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Multimodal Progress-Residual Fusion for Anticipating Navigation Retreat in Robotic Flexible Endoscopy

Chi Kit Ng, Tianchun Wu, Jiatan Sun, Yuming Sun, Jianru Zhang, Hongliang Ren

arXiv:2610.31028v1cs.RO

TL;DR

Flexible endoscopy makes commanded motion an ambiguous guide to distal progress, motivating anticipation of an operator’s retreat from causal multimodal histories. MPRF combines a nominal-progress residual with commands, EM tracking, and video; it achieves 0.580 AUPRC, with complete multimodal histories providing the main ranking advantage and the residual’s increment over raw fusion uncertain.

  • Problem

    Operators must judge whether intended motion produces useful progress, and retreat anticipation requires relating commands, distal response, and visual context.

    Source: §I · p. 1

  • Method

    MPRF combines a nominal-progress residual with causal histories of commands, EM tracking, and video to predict operator retreat.

    Source: §I · p. 1, §VI · p. 6

  • Results

    MPRF reaches 0.580 AUPRC and exceeds A+V, the strongest reduced-modality control, by 0.069 [0.016, 0.146], while its 0.016 gain over Raw fusion has an interval including zero.

    Source: B. Multimodal Complementarity and the… · pp. 4, 6

  • Takeaways & Limitations

    Complete multimodal histories provide the principal ranking advantage, while warning timing remains a separate design variable from endpoint ranking.

    Source: B. Multimodal Complementarity and the… · p. 4, §VI · p. 6

    Evidence is retrospective and limited to single-operator colon-phantom navigation on a fixed episode partition; prospective assistance studies remain needed to assess clinical utility.

    Source: §VI · p. 6

Abstract

from arXiv · show

Flexible robotic endoscopes provide access through tortuous anatomy, where friction, deformation, and wall interaction separate commanded insertion from useful distal progress. Operators interpret this mismatch and retreat to recover a navigable configuration. Recognizing the approach to that decision requires relating motor intent to distal response and the local visual scene. We introduce Multimodal Progress-Residual Fusion (MPRF), a causal model that anticipates operator retreat from commands, electromagnetic (EM) tracking, and endoscopic video. A nominal-progress model predicts the response expected in normal or successful navigation; a temporal predictor combines its EM residual with all three observed modalities. In a 150-episode colon-phantom collection, MPRF achieves 0.580 +/- 0.007 AUPRC across three seeds, compared with 0.511 for the strongest reduced-modality control. Complete multimodal histories provide the principal ranking advantage, while the residual adds an explicit intent-response representation with a small, uncertain increment over raw fusion. Analysis of frozen alarms relates warning timing to the behavioral label and explains the increase in event recall under a longer matching window. These results establish a basis for behavioral assistance during operator-controlled navigation.

I. INTRODUCTION

Flexible endoscopes require operators to judge whether commanded motion is producing useful distal progress. This section frames retreat anticipation as a short-horizon task that relates commands, tip response, and visual context.

I. INTRODUCTION

  • Friction, backlash, deformation, and environmental interaction can make the same insertion command produce tip translation, bending, or little progress.Limited displacement can also accompany deliberate inspection or viewpoint correction, so motion must be interpreted with the eliciting command and scene.Source: §I · p. 1
  • A pre-retreat cue flags a developing pattern associated with abandoning the current attempt, while the operator retains action authority.The proposed cue can prompt view inspection and preparation for reorientation.Source: §I · p. 1
  • The research gap is anticipating a short-horizon retreat decision by relating operator commands, tip response, and camera observations.Existing work addresses contact observability, continuum-state estimation, or expected-versus-measured response.Source: §I · p. 1
  • The retreat label is the recorded transition from manual navigation to the retreat controller, preserving the operator’s decision and providing a log index for causal training.It includes recovery and repositioning, and the evaluation also expands the matching window for the same predictions.Source: §I · p. 1
  • MPRF learns nominal EM and visual increments from normal and successful navigation, then fuses the EM progress residual with commands, pose, and visual features.The sensing roles are commands for intent, EM pose for distal response, and video for local visual context.Source: §I · p. 1, Figure 1 · p. 2
  • The study contributes a log-derived retreat-anticipation task, a context-conditioned residual representation, and matched evaluation of sensor complementarity, residual contribution, and alarm timing.Episode-level resampling assesses the sensitivity of ranking contrasts, while probability traces explain how the fixed alarm rule produces warnings.Source: §I · p. 1

II. RELATED WORK

Prior work motivates operator assistance and sensing for flexible robotic endoscopy. MPRF focuses on anticipating a human-controlled navigation transition using contextual interpretation of expected and observed response.

