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S2 Spatial Element Layer & 4D Semantic Tensor Map
by
MilesXiang
· GitHub ↗
· v1.0.0
· MIT-0
119
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Install in OpenClaw
/install s2-sel-4d-semantic-tensor-map
Description
S2 Spatial Element Layer & 4D Semantic Tensor Map. Integrates L0-L4 layer architecture, 20 material physics tensors, and Chronos backward-persistence time-sl...
Usage Guidance
This package appears coherent and local-only, but review these operational points before installing: 1) Validate in simulation — the code enforces sensor-fusion rules (e.g., disabling visual depth for clear_glass) which can affect robot safety; test thoroughly on hardware. 2) Confirm there are no hidden network calls in your runtime environment (the included files do not call external endpoints). 3) Check the legal/operational claim about '60s backward-persistence' to ensure it aligns with your policy and liability model before you rely on it for safety-critical decisions. 4) Review the S2-CLA license restrictions if you plan to redistribute or integrate the skill into commercial bundles.
Capability Analysis
Type: OpenClaw Skill
Name: s2-sel-4d-semantic-tensor-map
Version: 1.0.0
The skill bundle provides a specialized framework for spatial navigation and material analysis in robotics (S2-SEL). The Python code in core/s2_geojson_parser.py is a standard parser for GeoJSON data, and the instructions in SKILL.md and data/s2_material_tensor_library.json are strictly aligned with the stated purpose of guiding an agent through physical environments based on material properties. No indicators of data exfiltration, malicious execution, or harmful prompt injection were identified.
Capability Tags
Capability Assessment
Purpose & Capability
Name/description match the included artifacts: a parser (core/s2_geojson_parser.py), a local material tensor library, examples, and documentation. All requested resources are local files; nothing unrelated (e.g., cloud credentials or unrelated binaries) is required.
Instruction Scope
SKILL.md prescribes strict runtime behavior (e.g., always retrieve tensors from s2_material_tensor_library and disable visual depth for clear_glass). These directives operate only on the provided local files and S2-GeoJSON inputs, but they are prescriptive about sensor fusion choices (which has operational/safety implications). The skill does not instruct reading unrelated system files, environment variables, or sending data externally.
Install Mechanism
Instruction-only skill with one small Python file and local JSON assets; no install spec, no network downloads, no package manager actions. Low install risk.
Credentials
No environment variables, credentials, or config paths are required. All necessary data is bundled with the skill (material tensor JSON, examples).
Persistence & Privilege
Skill is not marked always:true and does not request persistent system privileges or modify other skills. It can be invoked by the agent normally; autonomous invocation is platform default and not a concern here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install s2-sel-4d-semantic-tensor-map - After installation, invoke the skill by name or use
/s2-sel-4d-semantic-tensor-map - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
## [1.0.0] - 2026-04-07
### 🏗️ 架构升级
- **SSSU 图层化重构**：确立 L0 至 L4 五层空间要素叠合架构，实现建筑设计与机器人导航的语义对齐。
- **4D Chronos 注入**：正式将“时间切片”引入空间要素参数项。支持 `DEFAULT` 泛时态与“精确到秒”的动态切片。
### ✨ 新增功能
- **物理张量库 (L3)**：发布包含玻璃、地毯、大理石等 20 种材质的物理张量代码表，支持视觉/雷达权重动态调整。
- **逆向持存验证**：实装“60秒逆向持存”逻辑，确认 T 时刻的状态代表 T-60s 区间的物理一致性。
- **S2-GeoJSON 标准**：发布基于 GeoJSON 扩展的 `.geojson` 交换格式，支持 `properties.tensors` 嵌套结构。
### 🔧 核心组件
- **解析引擎发布**：提供 Python 版 `s2_geojson_parser.py`，实现从地图要素到机器人行为干预指令的自动化转换。
*桃花源世界模型（S2-SWM）开源社区 持续驱动*
Metadata
Frequently Asked Questions
What is S2 Spatial Element Layer & 4D Semantic Tensor Map?
S2 Spatial Element Layer & 4D Semantic Tensor Map. Integrates L0-L4 layer architecture, 20 material physics tensors, and Chronos backward-persistence time-sl... It is an AI Agent Skill for Claude Code / OpenClaw, with 119 downloads so far.
How do I install S2 Spatial Element Layer & 4D Semantic Tensor Map?
Run "/install s2-sel-4d-semantic-tensor-map" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is S2 Spatial Element Layer & 4D Semantic Tensor Map free?
Yes, S2 Spatial Element Layer & 4D Semantic Tensor Map is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does S2 Spatial Element Layer & 4D Semantic Tensor Map support?
S2 Spatial Element Layer & 4D Semantic Tensor Map is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created S2 Spatial Element Layer & 4D Semantic Tensor Map?
It is built and maintained by MilesXiang (@spacesq); the current version is v1.0.0.
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