Add bedroom scene, demos, Chinese documentation, and scene type guide
#1
by yuq-nv - opened
- .gitattributes +12 -0
- README.md +662 -0
- demos/README.md +81 -0
- demos/demo_bedroom_nightstand_to_desk.json +3473 -0
- demos/demo_bedroom_nightstand_to_desk_trajectory.png +3 -0
- demos/demo_bedroom_wardrobe_to_desk.json +3473 -0
- demos/demo_bedroom_wardrobe_to_desk_trajectory.png +3 -0
- docs/scene_types_zh.md +186 -0
- scenes/scene_01_bedroom/README.md +54 -0
- scenes/scene_01_bedroom/flythrough.mp4 +3 -0
- scenes/scene_01_bedroom/renders/ceiling_top.png +3 -0
- scenes/scene_01_bedroom/renders/floor_plan.png +3 -0
- scenes/scene_01_bedroom/renders/furniture_side0.png +3 -0
- scenes/scene_01_bedroom/renders/furniture_side1.png +3 -0
- scenes/scene_01_bedroom/renders/furniture_side2.png +3 -0
- scenes/scene_01_bedroom/renders/furniture_side3.png +3 -0
- scenes/scene_01_bedroom/renders/furniture_top.png +3 -0
- scenes/scene_01_bedroom/renders/wall_north.png +3 -0
- scenes/scene_01_bedroom/renders/wall_top.png +3 -0
- scenes/scene_01_bedroom/sim_files/house.dmd.yaml +68 -0
- scenes/scene_01_bedroom/sim_files/house_state.json +0 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
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| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
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| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
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| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
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| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
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| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
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| 36 |
+
demos/demo_bedroom_nightstand_to_desk_trajectory.png filter=lfs diff=lfs merge=lfs -text
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| 37 |
+
demos/demo_bedroom_wardrobe_to_desk_trajectory.png filter=lfs diff=lfs merge=lfs -text
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| 38 |
+
scenes/scene_01_bedroom/flythrough.mp4 filter=lfs diff=lfs merge=lfs -text
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| 39 |
+
scenes/scene_01_bedroom/renders/ceiling_top.png filter=lfs diff=lfs merge=lfs -text
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| 40 |
+
scenes/scene_01_bedroom/renders/floor_plan.png filter=lfs diff=lfs merge=lfs -text
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| 41 |
+
scenes/scene_01_bedroom/renders/furniture_side0.png filter=lfs diff=lfs merge=lfs -text
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| 42 |
+
scenes/scene_01_bedroom/renders/furniture_side1.png filter=lfs diff=lfs merge=lfs -text
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| 43 |
+
scenes/scene_01_bedroom/renders/furniture_side2.png filter=lfs diff=lfs merge=lfs -text
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| 44 |
+
scenes/scene_01_bedroom/renders/furniture_side3.png filter=lfs diff=lfs merge=lfs -text
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| 45 |
+
scenes/scene_01_bedroom/renders/furniture_top.png filter=lfs diff=lfs merge=lfs -text
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| 46 |
+
scenes/scene_01_bedroom/renders/wall_north.png filter=lfs diff=lfs merge=lfs -text
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| 47 |
+
scenes/scene_01_bedroom/renders/wall_top.png filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
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@@ -0,0 +1,662 @@
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| 1 |
+
# SceneSmith 完整技术文档
|
| 2 |
+
|
| 3 |
+
> 语言：中文
|
| 4 |
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> 对应代码：[nepfaff/scenesmith](https://github.com/nepfaff/scenesmith)
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| 5 |
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> 论文：[arXiv 2602.09153](https://arxiv.org/abs/2602.09153)
