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The Hong Kong University of Science and Technology
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Counterfactual outcomes reveal that existing embodied agents miss 20% of potential success by not exploring alternative skill branches.
Frontier VLMs excel at precise scene estimation but falter in executing coordinated, goal-directed actions, highlighting a critical bottleneck for robot generalism.
Long-horizon embodied agents struggle to translate long-term memory into actionable plans, exposing critical gaps in current benchmarks and methodologies.
