Lifelong Embodied Memory System

Updated 15 December 2025

Lifelong Embodied Memory System (LEMS) is an architectural framework that enables agents to continuously acquire, store, and retrieve multimodal sensorimotor experiences via hierarchical dual-memory models.
LEMS employs dynamic short-term buffers and stable long-term repositories to support rapid adaptation, robust generalization, and the mitigation of catastrophic forgetting.
LEMS leverages replay, gating, and consolidation techniques inspired by biological memory to achieve real-time performance and scalable autonomous behavior.

A Lifelong Embodied Memory System (LEMS) is an architectural paradigm and technical framework designed to enable embodied agents—including mobile robots, manipulation systems, and interactive agents—to incrementally acquire, store, retrieve, and operationalize knowledge from continuous streams of multimodal sensorimotor experience over long time horizons, with a focus on overcoming catastrophic forgetting, supporting efficient retrieval, and maintaining real-time performance. Modern LEMS draw on inspiration from biological memory systems, hierarchical and distributed memory, and recent advances in neuro-inspired replay, active memory management, and scene abstraction. Such systems are paramount for scalable, robust generalization, and autonomous adaptation in dynamic real-world or simulated environments.

1. Biological and Theoretical Inspirations

The foundational blueprint for LEMS is the hierarchical dual-memory model observed in mammalian brains—principally the fast-encoding hippocampus (episodic, short-term) and the slow-consolidating neocortex (semantic, long-term) (Yin et al., 2022). This principle is operationalized in dual-memory architectures, which maintain both rapidly-updatable buffers for ongoing experience and more stable, capacity-bounded stores with active replay and consolidation. The replay mechanism, critical for countering catastrophic forgetting, is modeled after observed neural “memory replay” during sleep or quiet wakefulness, essential for consolidation of temporally-disjoint experiences.

Memory hierarchies in LEMS frequently integrate:

Short-term buffers (dynamic, limited size, high update rate): Used for immediate working memory, planning, and reward-based gating.
Long-term repositories (static, clustered, or graph-structured, high diversity): Used for cross-domain generalization, redundancy reduction, and lifetime performance (Liu et al., 3 Dec 2025).

2. Architectural Taxonomy and Modular Design

State-of-the-art LEMS feature multi-tiered architectures, typically incorporating:

Static (Long-Term) Memory: Structured as clustered feature banks (Yin et al., 2022), 3D scene graphs (Wang et al., 23 Sep 2024), or hierarchical knowledge graphs (Liu et al., 3 Dec 2025). These store compressed representations of diverse experiences or entities.
Dynamic (Short-Term) Memory: Fast-access circular buffers, FIFO or LFU-managed caches for recent or high-interest experience traces (Wang et al., 23 Sep 2024).
Parametric Memory/Model: Lightweight language or multimodal models continually distilled from retrieved experiences for rapid, differentiable recall (Liu et al., 3 Dec 2025).

In more advanced systems, multimodal integration is standard. For example, MemVerse encodes visual (CNN/VLM), proprioceptive, and linguistic features, transforming them into knowledge graphs with typed, multi-level abstraction from raw events to semantic and core generalizations (Liu et al., 3 Dec 2025).

Architectures such as RoboMemory segregate spatial (knowledge graph), temporal (summaries/FIFO), episodic (RAG-style episode summaries), and semantic (distilled strategies/facts) memory, with parallel updates and retrieval ensuring scalability and parallelism (Lei et al., 2 Aug 2025).

3. Memory Management: Replay, Gating, and Consolidation

A core challenge in lifelong memory is balancing efficient adaptation with knowledge retention. Contemporary LEMS employ the following strategies:

Replay Mechanisms: Generative or experience replay simulates prior context to reinforce feature extractors or planners, using gated sampling metrics that prioritize difficult, high-reward, or robustness-critical samples:

$R_k = R^{\rm ext}_k + R^{\rm int}_k$

where external reward reflects current learning loss and internal reward tests feature robustness to perturbations (Yin et al., 2022).

Gating and Importance Sampling: Living memory traces are scored for inclusion/exclusion into persistent stores using composite reward metrics (recency, frequency, “surprise”):

$I(m)=\alpha \,\mathrm{Freq}(m) + \beta\,e^{-\gamma\,\Delta t} + \delta\,\mathrm{Surp}(m)$

Low-importance or redundant entries are adaptively pruned (Liu et al., 3 Dec 2025).

Consolidation: Periodic distillation merges frequent or core episodes into semantic facts (e.g., “A always precedes B” after many sequential episodes) and transfers distilled summaries into compact parametric models or skill libraries for rapid access (Liu et al., 3 Dec 2025, Tziafas et al., 26 Jun 2024).