|
| 6 |
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> 本次运行环境：NVIDIA L40S GPU，SLURM 集群
|
| 7 |
+
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| 8 |
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---
|
| 9 |
+
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| 10 |
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## 目录
|
| 11 |
+
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| 12 |
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1. [系统总览](#1-系统总览)
|
| 13 |
+
2. [输入输出格式](#2-输入输出格式)
|
| 14 |
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3. [核心流程详解](#3-核心流程详解)
|
| 15 |
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4. [代码结构解析](#4-代码结构解析)
|
| 16 |
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5. [Robot Eval 模块](#5-robot-eval-模块)
|
| 17 |
+
6. [SceneSmith 能做什么 / 不能做什么](#6-scenesmith-能做什么--不能做什么)
|
| 18 |
+
7. [本次运行结果](#7-本次运行结果)
|
| 19 |
+
8. [Demo采集说明](#8-demo采集说明)
|
| 20 |
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9. [常见问题与局限性](#9-常见问题与局限性)
|
| 21 |
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10. [复现指南](#10-复现指南)
|
| 22 |
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| 23 |
+
---
|
| 24 |
+
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| 25 |
+
## 1. 系统总览
|
| 26 |
+
|
| 27 |
+
SceneSmith 是一个**全自动、文本驱动的室内场景生成系统**，专为机器人仿真设计。
|
| 28 |
+
|
| 29 |
+
给一段自然语言描述（如"一个有书桌、床和衣柜的卧室"），SceneSmith 会输出一个**物理上可用的完整室内场景**，包含：
|
| 30 |
+
- 精确的 6D 物体位姿
|
| 31 |
+
- 每个物体的碰撞几何体（SDF 格式）
|
| 32 |
+
- 物理属性（质量、摩擦系数、惯性矩阵）
|
| 33 |
+
- 可以直接导入 Drake / MuJoCo / Isaac Sim 的格式
|
| 34 |
+
|
| 35 |
+
系统的核心是用**多个 LLM Agent 协作**代替人工设计：每个 Agent 负责场景生成的一个阶段，用视觉反馈迭代优化，直到满足质量标准。
|
| 36 |
+
|
| 37 |
+
```
|
| 38 |
+
文字描述
|
| 39 |
+
│
|
| 40 |
+
▼
|
| 41 |
+
┌─────────────────────────────────────────────────────────┐
|
| 42 |
+
│ SceneSmith Pipeline │
|
| 43 |
+
│ │
|
| 44 |
+
│ Floor Plan Agent → Furniture Agent → Wall Agent │
|
| 45 |
+
│ ↓ ↓ ↓ │
|
| 46 |
+
│ 生成平面图 摆放大件家具 墙面装饰 │
|
| 47 |
+
│ ↓ ↓ │
|
| 48 |
+
│ Ceiling Agent → Manipuland Agent │
|
| 49 |
+
│ ↓ ↓ │
|
| 50 |
+
│ 天花板灯具 小件可操作物体 │
|
| 51 |
+
│ │
|
| 52 |
+
│ 每个阶段：LLM 规划 → 3D资产生成 → 物理验证 → 视觉评分 │
|
| 53 |
+
└─────────────────────────────────────────────────────────┘
|
| 54 |
+
│
|
| 55 |
+
▼
|
| 56 |
+
house.blend (Blender 渲染) + house.dmd.yaml (Drake 仿真) + house_state.json
|
| 57 |
+
```
|
| 58 |
+
|
| 59 |
+
---
|
| 60 |
+
|
| 61 |
+
## 2. 输入输出格式
|
| 62 |
+
|
| 63 |
+
### 输入
|
| 64 |
+
|
| 65 |
+
| 输入项 | 格式 | 说明 |
|
| 66 |
+
|--------|------|------|
|
| 67 |
+
| 场景描述 | 自然语言字符串 | 例：`"A cozy bedroom with a queen bed..."` |
|
| 68 |
+
| CSV 提示文件（可选） | `prompts.csv` | 批量生成多个场景时使用 |
|
| 69 |
+
| 配置文件 | Hydra YAML | 控制使用的模型、后端、参数 |
|
| 70 |
+
|
| 71 |
+
**`prompts.csv` 格式**：
|
| 72 |
+
```csv
|
| 73 |
+
scene_index,prompt
|
| 74 |
+
0,"A modern kitchen with..."
|
| 75 |
+
1,"A cozy living room with..."
|
| 76 |
+
```
|
| 77 |
+
|
| 78 |
+
**关键配置参数**：
|
| 79 |
+
```yaml
|
| 80 |
+
openai:
|
| 81 |
+
model: "gpt-5.2" # LLM 模型名
|
| 82 |
+
experiment:
|
| 83 |
+
num_scenes: 5 # 生成场景数量
|
| 84 |
+
scene_prompt: "..." # 单场景描述
|
| 85 |
+
furniture_agent:
|
| 86 |
+
asset_manager:
|
| 87 |
+
backend: "hunyuan3d" # 3D资产生成后端: sam3d | hunyuan3d | hssd
|
| 88 |
+
```
|
| 89 |
+
|
| 90 |
+
### 输出
|
| 91 |
+
|
| 92 |
+
每次运行生成如下目录结构：
|
| 93 |
+
|
| 94 |
+
```
|
| 95 |
+
outputs/YYYY-MM-DD/HH-MM-SS/
|
| 96 |
+
└── scene_000/
|
| 97 |
+
├── combined_house/
|
| 98 |
+
│ ├── house.blend ← Blender 完整场景 (~200MB)
|
| 99 |
+
│ ├── house.dmd.yaml ← Drake Directives（仿真可用）
|
| 100 |
+
│ ├── house_furniture_welded.dmd.yaml ← 家具焊接版（稳定性更好）
|
| 101 |
+
│ ├── house_state.json ← 所有物体的位姿+元数据
|
| 102 |
+
│ └── sceneeval_state.json ← 场景评分数据
|
| 103 |
+
├── room_bedroom/
|
| 104 |
+
│ ├── generated_assets/ ← 每个物体的 SDF + 纹理
|
| 105 |
+
│ │ ├── desk_0/
|
| 106 |
+
│ │ │ ├── model.sdf
|
| 107 |
+
│ │ │ ├── model.obj
|
| 108 |
+
│ │ │ └── texture.png
|
| 109 |
+
│ │ └── ...
