Perception, Memory & Action Modules

Updated 1 December 2025

Perception, Memory, and Action Modules are specialized components that convert raw sensory inputs into coherent, context-sensitive behavior.
They utilize techniques like CNNs, transformers, and memory banks to encode observations, store temporal information, and guide decision-making.
Empirical studies show that modular architectures significantly outperform traditional models in handling partial observability and long-term dependencies.

Perception, Memory, and Action Modules are foundational components in the architecture of intelligent agents, mediating the transformation of raw sensory input into temporally coherent behavior. In modern computational and robotic systems, these modules are realized via distinct but tightly coupled subsystems, each responsible for encoding sensory experience, storing and retrieving relevant information, and driving context-sensitive action selection. Their architecture, functional separation, and mode of integration directly determine agent performance, scalability, and generalization, especially in environments characterized by partial observability, long-term temporal dependencies, and high-dimensional state spaces.

1. Module Definitions and Operational Principles

In contemporary reinforcement learning (RL), robotics, active inference, and neuro-symbolic models, perception, memory, and action modules are specialized computational elements:

Perception modules extract high-level representations from raw sensor data (e.g., images, LiDAR, text). They typically employ convolutional neural networks (CNNs) or transformer-based encoders, sometimes augmented with modality-specific preprocessing pipelines (Bonatti et al., 2022, Shi et al., 26 Aug 2025, Oh et al., 2016).
Memory modules store and organize information over time—either as working buffers, external differentiable memories, transformers’ autoregressive states, or biologically inspired recurrent subunits—enabling the agent to handle temporal credit assignment, partial observability, and long-horizon reasoning (Stooke et al., 2020, Oh et al., 2016, Beker et al., 2022).
Action modules map current perceptions and retrieved memory to concrete action outputs, using policies or planning algorithms ranging from neural heads to symbolic planners or generative models (Stooke et al., 2020, Bonatti et al., 2022, Ali et al., 18 Jul 2024).

A well-calibrated perception–memory–action loop enables context-sensitive behavior, fusing up-to-date observations with internal state to yield robust long-term performance across complex domains (Stooke et al., 2020, Beker et al., 2022, Tresp et al., 2021).

2. Module Architectures and Update Mechanisms

Distinct architectural paradigms realize perception, memory, and action modules:

Neural Hierarchies (PPR Architecture)

The Perception-Prediction-Reaction (PPR) agent (Stooke et al., 2020) comprises four recurrent cores:

Perception core (fast): Processes current observation $x_t$ , producing a short-term summary, reset at slow-tick intervals $K$ .
Prediction core (fast): Integrates the previous action-reward tuple $(a_{t-1}, r_{t-1})$ and long-term memory $h^s_n$ .
Reaction core (fast): Combines $x_t$ and $h^s_n$ to generate the action policy.
Slow core: Aggregates output of perception/prediction every $K$ steps, yielding the long-term memory vector.

Update equations formalize each core's tick schedule and parameter sharing, inducing an information asymmetry: the perception core observes only immediate input, the prediction core only memory and motor context, and the reaction core both (Stooke et al., 2020).

Transformer-Based Memory

The PACT architecture (Bonatti et al., 2022) interleaves perception and action tokens into a causal transformer, where:

Perception tokens arise from LiDAR or RGB processed by PointNet or ResNet-18.
Memory is intrinsic to the transformer's autoregressive hidden state across token sequences.
Action heads decode policy logits from the transformer output at action-token positions.

External and Cognitive Memory

MemoryVLA (Shi et al., 26 Aug 2025) uses a two-level memory structure:

Perceptual tokens ( $P_t$ ) are derived from pretrained visual foundations.
Cognitive tokens ( $C_t$ ) encapsulate high-level instruction/context, extracted via VLMs (e.g. LLaMA-7B).
Memory bank stores separate streams for perceptual and cognitive tokens, supporting retrieval via cross-attention and consolidation via token-merge operations.

Declarative and working memory separation is employed in multitask LLM-robot systems, with logs of all actions and objects enabling persistent task state recall (Ali et al., 18 Jul 2024).

Differentiable and Attentive Memory

In the Minecraft RL setting (Oh et al., 2016), external memories store $M$ most recent perception embeddings, attended by context-dependent (e.g., recurrent LSTM-based) queries. This enables both short-window and long-range retrievals, improving generalization across POMDPs.

3. Learning Objectives, Training, and Information Flow

Training objectives and module interaction paradigms modulate how these systems acquire contextually relevant behavior:

Auxiliary policy-alignment loss (PPR): Enforces consistency among policies derived from observation-only, memory-only, and joint pathways via symmetric KL divergence, biasing behaviors to be reconstructible from either short-term or long-term context alone (Stooke et al., 2020).
Autoregressive behavioral cloning (PACT, MemoryVLA): Minimizes negative log-likelihood of action-state sequences conditioned on past context, directly learning an implicit model of closed-loop behavior from collected trajectories (Bonatti et al., 2022, Shi et al., 26 Aug 2025).
Contrastive representation learning (PALMER): Trains latent perception embeddings such that metric distances correspond to actual traversal times, combined with model-free Q-learning as a reachability proxy (Beker et al., 2022).
End-to-end integration: Perception, memory, and action modules are often trained jointly, establishing information flow through explicit interfaces (token passing, recurrent states) or implicit attention over episodic records or semantic embeddings (Tresp et al., 2021).