4. Representation and Retrieval Schemes

Contemporary LEMS rely on content-addressable retrieval mechanisms:

Embedding-based retrieval: Episodic or description nodes are vector-encoded (e.g., via CLIP for vision, LLM encoders for text), enabling fast similarity search by cosine or Euclidean metric (Pickett et al., 2016, Liu et al., 3 Dec 2025).
Hierarchical and relational storage: Scene and task knowledge is structured as graphs—nodes encode objects/actions, edges encode spatial/temporal/causal relations. Retrieval expands from top-k nearest nodes by embedding similarity, optionally traversing k-hop neighborhoods (Wang et al., 23 Sep 2024, Liu et al., 3 Dec 2025).
Sparse distributed representation: Architectures such as Sparsey utilize near-orthogonal, winner-take-all codes in deep hierarchies for fixed-time addressability, overcoming memory and time complexity bottlenecks of differentiable models (Rinkus, 2018).

Memory fusion or gating is typically achieved with attention mechanisms over multiple memory streams, with gating weights determined by learned or softmaxed linear weights [(Lei et al., 2 Aug 2025), 3D-Mem].

5. Empirical Validation and Evaluation Metrics

Performance is rigorously evaluated along axes of adaptation, retention, compactness, and practical utility:

Success/Recall Metrics: E.g., recall@k, weighted recall, success rate, efficiency gain, SPL (success-weighted path length) (Yin et al., 2022, Wang et al., 23 Sep 2024, Yang et al., 23 Nov 2024, Liu et al., 3 Dec 2025).
Retention and Adaptation: Retention Ability quantifies maintenance of previous knowledge after new training; Adaptation Efficiency measures speed of acquiring new knowledge without loss of old (Yin et al., 2022).
Compactness and Redundancy: Mechanisms such as co-visibility clustering, replay abstraction, and graph consolidation keep storage requirements bounded (e.g., 3D-Mem compresses 39.8 frames to 3.26 relevant snapshots per instruction after filtering) (Yang et al., 23 Nov 2024).

Recent systems demonstrably outperform both classical non-lifelong and contemporary continual baselines in long-horizon and open-world tasks. For example, BioSLAM yields +24% WR over Generative Replay on city-scale place recognition (Yin et al., 2022); RoboMemory surpasses Gemini-1.5-Pro on EmbodiedBench by 3% and baseline frameworks by 25% (Lei et al., 2 Aug 2025).

6. Preventing Catastrophic Forgetting and Ensuring Scalability

Multiple, complementary safeguards are employed:

Replay with high-importance samples only avoids overwhelming new learning with trivial or redundant samples, maintaining domain-specific discriminability (Yin et al., 2022).
Skill abstraction and persistent libraries: In LRLL, once a new skill is abstracted into the library (via cluster-based code refactoring), it is never removed, and historical demos are always replayed--ensuring all capabilities remain accessible indefinitely (Tziafas et al., 26 Jun 2024).
Critical period and metaplastic decay: Sparsey relies on freezing lower-level feature lexicons after saturation and allowing only higher-level representations to remain plastic, circumventing the stability-plasticity dilemma and substantially delaying or eliminating saturation at the highest levels (Rinkus, 2018).
Adaptive consolidation and pruning tailored to usage frequency, recency, and semantic surprise ensures that the most impactful knowledge is retained, with infrequently accessed or low-importance entries removed (Liu et al., 3 Dec 2025).

7. Applications, Extensions, and Future Directions

LEMS are foundational for autonomous mobile robotics, embodied AI agents in domestic or industrial settings, lifelong manipulation, cultural skill transfer, and scene understanding. Extensions under consideration include:

Multimodal knowledge graphs for richer relational memory, supporting vision, language, proprioception, and interaction logs (Liu et al., 3 Dec 2025).
Collaborative and cloud-shared memory, facilitating distributed learning across robot fleets (Yin et al., 2022).
Generalization across tasks: Techniques in Arcadia and LRLL demonstrate how memory supports transfer and rapid adaptation to previously unseen domains and instruction compositions (Gao et al., 25 Nov 2025, Tziafas et al., 26 Jun 2024).
Integration with tool-use agents and planning modules that leverage memory for zero/few-shot task completion and long-horizon planning (Fan et al., 31 Dec 2024, Wang et al., 23 Sep 2024).

Open challenges include learning optimal gating/control strategies (e.g., policy-gradient based gating), real-time consolidation in high-dimensional multimodal spaces, and minimizing drift or semantic confabulation in very long deployments.