|
| 110 |
+
│ ├── scene_states/ ← 各阶段保存的中间状态
|
| 111 |
+
│ │ ├── scene_after_furniture/scene.blend
|
| 112 |
+
│ │ ├── scene_after_wall_objects/scene.blend
|
| 113 |
+
│ │ ├── scene_after_ceiling_objects/scene.blend
|
| 114 |
+
│ │ └── final_scene/
|
| 115 |
+
│ │ ├── scene.blend
|
| 116 |
+
│ │ └── scene_state.json
|
| 117 |
+
│ └── scene_renders/ ← 各阶段的渲染图像 (PNG)
|
| 118 |
+
└── floor_plans/
|
| 119 |
+
└── final_floor_plan/
|
| 120 |
+
├── floor_plan.blend
|
| 121 |
+
└── floor_plan.dmd.yaml
|
| 122 |
+
```
|
| 123 |
+
|
| 124 |
+
**`house_state.json` 结构**（关键字段）：
|
| 125 |
+
```json
|
| 126 |
+
{
|
| 127 |
+
"rooms": {
|
| 128 |
+
"bedroom": {
|
| 129 |
+
"objects": {
|
| 130 |
+
"desk_0": {
|
| 131 |
+
"object_type": "furniture",
|
| 132 |
+
"name": "desk",
|
| 133 |
+
"description": "A modern wooden desk",
|
| 134 |
+
"sdf_path": "generated_assets/desk_0/model.sdf",
|
| 135 |
+
"transform": {
|
| 136 |
+
"translation": [0.79, 1.58, 0.0],
|
| 137 |
+
"rotation_wxyz": [1.0, 0.0, 0.0, 0.0]
|
| 138 |
+
},
|
| 139 |
+
"bbox_min": [-0.6, -0.3, 0.0],
|
| 140 |
+
"bbox_max": [0.6, 0.3, 0.75],
|
| 141 |
+
"mass": 15.0,
|
| 142 |
+
"mu_static": 0.6
|
| 143 |
+
}
|
| 144 |
+
}
|
| 145 |
+
}
|
| 146 |
+
}
|
| 147 |
+
}
|
| 148 |
+
```
|
| 149 |
+
|
| 150 |
+
**`house.dmd.yaml` 格式**（Drake Directives）：
|
| 151 |
+
```yaml
|
| 152 |
+
directives:
|
| 153 |
+
- add_model:
|
| 154 |
+
name: bedroom_desk_0
|
| 155 |
+
file: package://scene/room_bedroom/generated_assets/desk_0/model.sdf
|
| 156 |
+
- add_weld:
|
| 157 |
+
parent: world
|
| 158 |
+
child: bedroom_desk_0::base_link
|
| 159 |
+
X_PC:
|
| 160 |
+
translation: [0.79, 1.58, 0.0]
|
| 161 |
+
rotation: !Rpy { deg: [0, 0, 0] }
|
| 162 |
+
```
|
| 163 |
+
|
| 164 |
+
---
|
| 165 |
+
|
| 166 |
+
## 3. 核心流程详解
|
| 167 |
+
|
| 168 |
+
### Stage 1：Floor Plan Agent（平面图规划）
|
| 169 |
+
|
| 170 |
+
**输入**：场景描述文字
|
| 171 |
+
**输出**：房间尺寸、门窗位置、墙体布局（`floor_plan.dmd.yaml`）
|
| 172 |
+
|
| 173 |
+
工作方式：
|
| 174 |
+
1. LLM 解析场景描述，确定所需房间类型（卧室、厨房等）
|
| 175 |
+
2. 生成候选平面图（房间长宽、门的位置）
|
| 176 |
+
3. 用 Blender 渲染俯视图，LLM 评分（0-1分）
|
| 177 |
+
4. 迭代优化，选择得分最高的方案
|
| 178 |
+
|
| 179 |
+
评分标准：空间合理性、比例、门窗位置是否符合常识。
|
| 180 |
+
|
| 181 |
+
---
|
| 182 |
+
|
| 183 |
+
### Stage 2：Furniture Agent（家具摆放）
|
| 184 |
+
|
| 185 |
+
**输入**：平面图、场景描述
|
| 186 |
+
**输出**：家具的精确 6D 位姿 + 每件家具的 SDF 模型
|
| 187 |
+
|
| 188 |
+
这是最复杂的阶段，分三个子步骤：
|
| 189 |
+
|
| 190 |
+
#### 2a. 家具规划
|
| 191 |
+
LLM 决定放什么家具、大概的位置，生成一个结构化的家具列表。
|
| 192 |
+
|
| 193 |
+
#### 2b. 3D 资产生成
|
| 194 |
+
对每件家具，走以下路由（Asset Router）：
|
| 195 |
+
|
| 196 |
+
