Robotmem

[中文版](README_zh.md)

robotmem — Let Robots Learn from Experience

> Your robot ran 1000 experiments, starting from scratch every time. robotmem stores episode experiences — parameters, trajectories, outcomes — and retrieves the most relevant ones to guide future decisions.

FetchPush experiment: +25% success rate improvement (42% → 67%), CPU-only, reproducible in 5 minutes.

Quick Start

pip install robotmem

from robotmem import learn, recall, save_perception, start_session, end_session

# Start an episode
session = start_session(context='{"robot_id": "arm-01", "task": "push"}')

# Record experience
learn(
    insight="grip_force=12.5N yields highest grasp success rate",
    context='{"params": {"grip_force": {"value": 12.5, "unit": "N"}}, "task": {"success": true}}'
)

# Retrieve experiences (structured filtering + spatial nearest-neighbor)
memories = recall(
    query="grip force parameters",
    context_filter='{"task.success": true}',
    spatial_sort='{"field": "spatial.position", "target": [1.3, 0.7, 0.42]}'
)

# Store perception data
save_perception(
    description="Grasp trajectory: 30 steps, success",
    perception_type="procedural",
    data='{"sampled_actions": [[0.1, -0.3, 0.05, 0.8], ...]}'
)

# End episode (auto-consolidation + proactive recall)
end_session(session_id=session["session_id"])

7 APIs

| API | Purpose | |-----|---------| | learn | Record physical experiences (parameters / strategies / lessons) | | recall | Retrieve experiences — BM25 + vector hybrid search with context_filter and spatial_sort | | save_perception | Store perception / trajectory / force data (visual / tactile / proprioceptive / auditory / procedural) | | forget | Delete incorrect memories | | update | Correct memory content | | start_session | Begin an episode | | end_session | End an episode (auto-consolidation + proactive recall) |

Key Features

Structured Experience Retrieval

Not just vector search — robotmem understands the structure of robot experiences:

# Retrieve only successful experiences
recall(query="push to target", context_filter='{"task.success": true}')

# Find spatially nearest scenarios
recall(query="grasp object", spatial_sort='{"field": "spatial.object_position", "target": [1.3, 0.7, 0.42]}')

# Combine: success + distance  0.50 (protects constraint / postmortem / high-confidence entries)
- **Proactive Recall**: Returns historically relevant memories for the next episode

## FetchPush Demo

```bash
cd examples/fetch_push
pip install gymnasium-robotics
PYTHONPATH=../../src python demo.py  # 90 episodes, ~2 min

Three-phase experiment: baseline → memory writing → memory utilization. Expected Phase C success rate 10-20% higher than Phase A.

Architecture

SQLite + FTS5 + vec0
├── BM25 full-text search (jieba CJK tokenizer)
├── Vector search (FastEmbed ONNX, CPU-only)
├── RRF fusion ranking
├── Structured filtering (context_filter)
└── Spatial nearest-neighbor sorting (spatial_sort)

• CPU-only, no GPU required
• Single-file database ~/.robotmem/memory.db
• MCP Server (7 tools) or direct Python import
• Web management UI: robotmem web

Comparison

| Feature | MemoryVLA (Academic) | Mem0 (Product) | robotmem | |---------|---------------------|----------------|-------------| | Target users | Specific VLA models | Text AI | Robotic AI | | Memory format | Vectors (opaque) | Text | Natural language + perception + parameters | | Structured filtering | No | No | Yes (context_filter) | | Spatial retrieval | No | No | Yes (spatial_sort) | | Physical parameters | No | No | Yes (params section) | | Installation | Compile from paper code | pip install | pip install | | Database | Embedded | Cloud | Local SQLite |

License

Apache-2.0

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: robotmem
• Source: robotmem/robotmem
• License: Apache-2.0
• Homepage: https://robotmem.com

Install and usage instructions live in the source repository linked above.