PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft
For embodied AI agents, PEAM provides a self-evolving memory mechanism that eliminates hand-tuned thresholds and enables continual learning without forgetting, addressing a key bottleneck in deploying agents in open-ended environments.
PEAM introduces a parametric memory framework for embodied agents in Minecraft that internalizes experience into model parameters, improving long-horizon task performance and mitigating catastrophic forgetting while achieving better efficiency than retrieval-based methods.
We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience. PEAM pairs a slow deliberative LLM for open-ended reasoning with a fast parametric module for reflexive execution of consolidated skills. The fast module is a multimodal Mixture-of-Experts LoRA architecture with per-category physically isolated adapters, enabling parameter-level continual learning without catastrophic forgetting. We treat failure as a first-class training signal: failure--correction trajectory pairs are internalized through a joint behavioral-cloning and contrastive objective, so the agent learns not only what succeeds but also how corrected actions differ from failed ones. To govern consolidation, PEAM introduces a parameterization-worthiness score for deciding which experience should be internalized, and a scale-free self-triggered consolidation mechanism for deciding when to internalize without task-specific hand-tuned thresholds, making the agent self-evolving as the trigger transfers across task distributions without re-tuning. Experiments in Minecraft show that PEAM improves long-horizon task performance, mitigates forgetting on previously consolidated skills, and improves parametric-versus-retrieval efficiency over retrieval-based embodied agents and parametric memory variants.