```
|
| 197 |
+
家具描述文字
|
| 198 |
+
│
|
| 199 |
+
▼
|
| 200 |
+
Asset Router (LLM决策)
|
| 201 |
+
├── "generated" → Hunyuan3D-2 / SAM3D
|
| 202 |
+
│ ├── 生成参考图 (GPT-Image-2)
|
| 203 |
+
│ └── 图片 → 3D mesh (扩散模型)
|
| 204 |
+
│ ↓
|
| 205 |
+
│ 纹理烘焙 + SDF生成
|
| 206 |
+
│
|
| 207 |
+
├── "artvip" → 从ArtVIP库检索关节物体（开门的衣柜等）
|
| 208 |
+
│
|
| 209 |
+
└── "hssd" → 从HSSD数据集检索（更快，质量稳定）
|
| 210 |
+
```
|
| 211 |
+
|
| 212 |
+
#### 2c. 迭代摆放
|
| 213 |
+
1. 放置家具到初始位置
|
| 214 |
+
2. Drake 物理仿真：检测碰撞、检查是否可站立
|
| 215 |
+
3. Blender 渲染多视角图
|
| 216 |
+
4. LLM 用视觉反馈打分（美观度、布局合理性、可达性）
|
| 217 |
+
5. 如果分数不满足阈值，调整位置重试（最多 N 轮）
|
| 218 |
+
|
| 219 |
+
---
|
| 220 |
+
|
| 221 |
+
### Stage 3：Wall Agent（墙面装饰）
|
| 222 |
+
|
| 223 |
+
**输入**：已摆好家具的场景
|
| 224 |
+
**输出**：墙面装饰物（画、镜子、时钟、架子等）的位姿
|
| 225 |
+
|
| 226 |
+
同样走 asset router → 3D 生成 → Drake 验证 → 视觉评分的流程，但专注于墙面的高度、朝向、装饰品间距。
|
| 227 |
+
|
| 228 |
+
---
|
| 229 |
+
|
| 230 |
+
### Stage 4：Ceiling Agent（天花板）
|
| 231 |
+
|
| 232 |
+
**输入**：已完成墙面的场景
|
| 233 |
+
**输出**：天花板灯具、风扇等物体
|
| 234 |
+
|
| 235 |
+
主要验证灯具位置是否在房间中央、是否与墙面冲突。
|
| 236 |
+
|
| 237 |
+
---
|
| 238 |
+
|
| 239 |
+
### Stage 5：Manipuland Agent（可操作小物件）
|
| 240 |
+
|
| 241 |
+
**输入**：已完成天花板的场景、每件家具的支撑面信息
|
| 242 |
+
**输出**：桌面上的书、杯子、水果等小物件
|
| 243 |
+
|
| 244 |
+
特点：
|
| 245 |
+
- 自动分析每件家具的"支撑面"（桌面、架子面）
|
| 246 |
+
- 在支撑面上随机采样放置位置，Drake 验证不碰撞
|
| 247 |
+
- 可生成组合（stack/pile/filled_container）：叠起来的书、装苹果的碗等
|
| 248 |
+
|
| 249 |
+
---
|
| 250 |
+
|
| 251 |
+
### Stage 6：物理投影 + 最终导出
|
| 252 |
+
|
| 253 |
+
所有物体放好后：
|
| 254 |
+
1. Drake 运行物理仿真，让物体自然落下（消除浮空、轻微穿插）
|
| 255 |
+
2. 输出最终 `house.dmd.yaml`（固定位姿，weld 到 world）
|
| 256 |
+
3. Blender 渲染最终场景，保存 `house.blend`
|
| 257 |
+
|
| 258 |
+
---
|
| 259 |
+
|
| 260 |
+
## 4. 代码结构解析
|
| 261 |
+
|
| 262 |
+
```
|
| 263 |
+
scenesmith/
|
| 264 |
+
├── main.py ← 入口，Hydra 配置，调度各 Agent
|
| 265 |
+
│
|
| 266 |
+
├── scenesmith/
|
| 267 |
+
│ ├── experiments/
|
| 268 |
+
│ │ └── indoor_scene_generation.py ← 主实验类，串联所有 Agent
|
| 269 |
+
│ │
|
| 270 |
+
│ ├── floor_plan_agents/
|
| 271 |
+
│ │ ├── stateful_floor_plan_agent.py ← Floor Plan Agent 实现
|
| 272 |
+
│ │ └── tools/ ← 平面图相关工具
|
| 273 |
+
│ │
|
| 274 |
+
│ ├── furniture_agents/
|
| 275 |
+
│ │ ├── stateful_furniture_agent.py ← Furniture Agent 主类
|
| 276 |
+
│ │ └── tools/
|
| 277 |
+
│ │ ├── furniture_tools.py ← 家具放置、物理检查工具
|
| 278 |
+
│ │ ├── vision_tools.py ← Blender 渲染观察工具
|
| 279 |
+
│ │ └── scene_tools.py ← 场景状态查询
|
| 280 |
+
│ │
|
| 281 |
+
│ ├── wall_agents/ ← Wall Agent（同结构）
|
| 282 |
+
│ ├── ceiling_agents/ ← Ceiling Agent（同结构）
|
| 283 |
+
