RECALL & ROLL: Benchmarking How Embodied Agents Remember, Act, and Update Memory

Embodied agents must recall past experience and track progress while acting. We introduce Recall & Roll Benchmark (R&R-Bench) to evaluate these requirements across navigation and manipulation. It comprises 300 contexts across 228 simulated houses, totaling 43 hours of experience and 1,772 queries. Four task families, Finding, Restore, Resume, and Routine, cover distinct memory requirements, with variations in request scope, evidence source, and target concealment. Three evaluation conditions examine memory QA, execution given correct goals, and the full pipeline, supporting analysis of retrospective recall and its relationship to task execution. Across the evaluated systems, visual memory better supports spatial recall and passively observed information, while textual memory better supports unfinished-work recovery, storage-rule inference, and progress tracking. Guided by these findings, we introduce R&R Agent as a reference system that combines textual records, tagged visual evidence, and request-conditioned retrieval. It achieves the highest task-average memory-QA accuracy and full-pipeline success across all four families among the evaluated methods that construct memory from experience. Separate placement experiments examine retrieved visual goals for precise manipulation. R&R-Bench provides a common testbed for characterizing embodied memory requirements, identifying limitations of existing approaches, and informing future agent design.