PaperlayerView exact revision on arXiv

Source-linked AI summary

Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal

Rifa Ferzana

arXiv:2609.05593v1cs.ROcs.AI

TL;DR

Navigation-failure prediction remains underexplored relative to navigation performance, and failure severity gives the task consequence beyond a binary label. The paper weights training by failure consequences and evaluates the approach; adaptive weighting reaches high-severity recall of 0.998, though its matched-false-positive advantage is modest.

  • Problem

    Navigation-failure prediction remains underexplored relative to navigation performance, while the paper frames failure forecasting as needing to account for the seriousness of an event.

    Source: §1 · pp. 1–2

  • Method

    The framework weights training samples by failure consequence, using severity-based costs to emphasize consequential failures.

    Source: §1 · p. 2, §3.1 · p. 3, §3.2 · p. 3

  • Results

    0.998 HSR and missed consequence cost 82 are reported for adaptive risk sensitivity; under matched false-positive conditions, the discriminative advantage narrows to 0.986 versus 0.984.

    Source: §5.7 · p. 8, §8 · p. 13

  • Takeaways & Limitations

    Adaptive consequence modelling is best understood as allocating conservatism by physical context rather than providing a uniformly superior classifier.

    Source: §8 · p. 13

    The primary evaluation is in simulation, so prospective physical-platform deployment remains necessary; handcrafted features partly depend on the potential-field controller.

    Source: §7 · p. 12

Abstract

from arXiv · show

Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low speed under reliable sensing is not equivalent to the same event during rapid motion, close obstacle approach or degraded perception. This paper reframes navigation failure prediction as consequence-sensitive forecasting. We first establish a fixed baseline in which training weights are modulated by categorical severity, then introduce an adaptive extension defining a state-dependent consequence function combining severity with normalised velocity, obstacle proximity and sensing uncertainty, together with a risk-sensitivity term that rises as conditions deteriorate. We evaluate on 2,000 simulated differential-drive episodes (~1,000,000 timesteps) using episode-level GroupKFold, with external validation on the UCI SCITOS G5 dataset. Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; the adaptive extension reaches 0.998 and 82. Under matched false-positive conditions, however, the discriminative advantage is modest (0.986 versus 0.984), so most of the gain reflects a more conservative operating point rather than better ranking. The effect is consistent across all five folds and stable across a threefold span of context coefficients. Because the primary simulation produced no collisions, we add a controlled extension in which 108 of 600 episodes terminate in contact: collision recall rises from 0.850 to 0.966 (fixed) and 0.984 (adaptive), with missed collision cost falling from 1,000 to 105, at false-positive rates of 0.413 and 0.799, respectively. Context-dependent consequence modelling thus provides a principled mechanism for allocating conservatism by physical risk.

1 Introduction

Navigation-failure prediction remains less explored than navigation performance, while conventional metrics treat errors as equally costly despite sharply different physical consequences. The paper reframes the task as consequence-sensitive forecasting and proposes cost-sensitive training and safety-oriented evaluation metrics.

1 Introduction

  • Symmetric accuracy, precision and F1 treat errors equally, although a path deviation has minor efficiency cost and an undetected imminent collision risks physical harm.The passage notes that differential misclassification costs have been used in healthcare AI.Source: §1 · p. 1
  • The paper frames navigation failure prediction around both whether failure occurs and the seriousness of failing to recognise it.This shifts the question from binary failure classification to consequence-sensitive forecasting.Source: §1 · pp. 1–2
  • The proposed framework modulates training sample weights by failure consequence, upweighting high-severity events to prioritize dangerous-failure detection at a modest cost to overall performance.The contributions also include high-severity recall, cost detection rate and cost-weighted F1, evaluated on simulated trajectories and the UCI Wall-Following Robot dataset.Source: §1 · p. 2

2 Related Work

Prior robot failure-prediction work frames detection as binary classification, while safe reinforcement learning learns policies rather than predicting failures in deployed systems. Cost-sensitive methods address differential consequences but remain underused in robotics.

