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arxiv: 2606.06870 · v2 · pith:JAKGEKH5new · submitted 2026-06-05 · 💻 cs.RO

What Is My Robot Thinking? Design Considerations for Transparent and Trustworthy Shared Autonomy

Atharv Belsare , Zohre Karimi , Connor Mattson , Rushiil Nakka , Daniel S. Brown This is my paper

Pith reviewed 2026-06-27 22:04 UTC · model grok-4.3

classification 💻 cs.RO
keywords shared autonomytransparent interfacesintent inferenceassistive roboticsuser studyfeedback modalitytrust
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The pith

Feedback on inferred robot goals improves intent alignment in shared autonomy.

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

This paper tests how different feedback designs help users understand what an assistive robot is trying to do during shared control. It compares visual and auditory feedback, as well as sparse and rich information displays, in a study with 25 participants performing manipulation tasks. The key finding is that any feedback on the inferred goal helps users align their actions better and correct the robot less often. Visual feedback is generally preferred, and the amount of detail that helps depends on how complex the task is, while showing the full set of possible goals does not always lead to better results.

Core claim

Providing feedback on the robot's inferred goal significantly improves intent alignment and reduces corrective intervention. Participants preferred visual over auditory feedback, while preferences for sparse versus rich information depended on task complexity. Revealing the full belief distribution did not consistently improve alignment or trust.

What carries the argument

Feedback designs that make the robot's inferred goal legible through visual or auditory interfaces in vision-based shared autonomy.

If this is right

  • Feedback accelerates convergence to shared goals by making inference legible.
  • Visual feedback is preferred to auditory feedback across tasks.
  • Optimal information richness varies with task complexity.
  • Maximal disclosure of the belief distribution is not required for effective coordination or trust.

Where Pith is reading between the lines

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

  • Transparency guidelines could extend to other shared control applications like driving assistance.
  • Adaptive interfaces that adjust detail based on user experience might further improve outcomes.
  • Future work could test if these effects hold when inference accuracy is lower.

Load-bearing premise

Mismatches between the robot's inferred goals and the user's intended goals are the main source of friction in coordination.

What would settle it

A replication where adding feedback on inferred goals produces no reduction in corrective interventions or no improvement in alignment.

Figures

Figures reproduced from arXiv: 2606.06870 by Atharv Belsare, Connor Mattson, Daniel S. Brown, Rushiil Nakka, Zohre Karimi.

Figure 1
Figure 1. Figure 1: (Top) The robot infers a goal from the human’s joystick input but does not communicate this inference. This can lead to misaligned goals and requires the human to guess whether the robot needs a correction. (Middle) With a visual feedback interface, the robot displays its current inferred intent, allowing the human to see what the robot believes the target to be and adjust their input accordingly. (Bottom)… view at source ↗
Figure 2
Figure 2. Figure 2: Visual Feedback Interfaces. (Left) The teleoperator’s point-of-view, showing the scene camera feed on a display alongside the physical workspace. (Middle) Visual Sparse (VS): A single bounding box highlights the object with the highest inferred intent confidence, indicating the robot’s current predicted target. (Right) Visual Rich (VR): All candidate intents are displayed with bounding boxes, object labels… view at source ↗
Figure 3
Figure 3. Figure 3: User Study Tasks. (a) Shelving task where users were asked to place objects onto designated shelves. (b) Sorting task where the robot helps the user sort multiple objects into their respective recycling bins (cardboard, plastic, metal). exceeds a predefined threshold. For each object, it commu￾nicates the label and confidence score associated with the predicted action. For example, the robot might say: “I … view at source ↗
Figure 4
Figure 4. Figure 4: Post Treatment Questionnaire and User Responses. Mean post-trial 7-point Likert responses to the survey questions shown on the left, across interface conditions in the Shelving and Sorting tasks. Error bars denote standard error. Conditions include Teleop, VOSA (no feedback), and VOSA with visual or auditory feedback as described in Sec. IV. Teleop, VOSA without feedback, and all four interface treatments—… view at source ↗
Figure 5
Figure 5. Figure 5: Feedback effects on intent alignment and correc￾tive behavior. (a) Mean intent alignment percentage (higher is better), defined as the proportion of time steps in which the robot’s inferred goal matched the task-defined user goal. (b) Mean number of intent prediction changes per trial (lower is better). Error bars denote standard error; asterisks indicate significance levels (*p < 0.05, **p < 0.01, ***p < … view at source ↗
read the original abstract

Assistive robots operating under shared autonomy must balance user control with autonomous assistance. Because robot actions depend on internal intent inference that is not directly observable, mismatches between inferred and intended goals can undermine coordination and trust. We investigate how interface-level transparency, including feedback modality (visual vs. auditory) and information richness (sparse vs. rich), shapes interaction in a vision-based shared autonomy system. In a user study with N=25 participants across two assistive manipulation tasks, we evaluate how these designs influence coordination and trust. Providing feedback significantly improves intent alignment and reduces corrective intervention, indicating that making the inferred goal legible accelerates convergence in shared control. Participants preferred visual over auditory feedback, while preferences for sparse versus rich information depended on task complexity. We also found that revealing the full belief distribution did not consistently improve alignment or trust. Together, these findings indicate that effective transparency enhances coordination primarily through goal legibility, while trust depends on task-appropriate information exposure rather than maximal disclosure. Based on these results, we outline guidelines for designing transparent shared autonomy systems.

