or-act policy using observations plus history, with gains shown via multi-day experiments against passive baselines. No equations, state representations, reward functions, or derivations appear that would reduce any claimed prediction or sufficiency evaluation to a fitted parameter or self-citation by construction. The central assumption about contextual sufficiency is stated as an input to the framework rather than derived from it, and no self-citation chains or ansatzes are invoked in the text. This matches the default expectation of a non-circular empirical proposal paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the RL instantiation and contextual sufficiency evaluation are described at a conceptual level without detailing fitting procedures or background assumptions.

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