Robustness Under Uncertainty: Adversarial and Out-of-Distribution Stress Testing of HY-Embodied-0.5

TL;DR: What breaks HY-Embodied-0.5? Let’s systematically stress-test the model with adversarial perturbations and out-of-distribution scenarios, using frameworks like differential testing (Louloudakis et al., 2023) and embodied chain-of-thought analysis (Zawalski et al., 2024).

Research Question: Where does HY-Embodied-0.5 fail, and how can we systematically identify and mitigate robustness failures in complex, real-world settings?

Hypothesis: Comprehensive stress-testing will reveal specific blind spots and failure modes in HY-Embodied-0.5, particularly under adversarial or unforeseen input conditions, which can then be mitigated via targeted augmentation or model retraining.

Experiment Plan: - Develop an evaluation suite combining adversarial attacks (visual, language, and sensor noise), out-of-distribution tasks, and cascading failure scenarios.

• Employ differential testing to compare behaviors across model variants and benchmarks.
• Utilize Embodied Chain-of-Thought Reasoning to trace and interpret failure chains.
• Document and categorize failure cases, propose and test targeted robustness enhancements.

References:

• Louloudakis, N., Gibson, P., Cano, J., & Rajan, A. (2023). A Differential Testing Framework to Evaluate Image Recognition Model Robustness. arXiv.org.
• Zawalski, M., Chen, W., Pertsch, K., Mees, O., Finn, C., & Levine, S. (2024). Robotic Control via Embodied Chain-of-Thought Reasoning. Conference on Robot Learning.
• X. TencentRobotics et al. (2026). HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents.