Self-Healing Action Verification against Instruction Tampering and Sensor Failures in Humanoid Robots

Summary

A preprint proposes a 'self-healing' validation pipeline featuring LIME-based encoder selection, temporally masked reconstruction, and a zero-parameter multi-scale gallery matcher. This pipeline verifies whether the movement executed by a humanoid robot truly aligns with the intended command, even if motor-sensor readings are corrupted.

Why it matters

As humanoid robots enter unstructured and safety-critical environments, a gap emerges between command verification and the verification of the physical action actually performed. Sensor drift or adversarial interference can silently corrupt this feedback loop. Demonstrating robustness across multiple failure modes on a physical platform like the Booster K1 is a concrete step beyond simulation-based benchmarks. Furthermore, a classifier with zero trainable parameters offers an attractive option for deployment scenarios where retraining is not feasible.

Highlights

• LIME, originally used for model interpretability, is repurposed to select the motor encoders carrying the most discriminative signals for action recognition.
• A temporally masked reconstruction model uses joint coordination to fill in corrupted joint observations, effectively 'repairing' the sensor stream before classification.
• The final action matcher is a multi-scale gallery recognizer with zero trainable classifier parameters, avoiding the burden of task-specific training.

Critical take

The entire evaluation is based on a single, training-oriented humanoid robot (Booster K1), and the work remains a preprint that has not undergone peer review. Therefore, the reported 92.6% robustness rate should be approached with caution. Additionally, the term 'self-healing' presents a rather standard reconstruction and classification pipeline in an exaggerated manner. The scalability of the gallery-based matcher to high-degree-of-freedom and high-speed movements is also not addressed.