RoboMemory: Scalable Memory for Autonomous Robots

• RoboMemory is a modular framework that integrates short-term and long-term memory systems to enhance navigation and task planning in dynamic environments.
• It employs multi-layer databases, cloud integration, and sensor fusion to consolidate sensor data and generate semantic maps for robust robotic behavior.
• Experimental results indicate improved navigation success and reduced planning time, demonstrating practical benefits for autonomous robotic applications.

RoboMemory Framework is a class of architectures and algorithmic patterns designed to augment autonomous robotic agents with structured, scalable, and adaptive memory systems, enabling efficient navigation, task planning, and interaction in dynamic real-world environments. This entry synthesizes multiple instantiations of RoboMemory—from episodic memory-augmented home service robots to multi-layered cloud-backed navigation agents—each characterized by explicit separation of memory functions, query-efficiency, multi-level consolidation, and integration with high-level planning and low-level perception/action modalities (Joo et al., 2019).

1. Systems Architecture and Modular Organization

RoboMemory follows a modular architecture comprising Short-Term Memory (STM) and Long-Term Memory (LTM), interconnected with a set of specialized modules: Autonomous Navigation Module (ANM), Behavior Planner Module (BPM), and Learning Module (LM). STM operates as a cyclic, high-throughput working buffer residing on the robot, storing recent sensor data (e.g., RGB-D point clouds, LIDAR scans, occupancy maps, semantic labels). LTM is deployed as a multi-layer distributed database, including robot-mounted on-demand storage (SQLite, key-value), network-accessible databases (e.g., MongoDB), and a cloud-sharded NoSQL backend. The architectural design enables bidirectional synchronization and geo-distributed partial replication, allowing robots to access collective semantic-episodic maps, action schemas, and ontologies (Joo et al., 2019).

The flow of information proceeds from raw sensor ingestion (STM), periodic consolidation (STM→LTM), high-level querying for navigation and task planning (BPM via LTM), and adaptation cycles (LM governs STM–LTM update). This organization supports cloud-scale operations, operation in unknown environments, and high-level interaction paradigms.

2. Memory Models, Formalism, and Consolidation Algorithms

The operational semantics of RoboMemory revolve around the representation and update of STM and LTM. At each timestep $t$, STM maintains a fixed-length queue $STM_t = \{ e_{t-N+1}, ..., e_t \}$, with each event $e_i = \langle p_i, z_i, \tau_i \rangle$ encoding pose $p_i \in \mathbb{R}^6$, fused sensor measurements $z_i$ (from RGB-D and LIDAR), and short symbolic tags $\tau_i$ (semantic descriptors).

LTM is structured as a collection of memories $LTM = \{ M_j \}_{j=1...K}$, with each $M_j = \langle G_j, \Sigma_j, \rho_j \rangle$, comprising a semantic-topological subgraph $G_j$ (nodes, edges), node annotations $\Sigma_j$, and consolidation timestamps $\rho_j$.

Consolidation occurs periodically (every $T_c$ steps) via selection of STM segments satisfying novelty criteria, assessed by Gromov–Wasserstein graph distances. If a segment $S$ satisfies $novelty(S) > \theta_{novelty}$, it is merged into LTM by forming a new subgraph and semantics:

$C(S) = \langle G_S, \Sigma_S, \tau=t \rangle$

Algorithmically, the procedure is:

for each time step t=1...: e_t ← AcquireObservation() STM.enqueue(e_t) if STM.size > N_STM: STM.dequeue() if t mod T_c == 0: S ← select_candidate_segments(STM) for s in S: if novelty(s) > θ_novelty: M_new ← Consolidate(s) LTM.insert(M_new)

This pipeline is efficient for rapid context switching, supports scalable knowledge integration, and is robust to growing environment complexity (Joo et al., 2019).

