ResearchResearch paperRobotics & Embodied AI1 source · Oct 17, 2026

LIBERO-Pro2.0: A Benchmark for Evaluating the Robustness of Embodied Foundation Models

In this work, we introduce LIBERO-Pro2.0, a comprehensive benchmark for systematically evaluating the robustness of embodied foundation models under distribution shifts.

Key points

  • LIBERO has emerged as a widely adopted benchmark for evaluating robotic manipulation policies.
  • However, strong performance on LIBERO does not necessarily indicate robust embodied capabilities, as models may exploit benchmark-specific regularities rather than generalize to diverse operating conditions.
  • LIBERO-Pro2.0 encompasses 22 static perturbations and 20 runtime interventions across six dimensions: Condition, Environment, Observation, Execution, Robot State Configuration, and Language, yielding 1,648 validated shifted task instances.
  • We evaluate 17 representative robot policies spanning vision-language-action models, world-action models, and robustness-oriented policies.

Sources (1)

Extractive summary: sentences quoted from the sources.

Before this

  1. Oct 17, 2026FlashRT-PAI: Fast Kernels Are Not Fast Pipelines for Physical AI Inference
  2. Oct 16, 2026Look Before You Act: Bridging Active Perception and Vision-Language-Action Models via Observation-Aligned Data Adaptation
  3. Oct 16, 2026MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models
  4. Oct 15, 2026REKI: Registering Knowledge in VLAs
  5. Oct 15, 2026FastVLA: Task-Conditioned and Temporally Adaptive Visual Token Compression for Efficient Vision-Language-Action Models
  6. Oct 15, 2026VeriVLA: Adaptive Test-Time Verification for Flow-Based VLAs with Latent World Models

Related