RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Robots

Humphrey Munn^1,2 · Brendan Tidd² · Peter Böhm^1,2 · Marcus Gallagher¹ · David Howard²

¹The University of Queensland ²CSIRO Robotics, Data61

arXiv preprint 2602.01515 · February 2026

Abstract

Deploying learned control policies on humanoid robots is challenging: policies that appear robust in simulation can execute confidently in out-of-distribution (OOD) states after Sim-to-Real transfer, leading to silent failures that risk hardware damage. Although anomaly detection can mitigate these failures, prior methods are often incompatible with high-rate control, poorly calibrated at the extremely low false-positive rates required for practical deployment, or operate as black boxes that provide a binary stop signal without explaining why the robot drifted from nominal behavior.

We present RAPT, a lightweight, self-supervised deployment-time monitor for 50 Hz humanoid control. RAPT learns a probabilistic spatio-temporal manifold of nominal execution from simulation and evaluates execution-time predictive deviation as a calibrated, per-dimension signal. This yields (i) reliable online OOD detection under strict false-positive constraints and (ii) a continuous, interpretable measure of Sim-to-Real mismatch that can be tracked over time to quantify how far deployment has drifted from training. Beyond detection, we introduce an automated post-hoc root-cause analysis pipeline that combines gradient-based temporal saliency derived from RAPT's reconstruction objective with LLM-based reasoning conditioned on saliency and joint kinematics to produce semantic failure diagnoses in a zero-shot setting.

We evaluate RAPT on a Unitree G1 humanoid across four complex tasks in simulation and on physical hardware. In simulation across four Isaac Lab tasks, RAPT improves True Positive Rate (TPR) by 37% over the strongest baseline at a fixed episode-level false positive rate of 0.5%. On hardware, it achieves 89% TPR across 78 trials — with a single false positive in over an hour of nominal operation — and reaches 75% semantic failure-diagnosis accuracy across 21 categories on a challenging OOD subset, using only proprioceptive data.

Method

A residual state encoder, recurrent latent bridge, and probabilistic decoder model nominal trajectories with a heteroscedastic Gaussian likelihood. At deployment, the per-dimension negative log-likelihood feeds three calibrated statistical gates — a per-dimension spike gate, a global drift gate, and a physical range gate — evaluated in ~1.6 ms per control step. Thresholds are calibrated on a nominal batch from simulation, or from a brief verified real-world run to absorb static deployment offsets. When a detection fires, integrated gradients through time localize which observation dimensions drove the anomaly and when, and a multimodal LLM classifies the physical root cause from a fixed 21-class failure taxonomy.

RAPT architecture, hierarchical OOD detection pipeline, and diagnosis pipeline — **(A)** RAPT OOD-detection architecture. **(B)** Hierarchical OOD pipeline using three statistical gates for real-time (~1.6 ms) monitoring. **(C)** Detected anomalies trigger gradient-based saliency generation for zero-shot diagnosis via a multimodal LLM.

Results

0.92AUROC in simulation (4 tasks, 5 seeds)

+37%TPR over strongest baseline @ 0.5% FPR

56 / 63real-world anomalous trials detected (88.9% TPR)

1 FPin over an hour of nominal operation (15 trials)

1.6 msper 50 Hz control step

100%top-3 root-cause accuracy with visual keyframe

Real-world highlights

Payload mismatch with live root-cause evidence. An unmodelled 7.5 kg payload causes the robot to stumble; RAPT's per-dimension error crosses the calibrated NLL threshold and stops the policy, while the top-K temporal saliency (left) localizes the responsible observation channels online.

Nominal operation (5×). Continuous omni-directional walking with no false positives — one flag in over an hour of nominal trials overall.

Collision. The robot collides with an obstruction; RAPT detects the off-nominal contact and triggers a controlled safe fall.

Deformable ground. Stepping onto an unmodelled soft mattress is flagged from proprioception alone.

Safety response. On detection, RAPT can trigger a predefined behavior — here, walking back away from the anomaly.

Real-world OOD scenarios: policy latency, strong push, collision, deformable ground — each detected and met with a safety response — RAPT detects diverse real-world out-of-distribution events on a Unitree G1 — policy latency, strong pushes, collisions, deformable ground — and triggers predefined safety responses (safe stop, safe fall, walk-away recovery) before hardware damage occurs.

Citation

If you find RAPT useful, please cite:

@article{munn2026rapt,
  title   = {RAPT: Model-Predictive Out-of-Distribution Detection and Failure
             Diagnosis for Sim-to-Real Humanoid Robots},
  author  = {Munn, Humphrey and Tidd, Brendan and B{\"o}hm, Peter and
             Gallagher, Marcus and Howard, David},
  journal = {arXiv preprint arXiv:2602.01515},
  year    = {2026}
}

License

The paper is distributed under the arXiv.org perpetual non-exclusive license (arXiv:2602.01515v1). The code release is available under the BSD 3-Clause License.