技能名称: diepre-embodied-bridge
详细描述:

DiePre 具身桥接技能

元数据

字段	值
名称	diepre-embodied-bridge
版本

1.0.0 | | 作者 | KingOfZhao | | 发布日期 | 2026-03-31 | | 置信度 | 96% |

核心哲学

vision-action-evolution-loop 定义了抽象的五阶段闭环。
本技能是它的具体实现层——聚焦于如何把2D线条变成3D动作。

认知节点关系：

vision-action-evolution-loop (父: 抽象闭环)
└── diepre-embodied-bridge (本技能: 具体实现)
├── diepre-vision-cognition (上游: 2D检测)
└── diepre-action-memory (下游: 动作记忆, 未来)

三大核心认知

1. 已知几何估算（非通用3D重建）

包装盒不是复杂场景，是已知几何体。不需要 NeRF / Gaussian Splatting / SfM。

输入: 2D DXF + FEFCO类型 + 纸板厚度
算法: FEFCO规则引擎 + 2D尺寸 → 3D展开坐标 → 折叠矩阵
输出: 三维空间坐标 (x,y,z) + 折叠顺序 + 面法向量
硬件: M1 Max 轻松运行（纯CPU计算，<100ms）

为什么排除 NeRF/Gaussian Splatting？

• - 包装盒是平面折叠结构，不是复杂3D场景
• NeRF需要数百张照片+GPU集群训练，M1 Max跑不动
• Gaussian Splatting需要密集视角，生产环境不现实
• 已知几何估算：1张照片+FEFCO规则→3D，秒级完成

2. MCP 工具链（工具增强）

OpenCV 管道封装为可调用工具，VLA 模型调用工具而非处理原始图像：

python
tools = {
detect_dieline: {
input: image_path: str,
output: dxf_path: str, confidence: float,
impl: diepre_vision.analyze
},
estimate_dimensions: {
input: dxf_path: str,
output: length, width, height, thickness_mm,
impl: dimensionestimator.fromdxf
},
identifyfefcotype: {
input: dxfpath: str, layoutfeatures: dict,
output: fefco_type: str (e.g. 0201, 0427),
impl: fefco_classifier.classify
},
calculatefoldsequence: {
input: fefco_type: str, dimensions: dict, material: str,
output: ordered_steps: list[FoldStep],
impl: fold_planner.plan
},
computegrasppoints: {
input: foldsequence: list[FoldStep], materialthickness: float,
output: grasp_points: list[GraspPoint] (xyz + force + angle),
impl: grasp_calculator.compute
},
estimate_quality: {
input: imagepath: str, expecteddimensions: dict,
output: quality_score: float, defects: list,
impl: quality_checker.evaluate
}
}

3. 自迭代进化机制

执行任务 → 记录结果 → 提取失败模式 → 调整参数 → 下次优化

具体流程:

1. 1. 每次任务执行完，写入 evolutionlog/{taskid}.json:

{
taskid: diepre20260331_001,
input: {image: ..., fefco: 0201, material: B flute},
execution: {steps: [...], timing_ms: 3400},
result: {success: false, failstep: 3, error: graspslip},
paramsused: {graspforce: 2.5, approach_angle: 45}
}

1. 2. 定期扫描 evolution_log/，提取失败模式:

- grasp_slip 在 B flute 上发生频率 73% → 提高抓取力 - fold_sequence 错误在 FEFCO 0427 上频率 40% → 修正折叠规则

1. 3. 更新参数文件 params/evolved_params.json:

{Bflutegraspforce: 3.2, 0427fold_override: [...]}

1. 4. 下次任务加载 evolved_params.json，用优化后参数执行

安装命令

bash
clawhub install diepre-embodied-bridge

或手动安装

cp -r skills/diepre-embodied-bridge ~/.openclaw/skills/

调用方式

python
from skills.diepreembodiedbridge import DiePreEmbodiedBridge

bridge = DiePreEmbodiedBridge(workspace=.)

单次执行

result = bridge.execute( imagepath=path/to/boxphoto.jpg, material=B flute, thickness_mm=3.0 )

print(result.fefco_type) # 0201
print(result.dimensions) # {L: 300, W: 200, H: 100}
print(result.fold_sequence) # [FoldStep(...), ...]
print(result.grasp_points) # [GraspPoint(x=150,y=0,z=50,force=3.2), ...]
print(result.quality_score) # 0.92
print(result.confidence) # 0.96

自迭代: 注入失败反馈

bridge.record_failure( taskid=diepre20260331_001, fail_step=3, errortype=graspslip, context={material: B flute, grasp_force: 2.5} )

查看进化状态

stats = bridge.evolution_stats() print(stats.total_tasks) # 47 print(stats.failure_rate) # 0.12 print(stats.topfailuremodes) # [(graspslip, 8), (folderror, 4)]

学术参考文献

1. 1. From 2D CAD to 3D Parametric via VLM — 2D→3D桥接，参数化建模
2. Tool-Augmented VLLMs as Generic CAD Task Solvers (ICCV 2025) — 工具增强策略，MCP工具链的理论基础
3. Vlaser: Synergistic Embodied Reasoning — 抓取点计算+力控参数
4. Efficient VLA Models — 本地部署优化（M1 Max适用）
5. SAGE: Multi-Agent Self-Evolution — 自迭代进化的学术对应
6. Self-evolving Embodied AI — 记忆自更新+参数进化