Information asymmetry constraints (e.g., disabling slow-memory-to-perception paths in PPR, excluding raw observations from prediction) enforce division of labor, reducing interference and enabling disentangled representations (Stooke et al., 2020).

4. Functional Roles and Module Mapping Across Domains

The roles of perception, memory, and action modules exhibit broad consistency across robotics, deep RL, active inference, and neuro-symbolic systems:

Domain/Architecture	Perception	Memory	Action
PPR Agent (Stooke et al., 2020)	Sensory encoder	Temporal hierarchy (fast/slow)	Policy selection (reaction, prediction)
PACT (Bonatti et al., 2022)	Modal tokenizers	Transformer state (autoregressive)	Linear/MLP heads (action)
MemoryVLA (Shi et al., 26 Aug 2025)	VLM dual streams	Perceptual/cognitive memory bank	Diffusion transformer policy
PALMER (Beker et al., 2022)	CNN encoder	Trajectory replay buffer, latent metric	Sampled planner, segment restitching
Minecraft RL (Oh et al., 2016)	CNN feature extractor	Differentiable external memory w/ attention	Q-value network + ε-greedy
Active Inference (Biehl et al., 2018)	Variational inference	Full trajectory/action history	Free energy minimization, policy eval.

Across these, perception modules convert raw observations into compact, task-relevant features; memory modules buffer informational context across time scales; action modules apply mappings or planning logic to synthesize agent actions, often with explicit reference to retrieved memory.

5. Empirical Findings and Comparative Performance

Empirical studies across benchmark tasks—DMLab-30, Capture the Flag, SimplerEnv, LIBERO, Minecraft, Habitat, and VizDoom—demonstrate that advanced architectures leveraging explicit perception, memory, and action modules substantially outperform vanilla feedforward or LSTM baselines when temporal credit assignment or partial observability is a bottleneck (Stooke et al., 2020, Beker et al., 2022, Shi et al., 26 Aug 2025, Oh et al., 2016).

PPR achieves 72.0% capped human-normalized ELO on DMLab-30, +8% over LSTM; on long-horizon tasks, win rates reach 90% vs. 50–62% (Stooke et al., 2020).
MemoryVLA delivers a 26-point gain for long-horizon tasks in real-world settings over the best prior (Shi et al., 26 Aug 2025).
PALMER is two orders of magnitude more sample-efficient for image-based navigation versus traditional RL, using contrastive reachability and segment restitching (Beker et al., 2022).
Attentive memory and recurrence in Minecraft RL agents yield >98% success in extrapolation scenarios, with LSTM-only baselines collapsing below 60% accuracy (Oh et al., 2016).
LLM-based multitask robots require explicit declarative and working memory for cross-task retention; without memory, performance drops from near-perfect to as low as 14% (Ali et al., 18 Jul 2024).

Ablation studies consistently show that disabling either memory or alignment losses causes catastrophic failure on partially observable or long-horizon tasks (Stooke et al., 2020, Shi et al., 26 Aug 2025).

6. Theoretical Perspectives and Extensions

Perception, memory, and action module partitioning is supported by probabilistic modeling (as in active inference (Biehl et al., 2018)) and by neuro-symbolic integration (Tensor Brain (Tresp et al., 2021)), where symbolic index layers, real-valued representation layers, and oscillatory phase separation provide a unifying substrate for episodic recall, semantic memory, and future-oriented planning.

Active inference formalizes the perception–memory–action loop as alternating inference and control over complete posteriors, with modular swappability of intrinsic motivation functions (e.g., expected free energy, empowerment, knowledge seeking), enabling fine-grained architectural and functional modification within a single framework (Biehl et al., 2018).

Hybrid architectures with memory banks, attention over episodic and semantic records, and planning over sampled future episodes realize biological analogues (e.g., working vs. episodic vs. semantic memory, hippocampal–neocortical interaction) computationally (Shi et al., 26 Aug 2025, Tresp et al., 2021). This supports flexible, scalable deliberation and compositional generalization.

7. Significance, Open Challenges, and Future Directions

The centrality of perception, memory, and action modules in intelligent systems is empirically and theoretically established. These modules enable agents to handle partial observability, perform long-horizon planning, and exhibit adaptive, contextually appropriate behaviors. However, several remaining challenges persist:

Scaling external and transformer-based memory to arbitrarily long horizons without degradation or prohibitive compute.
Efficient cross-modal integration (vision, language, kinesthetics) and consolidation of memory streams (Shi et al., 26 Aug 2025, Bonatti et al., 2022).
Online continual learning in open worlds, resisting catastrophic forgetting while supporting rapid adaptation (Beker et al., 2022).
Biologically plausible architectures that ground symbolic reasoning and episodic recall in shared substrates (Tresp et al., 2021).

A plausible implication is that future agent architectures will converge on hybridized systems, integrating modular perception, compositional memory (including semantic, episodic, and working substrates), and action planners operating at multiple abstraction levels. This suggests that advances in one module (e.g., richer semantic memory, more precise perceptual encoding) will yield compounding benefits across the entire perception–memory–action loop.

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