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 / 2 minor

Summary. The manuscript reports results from a user study (N=25) examining how interface transparency designs—feedback modality (visual vs. auditory) and information richness (sparse vs. rich, including full belief distribution)—affect coordination, intent alignment, corrective interventions, and trust in a vision-based shared autonomy system for two assistive manipulation tasks. It claims that providing feedback yields statistically significant gains in alignment and reduced interventions, that visual feedback is preferred, that sparse/rich preferences are task-dependent, and that full belief distribution does not consistently help; design guidelines are derived from these observations.

Significance. If the empirical results are supported by complete statistical reporting and controls, the work offers concrete, task-sensitive evidence that legible goal inference via feedback improves shared-control coordination more effectively than maximal disclosure. This directly informs practical interface design for assistive robots and contributes empirical grounding to transparency research in HRI.

major comments (2)
  1. [Abstract] Abstract and (presumably) §4/§5: the claim of statistically significant improvements in intent alignment and reduced interventions is presented without details on the specific tests, effect sizes, confidence intervals, exclusion criteria, or raw data distributions. With N=25 this information is load-bearing for evaluating whether the central claim about feedback benefits holds.
  2. [Results] Results section (task-dependent findings): the observation that full belief distribution did not consistently improve alignment or trust is central to the guideline that 'maximal disclosure' is not required, yet the manuscript provides no quantitative breakdown of per-task belief-distribution effects or power analysis to support the 'not consistently' conclusion.
minor comments (2)
  1. Clarify how 'intent alignment' is operationalized (e.g., distance to ground-truth goal, intervention count, or subjective rating) and ensure this definition is used consistently when comparing modalities.
  2. Figure captions and legends should explicitly state whether error bars represent standard error, 95% CI, or SD, and whether statistical significance markers are corrected for multiple comparisons.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing statistical transparency and granularity. We will revise the manuscript to provide the requested details on tests, effect sizes, and per-task breakdowns while maintaining the core claims supported by our N=25 study.

read point-by-point responses

  1. Referee: [Abstract] Abstract and (presumably) §4/§5: the claim of statistically significant improvements in intent alignment and reduced interventions is presented without details on the specific tests, effect sizes, confidence intervals, exclusion criteria, or raw data distributions. With N=25 this information is load-bearing for evaluating whether the central claim about feedback benefits holds.

    Authors: We agree that full statistical reporting is necessary for evaluating the claims with N=25. In the revision we will add to §4/§5 (and reference in the abstract) the exact tests performed (e.g., paired t-tests or non-parametric equivalents), effect sizes, 95% confidence intervals, any participant exclusion criteria, and descriptive statistics or distribution summaries for the key metrics of intent alignment and corrective interventions. revision: yes

  2. Referee: [Results] Results section (task-dependent findings): the observation that full belief distribution did not consistently improve alignment or trust is central to the guideline that 'maximal disclosure' is not required, yet the manuscript provides no quantitative breakdown of per-task belief-distribution effects or power analysis to support the 'not consistently' conclusion.

    Authors: We will expand the Results section with a quantitative per-task breakdown of belief-distribution effects, including means, SDs, and direct statistical comparisons between sparse and rich conditions for each task. A post-hoc power discussion will be added to address the 'not consistently' phrasing; we note that the original design relied on pilot data rather than a priori power analysis, which we will acknowledge as a limitation. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper reports results from an empirical user study (N=25) evaluating feedback modalities in a vision-based shared autonomy system. No mathematical models, derivations, equations, parameter fittings, or uniqueness theorems are present. Central claims about improved intent alignment and reduced interventions follow directly from observed statistical outcomes across conditions, without any reduction to self-defined inputs, fitted parameters renamed as predictions, or load-bearing self-citations. The study design tests the hypotheses externally via participant data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical human-subject study with no mathematical model, fitted parameters, background axioms, or postulated entities.

pith-pipeline@v0.9.1-grok · 5726 in / 967 out tokens · 20036 ms · 2026-06-27T22:04:51.604800+00:00 · methodology

discussion (0)

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Reference graph

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