2.1 Robot Navigation and Failure Detection

  • Sünderhauf et al. and Daftry et al. studied robot failure detection but treated it as binary classification without distinguishing severity.The related work situates these studies alongside geometric planning, potential fields, probabilistic localisation and learning-based navigation.Source: §2.1 · p. 2

2.2 Safe Reinforcement Learning

  • Safe reinforcement-learning frameworks and benchmarks learn safe policies, whereas this work predicts failures in deployed systems as a complementary supervisory layer.The passage names constrained optimisation frameworks and Safety-Gymnasium and OmniSafe [14].Source: §2.2 · p. 2

2.3 Cost-Sensitive Learning

  • Cost-sensitive learning spans trees, boosting and deep networks, yet robotics underuses it and commonly evaluates failure prediction with metrics that ignore differential consequences.The passage describes this work as extending consequence-asymmetry approaches from healthcare AI to autonomous robotics.Source: §2.3 · p. 2

3 Methodology

The methodology forecasts failures over a lookahead window and assigns consequence costs by severity. It weights training according to consequence and operating context, with a simulated navigation environment and window-based features.

3.1 Problem Formulation

  • Each observation window yields a binary forecast of failure in a future interval, with each positive instance assigned the worst failure’s consequence cost in that interval.The formulation uses feature vector x(t) from [t−w, t] and labels y(t) for failures in [t, t+h].Source: §3.1 · p. 3

3.2 Cost-Sensitive Sample Weighting

  • Sample weights equal 1 + λ · c(i), so higher-consequence failures receive greater training weight; λ was fixed at 2.0 in primary experiments.Weights are normalized to unit mean; the framework also uses cost-proportional oversampling for the MLP, which changes the training distribution and may confound its improvements.Source: §3.2 · p. 3

3.3 Context-Dependent Consequence

  • Dynamic consequence scales base severity by normalized speed, sensing uncertainty and proximity risk, with proximity receiving the greatest fixed coefficient.The coefficients are α = 0.5, β = 1.0 and γ = 1.5; context terms are scaled using training-fold statistics only.Source: §3.3 · p. 4

3.4 Adaptive Risk Sensitivity

  • Risk sensitivity increases with sensing degradation and obstacle proximity, and the adaptive weight is normalized to unit mean within each training fold.The adaptive weighting increases conservatism in high-risk states without imposing explicit constraints on the navigation controller.Source: §3.4 · p. 4

3.5 Simulation Environment

  • The simulator uses a potential-field-controlled differential-drive robot navigating a 20×20 metre arena with eight randomly placed obstacles.Across 2,000 episodes (~1,000,000 timesteps), no collisions occurred; all high-severity simulated samples were near-misses.Source: §3.5 · p. 4

3.6 Feature Extraction

  • Each 10-timestep window provides 21 motion, proximity, trajectory and environmental-context features, with a 50-timestep (~5 s) prediction horizon.Range-derived features include Gaussian noise to prevent ground-truth leakage, and stride 3 reduces redundancy between samples.Source: §3.6 · p. 5

4 Experimental Setup

Experiments compare standard and cost-sensitive classifier configurations using safety-oriented metrics and episode-level grouped validation. Matched-condition ablations compare fixed and adaptive consequence weighting while controlling false-positive rates through a nested calibration split.

4.1 Classifiers

  • Four classifiers—Logistic Regression, Random Forest, XGBoost and an MLP—were evaluated with standard imbalance handling and cost-sensitive weighting at λ = 2.0.Hyperparameters were held constant across experiments.Source: §4.1 · p. 5

4.2 Evaluation Metrics

  • High-Severity Recall measures recall on consequence-cost-5-or-greater samples, Cost Detection Rate measures intercepted consequence cost, and Cost-Weighted F1 weights true positives and false negatives by 1 + c(i).These metrics supplement accuracy, precision, recall, F1 and AUROC.Source: §4.2 · p. 5

4.3 Evaluation Protocol

  • Five-fold episode-level GroupKFold keeps each episode’s samples within one partition; results are means ± standard deviations across folds.Training-fold statistics standardize features, and majority-class subsampling yields 53,088 evaluation samples.Source: §4.3 · pp. 5–6

4.4 Adaptive-Method Ablation Protocol

  • Five matched conditions compare class-balanced weighting, threshold-moving, fixed consequence weighting, dynamic consequence and dynamic consequence with adaptive risk sensitivity.Matched-FPR comparisons select thresholds on a separate episode-level calibration partition, then freeze them for held-out testing.Source: §4.4 · p. 6

5 Results

The results report standard-classification metrics, then show cost-sensitive gains in high-severity detection and cost detection alongside a false-positive tradeoff.

