A cost-aware active testing framework with language-based task embeddings estimates multi-task robot policy performance with fewer manual evaluations than random sampling.
Remembr: Building and reasoning over long-horizon spatio-temporal memory for robot navigation
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Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection
A cost-aware active testing framework with language-based task embeddings estimates multi-task robot policy performance with fewer manual evaluations than random sampling.