2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation
Overview
This research paper introduces 2AM, a framework that assigns task memory to a multimodal agent while using an RGB-based, stateless action model for motion execution. By compiling interaction history into subtask language and hints, the system steers the action model without relying on depth sensors, online geometry, or external planners. Trained with structured hint labels and robustness augmentations like condition dropout and spatial noise, 2AM achieves a 76.3% average completion rate on the LIBERO-Mem benchmark, representing a 61.5-point improvement over the strongest baseline of 14.8%, with 63.0% relaxed and 11.8% strict success rates.
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- arXiv Robotics — research abstractsSignal2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation
This research paper introduces 2AM, a framework that assigns task memory to a multimodal agent while using an RGB-based, stateless action model for motion execution. By compiling interaction history into subtask language and hints, the system steers the action model without relying on depth sensors, online geometry, or external planners. Trained with structured hint labels and robustness augmentations like condition dropout and spatial noise, 2AM achieves a 76.3% average completion rate on the LIBERO-Mem benchmark, representing a 61.5-point improvement over the strongest baseline of 14.8%, with 63.0% relaxed and 11.8% strict success rates.
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