5.1 Standard Classification Performance

  • Standard-classification results are reported as mean ± standard deviation under episode-level GroupKFold on 2,000 episodes.Source: Table 1 · p. 6, §5.1 · p. 6

5.2 Cost-Sensitive Performance

  • High-severity recall for cost-sensitive Logistic Regression rises from 0.851 to 0.985, while missed cost falls from 1,940 to 313.The reported comparison is from the cost-sensitive performance evaluation.Source: Table 2 · p. 6, §5.2 · p. 7

5.3 Safety–Efficiency Tradeoff

  • Cost-sensitive Logistic Regression raises CDR from 0.860 to 0.977 on the CDR-versus-false-positive plane.Source: §5.3 · p. 7

5.4 Inference Latency

Adaptive weighting improves the full ablation's safety metrics, with a small matched-FPR advantage; the section also reports latency, sensitivity, and SCITOS G5 validation.

5.4 Inference Latency

  • Mean inference time per sample is below 0.001 ms for Logistic Regression, 0.003 ms for MLP, 0.005 ms for XGBoost, and 0.029 ms for Random Forest.The paper reports that all models support real-time operation as a supervisory safety layer.Source: §5.4 · p. 7

5.5 Sensitivity to λ and Cost Ratios

  • As λ increases from 0 to 4, HSR rises from 0.823 to 0.995 while F1 falls from 0.851 to 0.780; even the 1:2:5 cost ratio achieves HSR 0.960 versus the standard baseline's 0.851.Source: §5.5 · p. 7

5.6 Validation on Real Robot Data

  • SCITOS G5 validation uses 5,441 samples from a 24-ultrasound-sensor wall-following dataset, with stratified five-fold cross-validation because it is a single continuous traversal without episode structure.The labels map Move-Forward to cost 0, slight turns to cost 1, and Sharp-Right-Turn to cost 5; the latter denotes emergency intervention rather than failure.Source: §5.6 · pp. 7–8, Table 3 · p. 8

5.7 Adaptive Consequence-Sensitive Ablation

  • High-severity recall rises from 0.851 to 0.918 to 0.985 across class-balanced, threshold-moving, and fixed weighting; dynamic consequence reaches 0.995 and adaptive weighting 0.998.Missed consequence cost falls from 1,940 to 82, a twenty-fourfold reduction relative to class-balanced training.Source: Table 4 · p. 8, §5.7 · p. 8
  • At test FPR ≈ 0.333, adaptive weighting reaches HSR 0.986 ± 0.002 versus 0.984 ± 0.003 for fixed weighting, with identical CW-F1 of 0.935.The substantial missed-cost reduction is primarily attributed to a more conservative operating point rather than better sample ranking.Source: Table 5 · p. 9, §5.7 · p. 9

5.8 Paired Fold-Level Comparison

Adaptive weighting’s gains were consistent across held-out folds, robust to coefficient scaling, and varied across risk strata. The risk-stratified comparison found its largest gains in some low-risk proximity and sensing bands, while velocity gains were greatest at high speed.

5.8 Paired Fold-Level Comparison

  • Across all five folds, adaptive weighting improved HSR and CDR and reduced missed cost, with ΔHSR of +0.011 to +0.016 and missed-cost reductions of 175–264 units per fold.The paired differences compare adaptive with fixed weighting under episode-level GroupKFold.Source: Table 6 · p. 9, §5.8 · p. 9

5.9 Coefficient Robustness

  • HSR ranged from 0.995 to 0.999 as context coefficients scaled from 0.5× to 1.5×, while larger coefficients produced monotonically more conservative behavior.The coefficients were fixed a priori and not selected using these results.Source: Table 7 · p. 9, §5.9 · p. 9

5.10 Risk-Stratified Analysis

  • Adaptive HSR rose from 0.768 to 0.973 at low proximity risk and from 0.861 to 0.984 at low sensing uncertainty, while high-velocity gains exceeded low-velocity gains.In high-proximity and high-uncertainty bands, both methods reached HSR ≥ 0.998 with negligible differences; velocity gains were +0.065 at high velocity and +0.003 at low velocity.Source: Table 8 · p. 10, §5.10 · p. 10

5.11 Collision-Inclusive Evaluation

A controlled collision-inclusive extension tests the methods in episodes that terminate in contact, which the primary simulation did not produce. Adaptive weighting achieves higher collision recall and lower missed collision cost than fixed weighting, but at a substantially higher false-positive rate.

