Human-Centered Robot Learning and Evaluation
We study how to evaluate robot manipulation policies beyond a single success rate, and how people can shape what gets measured. We treat evaluation as a structured measurement problem: behavioral metrics for efficiency, coordination, and safety, together with outcome metrics that trace stagewise progress and localize failures, separate policies with similar success rates. We treat the task set itself as something people author: natural-language instructions are compiled into reproducible, executable task specifications, each defining a family of related variations, so that an evaluation space grows with its contributors instead of being fixed by a few experts. The same perspective extends to the language models that policies build on, where we benchmark physical reasoning about everyday objects. Our broader goal is evaluation that is diagnostic, reproducible, and extensible by its community.
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