A. Robotic Endoscopy and Operator Assistance

  • Robotic endoscopes combine flexible actuation with distal steering and insertion, while MPRF predicts a forthcoming operator transition and supplies an advisory probability.Its supervision comes from recorded control-state changes within the autonomy spectrum.Source: A. Robotic Endoscopy and Operator… · p. 2

B. Sensing and Expected Response

  • Expected-versus-observed body response motivates the progress residual, but friction, steering, tracker variation, and successful exploration mean multimodal context is needed to interpret discrepancies.MPRF learns that behavioral interpretation from causal histories.Source: B. Sensing and Expected Response · p. 2

C. Multimodal Temporal Prediction

The paper distinguishes retreat anticipation from prior surgical activity and workflow prediction. It uses causal multimodal histories and weak labels derived from logged navigation behavior.

C. Multimodal Temporal Prediction

  • Unlike prior phase, tool, workflow, or activity prediction, the target here is an imminent navigation-strategy change interpreted through commands, EM motion, and video context.Matched modality controls test retaining each stream, and raw multimodal fusion isolates the additional residual channel.Source: C. Multimodal Temporal Prediction · p. 2

A. Causal Inputs and Weak Behavioral Labels

  • Each synchronized record combines motor commands, EM pose, and image features from a frozen MobileNetV3-Small encoder projected to 32 dimensions.An eligible attempt is a manual-navigation segment of at least 32 rows; model histories use 16 rows, and the 10-row horizon is nominally 0.5 seconds at 20 Hz.Source: A. Causal Inputs and Weak Behavioral… · p. 2

B. Nominal Progress and Residual Fusion

MPRF first estimates nominal progress and then uses the discrepancy between expected and observed EM change as a residual in a causal multimodal predictor. The auxiliary visual-increment objective and frozen first stage separate nominal-response learning from retreat prediction.

B. Nominal Progress and Residual Fusion

  • Stage A predicts nominal EM and visual increments from prior state and current command, fitting Normal and successful navigation with dimension-averaged L1 losses before being frozen.Its visual-increment head supplies auxiliary supervision.Source: B. Nominal Progress and Residual Fusion · pp. 2–3, Figure 2 · p. 3
  • Stage B processes 16 causal tokens with a GRU and predicts future retreat through a normalized 48-unit GELU head, dropout, and sigmoid.The fused tokens combine current commands, EM pose, visual features, and the residual; Stage B uses positive-weighted binary cross-entropy.Source: Figure 2 · p. 3, B. Nominal Progress and Residual Fusion · p. 3
  • The observed-minus-predicted EM increment is the progress residual, which is retained alongside raw commands, pose, and visual features so context informs its predictive meaning.The residual is initialized to zero at an attempt’s first row.Source: B. Nominal Progress and Residual Fusion · p. 3, Figure 2 · p. 3

A. Platform, Recording Conditions, and Partition

The study uses 150 colon-phantom episodes with synchronized video, distal EM tracking, and commands, and evaluates retreat anticipation on episode-disjoint partitions with matched multimodal models.

A. Platform, Recording Conditions, and Partition

  • 150 episodes comprise 50 each of Normal navigation, Narrow-lumen success, and Narrow-lumen failure, with retreat-controller transitions supplying the predictive target.One operator recorded the collection; acquisition categories also organize nominal-progress training.Source: A. Platform, Recording Conditions, and… · p. 3
  • The evaluation partition contains 5,392 causal endpoints, including 210 positives (3.89%), from 31 recordings and 21 events.Predictive metrics use censored pre-retreat windows; the full collection has 112 raw retreat transitions and 96 eligible events.Source: A. Platform, Recording Conditions, and… · p. 3

B. Matched Models and Evaluation

  • Six models compare video, two-modality controls, raw A+E+V fusion, and MPRF across seeds 17, 42, and 2701 using the original split.Controls share the temporal encoder, head, and fitting recipe.Source: B. Matched Models and Evaluation · p. 3
  • Figure 3 pairs endoscopic views with synchronized EM displacement and command traces; the logged first retreat begins at 4.256 s.Predictive labels use controller transitions and censored pre-onset segments, rather than the qualitative phase shading.Source: Figure 3 · p. 4
  • AUPRC is the primary metric for the sparse positive class, with paired 95% percentile intervals estimated from 2,000 stratified whole-episode resamples.Each resample shares episode multiplicities across models and averages over three fitted seeds; seed SD captures fitting variability.Source: B. Matched Models and Evaluation · p. 4
  • Alarm thresholds and persistence are selected on validation against a 0.5 false-alarm/min target, then evaluated with 10- and 20-row matching windows on the same saved scores and alarms.All MPRF runs selected the fallback threshold 0.95 with persistence five.Source: B. Matched Models and Evaluation · p. 4

V. RESULTS

The recordings show why commanded motion and distal progress need temporal and behavioral context: low progress can occur during successful passage as well as failure, while full-recording summaries differ across navigation outcomes.

