Oct 11, 2026 · Artificial Intelligence

LIBERO-MAX: 224K Simulations Expose Robot Policy Fragility Under Mid-Task Changes

LIBERO-MAX evaluates 14 robot policies across 8,000 paired scenarios and 224,000 simulations, showing all policies lose 11–25.7 percentage points in success rate when environments change mid-execution, and even higher observation frequency fails to recover the gap.

LIBERO-MAXdynamic environmentmid-execution disturbance

0 likes · 11 min read

LIBERO-MAX: 224K Simulations Expose Robot Policy Fragility Under Mid-Task Changes

AsiaInfo Technology: New Tech Exploration

Aug 7, 2026 · Artificial Intelligence

World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training

The article analyzes the fundamental bottleneck of current Physical AI post‑training, where VLA models lack foresight, and presents World Action Models (WAM) – both Cascaded and Joint architectures – along with four post‑training pathways that combine evaluation and imagination to break the data‑collection loop.

Imitation LearningPhysical AIReinforcement Learning

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World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training

world action models

LIBERO-MAX: 224K Simulations Expose Robot Policy Fragility Under Mid-Task Changes

World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training

World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training