GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates

Noosaga Placements

• Roboticssubfield95%
  The paper addresses failure anticipation in vision-language navigation agents, a core robotics problem, and is categorized under cs.RO.
  arXiv:2606.20479v1 [cs.RO]Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillation, stagnation, or inefficient detours.
• Probabilistic Roboticsframework90%
  The paper uses a Kalman filter to model distance-to-goal dynamics, which is a probabilistic state estimation technique central to probabilistic robotics.
  GroundControl models distance evolution using a constant-velocity Kalman filter and combines normalized innovation statistics with complementary trajectory features capturing progress, monotonicity, path efficiency, and oscillatory behavior.We capture this local structure using the state xt = [dt , d˙t ]⊤ , where d˙t denotes the instantaneous rate of change.
• Vision-Language Modelsframework85%
  The paper is situated within the context of vision-language models (VLMs) used for navigation, and evaluates uncertainty for such models.
  Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillation, stagnation, or inefficient detours.We evaluate episode-level uncertainty quality on EB-Navigation across five splits... The navigation policy is fixed; no retraining or policy modification is performed.
• Mobile Roboticssubfield80%
  The work focuses on navigation in embodied environments, which is a key aspect of mobile robotics.
  Each episode i ∈ {1, . . . , N } specifies an initial robot pose, a natural-language instruction Li , and a target object definition.At each time step t = 1, . . . , Ti , the agent receives an egocentric observation ot and selects a discrete action ut ∈ A, where |A| = 8 corresponds to forward, backward, left, right, rotate-left, rotate-right, look-up, and look-down motions of fixed magnitude.
• Natural Language Processingsubfield70%
  The paper uses vision-language models (VLMs) for navigation, which involves natural language understanding and grounding.
  Modern vision-language models (VLMs) can interpret multi-step instructions, resolve object references in egocentric observations, and execute navigation primitives with competitive Success weighted by Path Length (SPL).We evaluate episode-level uncertainty quality on EB-Navigation across five splits... The navigation policy is fixed; no retraining or policy modification is performed.
• Robustness and Assuranceframework60%
  The paper's goal of anticipating navigation failures and enabling selective deployment relates to robustness and assurance in AI systems.
  Reliable deployment, therefore, requires uncertainty signals that anticipate emerging failure dynamics during execution rather than reflect only instantaneous action entropy.To evaluate uncertainty quality independently of task success, we formalize Selective Risk–Coverage Navigation (SRCN), a protocol that measures how effectively an uncertainty score ranks episodes by failure or inefficiency using risk–coverage curves and AURC / E-AURC summaries.

Abstract

Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillation, stagnation, or inefficient detours. Reliable deployment, therefore, requires uncertainty signals that anticipate emerging failure dynamics during execution rather than reflect only instantaneous action entropy. We introduce \emph{GroundControl}, a trajectory-consistent uncertainty estimator defined as statistical deviation from nominal goal-directed distance-to-goal dynamics aggregated over an episode. GroundControl models distance evolution using a constant-velocity Kalman filter and combines normalized innovation statistics with complementary trajectory features capturing progress, monotonicity, path efficiency, and oscillatory behavior. The resulting uncertainty score reflects geometric and temporal inconsistency in navigation behavior rather than local prediction dispersion. To evaluate uncertainty quality independently of task success, we formalize \emph{Selective Risk--Coverage Navigation (SRCN)}, a protocol that measures how effectively an uncertainty score ranks episodes by failure or inefficiency using risk--coverage curves and AURC / E-AURC summaries. Across five EB-Navigation splits ($N=300$ episodes), trajectory-consistent uncertainty achieves near-oracle ordering under success-based selective risk, with weighted-average $\mathrm{E\text{-}AURC}_{\mathrm{SR}}=0.0024$ for the GPT-4o model, substantially outperforming entropy-, conformal-, and heuristic baselines. Under SPL-based selective evaluation, GroundControl consistently achieves the lowest AURC and E-AURC across models and navigation splits. These results show that modeling deviation from goal-directed dynamics provides an interpretable and robust signal for anticipating navigation failures in vision-language agents.

Paper Context