A. Why Intent Requires Response and Scene Context

  • Normal navigation shows sustained displacement with intermittent commands, while successful narrow-lumen passage includes a plateau during continued insertion followed by renewed motion.The failure example proceeds to retreat and steering, illustrating that reduced progress takes meaning from its temporal context and ongoing operator intent.Source: A. Why Intent Requires Response and… · p. 4
  • Median command magnitudes are 79.99, 16.79, and 40.68 for Normal, Pass, and Failure, while median net EM translations are 113.09, 104.33, and 13.46 mm.Normal and Pass reach comparable final displacement under very different command magnitudes; Failure has intermediate command magnitude but small final shift, consistent with return motion in complete recordings.Source: A. Why Intent Requires Response and… · p. 4
  • The median command–increment correlation shifts from 0.237 in Pass to −0.223 in Failure, and the first two summary PCs explain 91.3% of variance.These are descriptive full-recording summaries, including recovery; censored-window comparisons test anticipation directly.Source: A. Why Intent Requires Response and… · p. 4

B. Multimodal Complementarity and the Residual Contribution

MPRF has the strongest reported AUPRC, while comparisons indicate that retaining all three modalities accounts for the principal ranking advantage and that the residual’s additional contribution is uncertain.

B. Multimodal Complementarity and the Residual Contribution

  • Figure 4 characterizes full recordings with one point per episode and boxplots of command magnitude, net tip translation, command–increment correlation, and PCA summaries.The summaries include recovery; the figure distinguishes Normal, Pass, and Failure.Source: Figure 4 · p. 5
  • 0.580 AUPRC for MPRF compares with 0.564 for Raw A+E+V and 0.511 for the strongest reduced-modality control, A+V.MPRF exceeds A+V by 0.069 [0.016, 0.146]; Table I reports matched 10-row anticipation results on 5,392 evaluation endpoints.Source: B. Multimodal Complementarity and the… · p. 4, Table I · p. 5
  • Removing video, EM, or commands lowers mean AUPRC to 0.440, 0.511, or 0.476, respectively, while the MPRF–Raw gap is 0.016.Figure 5 compares gains from added modalities and the residual; its EM and residual intervals include zero.Source: B. Multimodal Complementarity and the… · p. 4, Figure 5 · p. 5
  • Figure 6 shows a warning 19 rows before retreat entering the expanded matching window, while the final-window peak in another case remains 0.522.The frozen gate uses threshold 0.95, persistence five, and a 20-row refractory interval.Source: Figure 6 · p. 6
  • Table II reports frozen-alarm recall and false alarms per nominal minute under two matching windows.The same saved scores and alarms are assessed under the two windows; the longer window changes the behavioral matching target.Source: Table II · p. 6
  • Paired intervals include zero for Raw versus A+V (0.053 [−0.014, 0.150]) and MPRF versus Raw (0.016 [−0.029, 0.041]), supporting a smaller, uncertain residual contribution.Intervals are positive for Raw versus A+E and E+V; Raw leads F1 and MCC at threshold 0.5, while MPRF leads mean AUPRC, AUROC, and Brier score.Source: B. Multimodal Complementarity and the… · p. 6

C. Alarm Timing Determines Event Attribution

With the predictor and alarm gate fixed, expanding the matching window raises event recall, showing that alarm attribution depends on the retreat timestamp. Alarm occurrences cluster in the added 10-row band, while the gate’s probability evolution and refractory rule shape warning timing.

C. Alarm Timing Determines Event Attribution

  • 54.0% recall at 0.97 FAPM under 20-row matching, versus 15.9% at 2.78 FAPM under 10-row matching, with the same frozen MPRF alarms.Expanded matching raises positive prevalence to 7.75%; the predictor and gate stay fixed, so the comparison measures sensitivity to the retreat timestamp.Source: C. Alarm Timing Determines Event… · p. 6
  • 24 alarm occurrences fall 11–20 rows before retreat, compared with 10 within 10 rows and 12 earlier.These are repeated model–attempt evaluations of the same physical collection.Source: C. Alarm Timing Determines Event… · p. 6
  • A 19-row-early alarm triggers the refractory interval, suppressing a second warning in the trained window; another attempt reaches only 0.522 within its final 10 rows.A strict detection occurs four rows before onset, illustrating how probability evolution and the gate jointly determine sparse advisory events.Source: C. Alarm Timing Determines Event… · p. 6

VI. DISCUSSION AND CONCLUSION

The results support complementary sensing, while the residual’s additional contribution over raw fusion is smaller and uncertain. The evidence is retrospective and limited to single-operator colon-phantom navigation, leaving prospective assistance utility for future testing.

VI. DISCUSSION AND CONCLUSION

  • Complementary sensing provides the strongest evidence, while paired episode intervals place the residual’s additional contribution over raw fusion on a smaller, uncertain scale.Alarm timing is a separate design variable from endpoint ranking.Source: §VI · p. 6
  • The empirical scope is retrospective, single-operator colon-phantom navigation on a fixed episode partition.Predictive results concern strictly pre-retreat histories, and the behavioral target captures operator judgment.Source: §VI · p. 6
  • Prospective assistance studies should test prespecified warning times alongside nuisance burden, workload, navigation efficiency, and physical-load endpoints.These outcomes provide the next test of clinical utility.Source: §VI · p. 6
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