3. Integration with SLAM, Sensor Fusion, and Semantic Mapping

RoboMemory incorporates a tightly coupled SLAM and mapping subsystem. 3D visual semantic SLAM embeds coordinate transformations from camera to world frames and structures the mapping with pose graphs (nodes as keyframes, edges as odometry and loop closures). Optimization routines minimize aggregate errors over the pose graph:

$$\min_{T_i} \sum_{ij} \| \log( Z_{ij}^{-1}(T_i^{-1} T_j) ) \|_{\Sigma_{ij}}^2$$

Semantic labeling is performed by pixel-wise deep networks (e.g., MobileNet-DeepLab), with label fusion over surfel maps for robust semantic annotation:

$$class\_prob_{w}(x) \leftarrow \alpha \cdot class\_prob_{c}(u) + (1-\alpha) \cdot prior\_class\_prob_{w}(x)$$

Sensor fusion leverages an Extended Kalman Filter, integrating RGB-D and 2D LIDAR in the state/prediction/update cycles:

$$x_k = f(x_{k-1}, u_k) + w_k,\quad z_k = h(x_k) + v_k$$

RoboMemory thus provides not only spatial and dynamic situational awareness but also semantic context, crucial for high-level planning and interaction.

4. Behavior Planning, Task Execution, and Action Modules

The Behavior Planner Module (BPM) operationalizes task planning as a finite-horizon Markov Decision Process (MDP):

• State space: $$S = \{\text{node in topo-map} \times \text{robot state}\}$$
• Actions: $\mathcal{A} = \{\text{primitive actions}\}$
• Transition: $T(s,a,s')$
• Reward: $R(s,a)$

Value iteration solves for optimal policies:

$$V_{n+1}(s) = \max_{a} [R(s,a) + \gamma \sum_{s'} T(s,a,s') V_n(s')]$$

This formalism enables the synthesis of goal-directed behaviors (e.g., navigation, interaction, delivery), with the planning module querying LTM for relevant topological maps and behavior schemas. Coupling BPM with STM/LTM ensures rapid adaptation to failures, unanticipated observations, and new tasks (Joo et al., 2019).

5. Database, Communication Protocols, and Cloud Integration

LTM is instantiated over multi-layered database architectures (MongoDB for on-premise/network, REST-based sharded NoSQL for cloud), with schema supporting semantic-topological graphs, behaviors, timestamps, and agent metadata. Robots interact with databases through synchronous ROS service calls (low-latency, $30$ ms RTT) and asynchronous cloud REST requests (typical $120$ ms RTT, variable up to $1$ s), employing local least-recently-used (LRU) caching with time-to-live.

Prefetch strategies, triggered by BPM, further reduce latency, allowing batch retrieval of map segments or schemas in advance of mission execution. The persistence layer is optimized for data compression, indexability, and real-time query-response efficiency.

6. Experimental Evaluation and Performance Metrics

Simulation experiments with ROS/Gazebo, differential-drive mobile robots, and varied sensor configurations demonstrate that the addition of RoboMemory yields significant improvements:

Metric	SLAM-only Baseline	RoboMemory Framework
Navigation Success Rate	78%	95%
Planning Time (s)	1.9 ± 0.4	1.2 ± 0.2
STM Usage (MB)	35 ± 5	48 ± 6 (more semantic tags)
LTM Round-trip (ms)	–	30 (network), 120 (cloud)
Map Update Latency (ms)	150	80

The increase in STM footprint is offset by greater semantic richness, while knowledge-driven planning yields a $\sim37\%$ decrease in planning latency. Success rates scale positively with map complexity due to the semantic-episodic memory structure (Joo et al., 2019).

7. Significance, Limitations, and Future Research

RoboMemory delivers a scalable, memory-augmented foundation for autonomous robot operation—enabling robust navigation, adaptive high-level planning, and context-rich human–robot interaction. Its modularity and cloud-integration support geo-distributed deployments and collaborative multi-agent scenarios. The STM/LTM separation, consolidation algorithms, and semantic mapping ensure efficiency in dynamic, unknown environments.

Limitations center on the overhead of STM and database round-trip times under poor connectivity, as well as the granularity of semantic labeling in unstructured scenes. A plausible implication is the need for further hierarchical and incremental memory compression techniques as environments and task complexity grow. Research directions include distributed multi-robot coordination (leveraging shared LTM), online novelty detection, LTM schema evolution for open-world adaptation, and closed-loop coupling with natural-language action modules.

RoboMemory, as introduced in (Joo et al., 2019), provides a principled and extensible blueprint for the design and deployment of embodied intelligence in autonomous robotics, integrating real-time perception, memory consolidation, and knowledge-driven behavioral synthesis.