Robocasa-Mem: Benchmarking Environment and Process Memory in Long-Horizon Mobile Manipulation
Overview
This paper introduces RoboCasa-Mem, a new benchmark for mobile manipulation that evaluates environment memory (spatial locations, object attributes) and process memory (task progress, scheduling). It features Behavior-Conditioned Evaluation to assess consistency with interaction history rather than fixed targets. The release includes 1,800 successful demonstrations and the HMAP baseline policy, which uses hierarchical memory-augmented planning and VLA controllers incorporating geometry data.
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Reported quantity · successful demonstrations: 1800 other · Basis not reported
“nditioned Evaluation. For tasks with multiple valid execution paths, instead of fixing a single target or trajectory, the evaluator derives later task constraints from the robot's earlier actions. This allows us to evaluate whether later behavior is consistent with the robot's own interaction history while reducing shortcuts based on fixed target assignments. We provide 1,800 successful demonstrations for policy trai”
Exact source · revision 1Source owner not reported
Reported quantity · mobile manipulation tasks: 9 other · Basis not reported
“ark comprising nine mobile manipulation tasks that require agents to use interaction history at key decision points. Specifically, the tasks are grouped into two categories of memory: One is environment memory, which captures spatial locations, object attributes, and object relationships. The other is process memory, which captures single-task progress and multi-task scheduling. Particularly, we introduce Behavior-Co”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRobocasa-Mem: Benchmarking Environment and Process Memory in Long-Horizon Mobile Manipulation
This paper introduces RoboCasa-Mem, a new benchmark for mobile manipulation that evaluates environment memory (spatial locations, object attributes) and process memory (task progress, scheduling). It features Behavior-Conditioned Evaluation to assess consistency with interaction history rather than fixed targets. The release includes 1,800 successful demonstrations and the HMAP baseline policy, which uses hierarchical memory-augmented planning and VLA controllers incorporating geometry data.
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