│ ├── manipuland_agents/ ← Manipuland Agent���同结构）
|
| 284 |
+
│ │
|
| 285 |
+
│ ├── agent_utils/
|
| 286 |
+
│ │ ├── asset_manager.py ← 资产获取总入口
|
| 287 |
+
│ │ ├── asset_router/ ← LLM决策走哪个资产后端
|
| 288 |
+
│ │ ├── geometry_generation_server/ ← 3D生成服务（Hunyuan3D工作进程）
|
| 289 |
+
│ │ │ ├── server_manager.py
|
| 290 |
+
│ │ │ ├── worker_pool.py ← GPU工作进程池
|
| 291 |
+
│ │ │ └── gpu_worker.py ← 单个GPU的3D生成进程
|
| 292 |
+
│ │ ├── drake_utils.py ← Drake 仿真工具
|
| 293 |
+
│ │ ├── rendering.py ← 场景渲染工具
|
| 294 |
+
│ │ ├── physics_validation.py ← 物理碰撞检测
|
| 295 |
+
│ │ ├── reachability.py ← 机器人可达性分析
|
| 296 |
+
│ │ ├── image_generation.py ← 参考图生成（GPT-Image-2）
|
| 297 |
+
│ │ ├── vlm_service.py ← VLM评分服务
|
| 298 |
+
│ │ └── blender/ ← Blender 进程通信
|
| 299 |
+
│ │ ├── server_manager.py ← Blender 服务器管理
|
| 300 |
+
│ │ └── renderer.py ← 渲染接口
|
| 301 |
+
│ │
|
| 302 |
+
│ └── robot_eval/
|
| 303 |
+
│ ├── dmd_scene.py ← Drake 场景加载
|
| 304 |
+
│ ├── policy_interface/
|
| 305 |
+
│ │ ├── policy_agent.py ← 任务 → 物体绑定 Agent
|
| 306 |
+
│ │ └── predicate_resolver.py ← 绑定 → 精确位姿
|
| 307 |
+
│ ├── success_validation/
|
| 308 |
+
│ │ └── validator_agent.py ← 任务成功验证 Agent
|
| 309 |
+
│ └── task_generation/
|
| 310 |
+
│ └── scene_prompt_generator.py ← 任务 → 场景描述
|
| 311 |
+
│
|
| 312 |
+
└── scripts/
|
| 313 |
+
├── robot_eval/
|
| 314 |
+
│ ├── generate_prompts.py ← Stage 1: task → prompts
|
| 315 |
+
│ ├── policy_interface.py ← Stage 3: scene + task → robot poses
|
| 316 |
+
│ └── validate.py ← Stage 4: 验证任务完成
|
| 317 |
+
├── collect_demo.py ← Demo采集与可视化（本次添加）
|
| 318 |
+
├── render_flythrough.py ← 生成场景飞行视频
|
| 319 |
+
└── export_scene_to_mujoco.py ← 导出到 MuJoCo
|
| 320 |
+
```
|
| 321 |
+
|
| 322 |
+
---
|
| 323 |
+
|
| 324 |
+
### 关键类说明
|
| 325 |
+
|
| 326 |
+
#### `StatefulFurnitureAgent`
|
| 327 |
+
继承自 `BaseStatefulAgent`，用 `openai-agents` SDK 运行 LLM agent loop。
|
| 328 |
+
|
| 329 |
+
Agent 的 tools 包括：
|
| 330 |
+
- `place_furniture(obj_id, x, y, yaw)` — 放置家具
|
| 331 |
+
- `observe_scene()` — 渲染当前场景，返回多视角图给 LLM 看
|
| 332 |
+
- `get_physics_check()` — 运行 Drake 碰撞检测
|
| 333 |
+
- `generate_furniture_assets(descriptions)` — 批量生成 3D 资产
|
| 334 |
+
- `score_scene()` — VLM 打分
|
| 335 |
+
|
| 336 |
+
每轮 LLM 调用 tools → 观察反馈 → 调整 → 直到评分达标或超过最大轮数。
|
| 337 |
+
|
| 338 |
+
#### `AssetManager`
|
| 339 |
+
统一的资产获取接口，内部调用 `AssetRouter` 决策：
|
| 340 |
+
```python
|
| 341 |
+
asset = await asset_manager.get_asset(
|
| 342 |
+
description="A wooden desk with drawers",
|
| 343 |
+
object_type="furniture",
|
| 344 |
+
strategy="generated" # or "hssd", "artvip"
|
| 345 |
+
)