5.11 Collision-Inclusive Evaluation

  • The controlled extension produced 108 contact episodes among 600 episodes; the primary simulation produced no collisions.The extension changed the repulsion model while retaining the primary study’s thresholds, features, costs, and GroupKFold protocol.Source: §5.11 · p. 10
  • Adaptive weighting’s FPR was 0.799 versus 0.413 for fixed weighting, and the authors describe fixed weighting as more practical for most deployments.They consider the adaptive operating point defensible only when collisions are genuinely catastrophic relative to interruption.Source: §5.11 · p. 11
  • Collision recall rose from 0.850 to 0.966 with fixed weighting and 0.984 with adaptive weighting, while missed collision cost fell from 1,000 to 225 and 105, respectively.The adaptive result was reported at FPR 0.799.Source: Figure 2 · p. 11, §5.11 · p. 11

6 Discussion

The discussion argues that cost-sensitive evaluation exposes safety-relevant misses and that adaptive weighting mainly offers a way to choose a more conservative operating point. Its practical relevance depends on the deployment’s tolerance for false alarms and on the limits of the evaluation evidence.

6.1 The Case for Cost-Sensitive Evaluation

  • Standard metrics left Logistic Regression’s missed high-severity failures obscure: it missed 14.9%, amounting to 1,940 units of missed consequence cost, despite F1 = 0.853 and AUROC = 0.943.The passage notes these events carry five times the consequence cost of path deviations.Source: §6.1 · p. 11

6.2 Architecture-Dependent Sensitivity

  • Cost-sensitive weighting had the largest effect on Logistic Regression and the smallest on XGBoost, which already performed near-perfectly on high-severity events in standard mode.The discussion attributes this contrast to XGBoost’s complex decision boundaries and native class-imbalance handling, versus Logistic Regression’s linear boundary.Source: §6.2 · p. 11

6.3 What Adaptive Weighting Does and Does Not Provide

  • At its natural operating point, adaptive weighting reduced missed consequence cost from 313 to 82, but matched-FPR results show only a small HSR difference and identical cost-weighted F1.At equal false-alarm rates, HSR was 0.986 versus 0.984 and cost-weighted F1 was 0.935 for both; most of the larger gain reflects a more conservative threshold rather than better discrimination.Source: §6.3 · pp. 11–12
  • Risk-stratified results characterize model behavior, not why events occur; explaining the latter would require trajectory-level analysis.Adaptive gains concentrate in states that appear benign on proximity or sensing, while velocity shows larger gains at higher speed.Source: §6.3 · p. 12

6.4 Implications for Robotic Deployment

  • A cost-sensitive prediction module could monitor sensor and motion data alongside an existing navigation stack and alert when predicted high-consequence risk exceeds a threshold.The discussion reports current minimum range and sensor noise as dominant features, with sub-millisecond inference.Source: §6.4 · p. 12

7 Limitations and Future Work

The evaluation remains simulation-based despite external validation on UCI SCITOS G5, and the authors identify physical-platform validation and broader controller and system settings as future work.

7 Limitations and Future Work

  • The primary evaluation is in simulation; external validation on UCI SCITOS G5 does not remove the need for prospective physical-platform deployment.The handcrafted feature set is partly tied to the potential-field controller and would require adaptation to latent representations for learning-based controllers.Source: §7 · p. 12
  • The three-level severity taxonomy could extend to continuous cost functions, while context parameters fixed a priori remain an optimisation target.Source: §7 · p. 12
  • Collision-inclusive results use a modified controller and represent a different operating regime, so they should not be compared directly with primary-simulation results.Source: §7 · p. 12
  • Future work includes physical-platform validation using datasets such as TUM RGB-D or EuRoC MAV, plus reinforcement-learning integration for closed-loop risk-aware navigation.Source: §7 · p. 12
  • Future work also includes multi-robot systems, where failure cost depends on spatial configuration.Source: §7 · p. 13

8 Conclusion

The conclusion reports improved high-severity detection and lower missed consequence cost with fixed and adaptive weighting, while noting trade-offs and the value of safety-oriented metrics. It frames adaptive weighting as allocating conservatism by physical context rather than as a uniformly superior classifier.

8 Conclusion

  • Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; adaptive weighting reaches 0.998 and 82.Source: §8 · p. 13
  • Cost-weighted F1 peaks at fixed weighting (0.935) rather than adaptive (0.921), while the matched false-positive comparison is 0.986 versus 0.984.The effect is consistent across 5/5 folds and stable across a threefold coefficient span.Source: §8 · p. 13
  • In the collision-inclusive extension, collision recall is 0.984 and missed collision cost falls from 1,000 to 105, at a false-positive rate of 0.799.The passage states this false-positive rate is defensible only where contact is catastrophic.Source: §8 · p. 13
  • The paper argues that robotics should adopt safety-oriented evaluation metrics alongside standard metrics because not all errors are equal.Source: §8 · p. 13
Loading 2609.05593v1…