robotmem

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

fromrobotmemimportlearn, recall, save_perception, start_session, end_session# Start an episodesession=start_session(context='{"robot_id": "arm-01", "task": "push"}')
# Record experiencelearn(
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 datasave_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 experiencesrecall(query="push to target", context_filter='{"task.success": true}')
# Find spatially nearest scenariosrecall(query="grasp object", spatial_sort='{"field": "spatial.object_position", "target": [1.3, 0.7, 0.42]}')
# Combine: success + distance < 0.05mrecall(
query="push",
context_filter='{"task.success": true, "params.final_distance.value": {"$lt": 0.05}}'
)

Context JSON — 4 Sections

{
"params": {"grip_force": {"value": 12.5, "unit": "N", "type": "scalar"}},
"spatial": {"object_position": [1.3, 0.7, 0.42], "target_position": [1.25, 0.6, 0.42]},
"robot": {"id": "fetch-001", "type": "Fetch", "dof": 7},
"task": {"name": "push_to_target", "success": true, "steps": 38}
}

Each recalled memory automatically extracts params / spatial / robot / task as top-level fields.

Memory Consolidation + Proactive Recall

end_session automatically triggers:

Consolidation: Merges similar memories with Jaccard similarity > 0.50 (protects constraint / postmortem / high-confidence entries)
Proactive Recall: Returns historically relevant memories for the next episode

FetchPush Demo

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

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