|
| 346 |
+
# 返回 Asset(sdf_path, obj_path, texture_path, dimensions, ...)
|
| 347 |
+
```
|
| 348 |
+
|
| 349 |
+
#### `DMDScene` + `PredicateResolver`
|
| 350 |
+
用于 robot eval：
|
| 351 |
+
```python
|
| 352 |
+
scene = load_scene_for_validation(
|
| 353 |
+
scene_state_path=Path("house_state.json"),
|
| 354 |
+
dmd_path=Path("house.dmd.yaml"),
|
| 355 |
+
)
|
| 356 |
+
scene.finalize() # 建立 Drake plant，同步位姿
|
| 357 |
+
|
| 358 |
+
resolver = PredicateResolver(scene=scene, cfg=cfg)
|
| 359 |
+
result = resolver.resolve("Pick the apple and place it on the plate")
|
| 360 |
+
# result.poses[0].target_position → [x, y, z] 目标位姿
|
| 361 |
+
# result.poses[0].placement_bounds_min/max → 有效放置区域 AABB
|
| 362 |
+
```
|
| 363 |
+
|
| 364 |
+
---
|
| 365 |
+
|
| 366 |
+
## 5. Robot Eval 模块
|
| 367 |
+
|
| 368 |
+
这是 SceneSmith 的机器人评测框架，共 4 个阶段：
|
| 369 |
+
|
| 370 |
+
### 阶段 1：生成场景提示词
|
| 371 |
+
```bash
|
| 372 |
+
python scripts/robot_eval/generate_prompts.py \
|
| 373 |
+
--task "Pick a fruit from the bowl and place it on the plate" \
|
| 374 |
+
--output-dir outputs/eval_run \
|
| 375 |
+
--num-prompts 5
|
| 376 |
+
```
|
| 377 |
+
LLM 分析 task，提取：
|
| 378 |
+
- 必须存在的物体（水果、碗、盘子）
|
| 379 |
+
- 初始状态约束（水果不能已经在盘子上）
|
| 380 |
+
- 可变的风格维度（厨房风格、其他装饰物）
|
| 381 |
+
|
| 382 |
+
输出 `prompts.csv` 供 `main.py` 生成多个不同风格的场景。
|
| 383 |
+
|
| 384 |
+
### 阶段 2：生成场景
|
| 385 |
+
```bash
|
| 386 |
+
python main.py +name=eval experiment.csv_path=outputs/eval_run/prompts.csv
|
| 387 |
+
```
|
| 388 |
+
|
| 389 |
+
### 阶段 3：Policy Interface（任务 → 机器人位姿）
|
| 390 |
+
```bash
|
| 391 |
+
python scripts/robot_eval/policy_interface.py \
|
| 392 |
+
--scene-state outputs/.../house_state.json \
|
| 393 |
+
--dmd outputs/.../house.dmd.yaml \
|
| 394 |
+
--task "Pick a fruit from the bowl and place it on the plate" \
|
| 395 |
+
--output-json robot_commands.json
|
| 396 |
+
```
|
| 397 |
+
|
| 398 |
+
输出格式：
|
| 399 |
+
```json
|
| 400 |
+
{
|
| 401 |
+
"task": "Pick a fruit...",
|
| 402 |
+
"robot_start_xy": [1.2, -0.5],
|
| 403 |
+
"world_bounds": {"min": [-3, -3, 0], "max": [3, 3, 2.7]},
|
| 404 |
+
"commands": [{
|
| 405 |
+
"action": "pick_and_place",
|
| 406 |
+
"rank": 1,
|
| 407 |
+
"confidence": 0.92,
|
| 408 |
+
"drake_model_name": "bedroom_apple_0",
|
| 409 |
+
"target_position": [0.5, 0.3, 0.85],
|
| 410 |
+
"placement_bounds_min": [0.2, 0.1, 0.85],
|
| 411 |
+
"placement_bounds_max": [0.8, 0.5, 1.0],
