human-inspired-context-selective-multimodal-memory-social-robots

---
name: human-inspired-context-selective-multimodal-memory-social-robots
description: "Human-inspired context-selective multimodal memory architecture for social robots. Hippocampal-inspired memory consolidation, context-dependent retrieval, and multimodal integration for natural human-robot interaction. Activation: context selective memory, multimodal memory robot, hippocampal memory consolidation, social robot memory, human-inspired memory"
---

# Human-Inspired Context-Selective Multimodal Memory for Social Robots

## Overview

This paper presents a human-inspired context-selective multimodal memory system for social robots. Drawing from neuroscience findings on hippocampal memory consolidation and context-dependent retrieval, the architecture enables robots to store, organize, and recall multimodal experiences (visual, auditory, textual) in a context-aware manner.

## Source Paper

- **Title:** Human-Inspired Context-Selective Multimodal Memory for Social Robots
- **arXiv: 2604.12081v1
- **Date:** 2026-04-13
- **Authors:** Hangyeol Kang, Slava Voloshynovskiy, Nadia Magnenat Thalmann et al.
- **PDF: https://arxiv.org/pdf/2604.12081v1

## Core Concepts

### Context-Selective Encoding
- Memories encoded with rich contextual metadata (who, where, when, emotional valence)
- Attention-gated encoding: only salient information consolidated
- Multimodal fusion: visual, auditory, and textual inputs bound together
- Inspired by hippocampal pattern separation/completion

### Memory Consolidation
- Fast encoding to slow consolidation pipeline
- Sleep-like replay for memory strengthening
- Forgetting mechanism: decay of unused memories
- Priority-based consolidation: emotional/salient memories prioritized

### Context-Dependent Retrieval
- Retrieval triggered by contextual cues
- Pattern completion from partial cues
- Context mismatch detection triggers broader search
- Episodic-to-semantic memory transformation over time

## Architecture

Perception to Context Encoding to Memory Store to Retrieval to Action

## Implementation

### Step 1: Context-Selective Encoder

```python
import torch
from dataclasses import dataclass
from datetime import datetime

@dataclass
class MemoryContext:
    person_id: str
    location: str
    time: datetime
    emotional_valence: float
    modality: list
    salience: float

class ContextEncoder(torch.nn.Module):
    def __init__(self, vision_dim=512, audio_dim=256, text_dim=512, context_dim=128):
        super().__init__()
        self.vision_proj = torch.nn.Linear(vision_dim, 256)
        self.audio_proj = torch.nn.Linear(audio_dim, 128)
        self.text_proj = torch.nn.Linear(text_dim, 256)
        self.context_proj = torch.nn.Linear(context_dim, 128)
        self.fusion = torch.nn.Sequential(
            torch.nn.Linear(256 + 128 + 256 + 128, 512),
            torch.nn.ReLU(),
            torch.nn.Linear(512, 512)
        )

    def forward(self, vision, audio, text, context):
        v = self.vision_proj(vision)
        a = self.audio_proj(audio)
        t = self.text_proj(text)
        c = self.context_proj(context)
        combined = torch.cat([v, a, t, c], dim=-1)
        return self.fusion(combined)
```

### Step 2: Memory Store with Consolidation

```python
import numpy as np

class MemoryStore:
    def __init__(self, max_memories=10000):
        self.memories = []
        self.max_memories = max_memories
        self.decay_rate = 0.01

    def encode(self, embedding, context):
        memory = {
            'embedding': embedding.detach().cpu().numpy(),
            'context': context,
            'strength': context.salience,
            'access_count': 0,
            'created': datetime.now(),
        }
        self.memories.append(memory)
        if len(self.memories) > self.max_memories:
            self.memories.sort(key=lambda m: m['strength'])
            self.memories = self.memories[len(self.memories)//4:]

    def consolidate(self):
        for mem in self.memories:
            days_since = (datetime.now() - mem.get('last_accessed', mem['created'])).days
            mem['strength'] *= (1 - self.decay_rate) ** days_since
            if mem['access_count'] > 3:
                mem['strength'] = min(1.0, mem['strength'] + 0.1)

    def retrieve(self, query_embedding, top_k=5):
        scores = []
        for i, mem in enumerate(self.memories):
            sim = np.dot(query_embedding, mem['embedding'])
            sim /= (np.linalg.norm(query_embedding) * np.linalg.norm(mem['embedding']) + 1e-8)
            scores.append((i, sim * mem['strength']))
        scores.sort(key=lambda x: x[1], reverse=True)
        return [self.memories[idx] for idx, _ in scores[:top_k]]
```

### Step 3: Episodic-to-Semantic Transformation

```python
class SemanticExtractor:
    def __init__(self):
        self.concepts = {}

    def update(self, memories):
        for mem in memories:
            person = mem['context'].person_id
            if person not in self.concepts:
                self.concepts[person] = {'embeddings': [], 'interactions': 0}
            self.concepts[person]['embeddings'].append(mem['embedding'])
            self.concepts[person]['interactions'] += 1

    def get_semantic(self, person_id):
        if person_id not in self.concepts:
            return None
        embeddings = np.array(self.concepts[person_id]['embeddings'])
        return {
            'person_id': person_id,
            'prototype': embeddings.mean(axis=0),
            'interaction_count': self.concepts[person_id]['interactions']
        }
```

## Neuroscience Inspiration

| Robot Component | Brain Analogue | Function |
|----------------|---------------|----------|
| Context Encoder | Hippocampus | Encode experiences with context |
| Memory Store | Hippocampal-CA3 | Store and recall episodic memories |
| Consolidation | Sleep replay | Strengthen important memories |
| Forgetting | Synaptic pruning | Remove unused information |
| Semantic Extractor | Neocortex | Abstract general knowledge |

## Applications

- Social robotics: Personalized human-robot interaction
- Elderly care: Remember patient preferences and routines
- Education: Adaptive tutoring with memory of student progress
- Healthcare: Context-aware patient monitoring
- Companion robots: Build relationships through shared memories

## Related Skills

- brain-inspired-memory-ai-agents
- triple-loop-memory-consolidation
- context-selective-multimodal-memory

## Activation Keywords

- context selective memory, multimodal memory robot, hippocampal memory consolidation, social robot memory, human-inspired memory, episodic memory