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Adapting Robot Foundation Models for Clinical Environments

The challenge lies in adapting general-purpose robot foundation models to dynamic clinical environments, where tasks and constraints vary significantly from typical benchmarks. This adaptation is crucial for effectively utilizing robots in healthcare settings, such as hospitals and pharmacies. The primary issue is ensuring these models can transfer well to real-world healthcare scenarios.

Computing & Technology, Computer Science, Machine Learning