|
| 412 |
+
"reasoning": "Apple found on nightstand (precondition met), plate on desk is valid goal"
|
| 413 |
+
}]
|
| 414 |
+
}
|
| 415 |
+
```
|
| 416 |
+
|
| 417 |
+
**注意**：SceneSmith 不包含 robot URDF 和 policy。`placement_bounds_min/max` 是给你的 policy 用的采样区域。
|
| 418 |
+
|
| 419 |
+
### 阶段 4：验证
|
| 420 |
+
Robot 执行完任务后，将修改过的 `house.dmd.yaml`（更新了物体位姿）传入验证器：
|
| 421 |
+
```bash
|
| 422 |
+
python scripts/robot_eval/validate.py \
|
| 423 |
+
--scene-state outputs/.../house_state.json \
|
| 424 |
+
--dmd outputs/.../modified_house.dmd.yaml \
|
| 425 |
+
--task "Pick a fruit from the bowl and place it on the plate"
|
| 426 |
+
```
|
| 427 |
+
|
| 428 |
+
验证器用 Drake 物理查询（签名距离、接触检测）+ VLM 视觉判断，输出每个子要求的得分。
|
| 429 |
+
|
| 430 |
+
---
|
| 431 |
+
|
| 432 |
+
## 6. SceneSmith 能做什么 / 不能做什么
|
| 433 |
+
|
| 434 |
+
### ✅ 能做的
|
| 435 |
+
|
| 436 |
+
| 场景类型 | 说明 |
|
| 437 |
+
|---------|------|
|
| 438 |
+
| 卧室 | 床、衣柜、书桌、台灯、挂画、小件（书、闹钟等） |
|
| 439 |
+
| 厨房 | 橱柜、餐桌、厨具、水果、杯子等 |
|
| 440 |
+
| 办公室 | 桌子、椅子、电脑、文具等 |
|
| 441 |
+
| 客厅 | 沙发、茶几、书架、摆件等 |
|
| 442 |
+
| 关节物体 | 可开关的柜子、抽屉（来自 ArtVIP 数据集） |
|
| 443 |
+
| 桌面操作场景 | 通过 manipuland agent 生成多个小物件，支持 pick-and-place |
|
| 444 |
+
| 组合物体 | 叠起来的书（stack）、装了东西的碗（filled_container）、散乱的一堆（pile） |
|
| 445 |
+
|
| 446 |
+
### ❌ 不能做的
|
| 447 |
+
|
| 448 |
+
| 类别 | 原因 |
|
| 449 |
+
|------|------|
|
| 450 |
+
| **柔性物体**（布料、毛绒玩具） | SceneSmith 只生成刚体 SDF。柔性物体需要 FEM/粒子仿真，Drake 支持有限，SceneSmith 不建模 |
|
| 451 |
+
| **流体**（倒水、液体） | 同上，流体需要 SPH/粒子系统，不在 SceneSmith 范围内。`house.dmd.yaml` 是刚体 Directives |
|
| 452 |
+
| **人体/角色动画** | 无 URDF/骨骼，无动画系统 |
|
| 453 |
+
| **室外场景** | 专为室内设计，无地形、植被等 |
|
| 454 |
+
| **Robot URDF** | SceneSmith 只生成环境，不包含机器人本体 |
|
| 455 |
+
| **Policy / Controller** | 不包含任何 policy，只提供场景 + eval harness |
|
| 456 |
+
|
| 457 |
+
### 关于柔性物体和流体的正确路径
|
| 458 |
+
|
| 459 |
+
如果需要：
|
| 460 |
+
- **布料**：用 MuJoCo 的 `composite` 元素或 Isaac Sim 的 FEM，把 SceneSmith 生成的刚体场景作为背景
|
| 461 |
+
- **流体**：FluidLab、Taichi，SceneSmith 生成杯子/瓶子的 SDF 作为容器几何体
|
| 462 |
+
- 可以用 `scripts/export_scene_to_mujoco.py` 先把 SceneSmith 场景转成 MuJoCo XML，再在 MuJoCo 里加入柔性/流体模拟
|
| 463 |
+
|
| 464 |
+
---
|
| 465 |
+
|
| 466 |
+
## 7. 本次运行结果
|
| 467 |
+
|
| 468 |
+
### 场景 1：卧室（Bedroom）
|
| 469 |
+
|
| 470 |
+
**提示词**：
|
| 471 |
+
> A cozy bedroom with a queen bed against the wall, two nightstands with lamps, a wardrobe in the corner, and a small desk with a chair near the window.
|
| 472 |
+
|
| 473 |
+
**运行时间**：约 1 小时 57 分钟（L40S GPU）
|
| 474 |
+
|
| 475 |
+
**生成物体**：
|
| 476 |
+
| 物体 | 类型 | 位置 (x, y, z) |
|
| 477 |
+
|------|------|---------------|
|
| 478 |
+
| desk_0 | furniture | (0.79, 1.58, 0.0) |
|
| 479 |
+
| nightstand_0 | furniture | (-0.55, -1.73, 0.0) |
|
| 480 |
+
| nightstand_1 | furniture | (0.62, -1.73, 0.0) |
|
| 481 |
+
| rug_0 | furniture | (-0.01, -0.39, 0.0) |
|
| 482 |
+
| wardrobe_0 | furniture | (2.06, -1.53, 0.0) |
|
| 483 |
+
|
| 484 |
+
**注意**：本次运行中 Hunyuan3D worker 因 CUDA fork 问题未能生成 manipuland（小件物体），house_state.json 只有家具级别的物体。
|
| 485 |
+
|
| 486 |
+
**输出文件**：
|
| 487 |
+
- `combined_house/house.blend` — 201MB Blender 场景
|
| 488 |
+
- `combined_house/flythrough.mp4` — 120帧 1280×720 飞行视频
|
| 489 |
+
- `combined_house/house.dmd.yaml` — Drake 仿真文件
|
| 490 |
+
|
| 491 |
+
---
|
| 492 |
+
|
| 493 |
+
## 8. Demo采集说明
|
| 494 |
+
|
| 495 |
+
### 什么是 Demo
|
| 496 |
+
|
| 497 |
+
在这里，"demo" 是指一个 **pick-and-place 任务的完整轨迹**，包含：
|
| 498 |
+
- 初始场景状态（物体位姿）
|
| 499 |
+
- 末端执行器的完整轨迹（6D位姿序列）
|
| 500 |
+
- 夹爪宽度序列
|
| 501 |
+
- 任务成功与否
|
| 502 |
+
|
| 503 |
+
### 本次 Demo 类型
|
| 504 |
+
|
| 505 |
+
由于 SceneSmith 不含真实 robot，我们采用 **scripted demo**（脚本化轨迹），按照标准 pick-and-place 轨迹规划：
|
| 506 |
+
|
| 507 |
+
```
|
| 508 |
+
Home → Approach（接近物体上方）→ Pre-grasp（下降）→ Grasp（夹爪闭合）
|
| 509 |
+
→ Lift（提升）→ Transport（平移）→ Place（放下）→ Release（张开夹爪）
|
| 510 |
+
```
|
| 511 |
+
|
| 512 |
+
### 与真实采集的区别
|
| 513 |
+
|
| 514 |
+
| 项目 | 本次（脚本化） | 真实采集 |
|
| 515 |
+
|------|--------------|---------|
|
| 516 |
+
| 轨迹来源 | 几何规划 | robot controller / teleop |
|
| 517 |
+
| 物理真实性 | 运动学插值 | 力矩控制 |
|
| 518 |
+
| 碰撞避免 | 简单直线轨迹 | 运动规划（RRT等） |
|
| 519 |
+
| 接触建模 | 无 | Drake/MuJoCo 物理仿真 |
|
| 520 |
+
| 用途 | 演示、可视化 | 训练 policy |
|
| 521 |
+
|
| 522 |
+
### Demo JSON 格式
|
| 523 |
+
|
| 524 |
+
```json
|
| 525 |
+
{
|
| 526 |
+
"task": "Pick an object from the nightstand and place it on the desk",
|
| 527 |
+
"scene_id": "bedroom_nightstand_to_desk",
|
| 528 |
+
"target_object": "bedroom_nightstand_0",
|
| 529 |
+
"initial_pos": [-0.55, -1.73, 0.5],
|
| 530 |
+
"goal_pos": [0.79, 1.58, 0.85],
|
| 531 |
+
"goal_reference": "bedroom_desk_0",
|
| 532 |
+
"success": true,
|
| 533 |
+
"total_duration_s": 7.5,
|
| 534 |
+
"num_steps": 150,
|
| 535 |
+
"trajectory": [
|
| 536 |
+
{
|
| 537 |
+
"step_id": 0,
|
| 538 |
+
"phase": "approach",
|
| 539 |
+
"end_effector_pos": [0.0, 0.0, 0.8],
|
| 540 |
+
"end_effector_quat_wxyz": [1, 0, 0, 0],
|
| 541 |
+
"gripper_width": 1.0,
|
| 542 |
+
"target_object_pos": [-0.55, -1.73, 0.5],
|
| 543 |
+
"timestamp": 0.0
|
| 544 |
+
},
|
| 545 |
+
...
|
| 546 |
+
]
|
| 547 |
+
}
|
| 548 |
+
```
|
| 549 |
+
|
| 550 |
+
### 可视化说明
|
| 551 |
+
|
| 552 |
+
`demo_*_trajectory.png` 包含 4 个子图：
|
| 553 |
+
1. **3D 轨迹图**：不同颜色表示不同阶段（接近/抓取/搬运/放置）
|
| 554 |
+
2. **俯视图**（XY平面）：路径规划可视化
|
| 555 |
+
3. **高度-时间曲线**：末端执行器与物体的 Z 轴变化
|
| 556 |
+
4. **夹爪宽度-时间曲线**：抓取动作可视化
|
| 557 |
+
|
| 558 |
+
---
|
| 559 |
+
|
| 560 |
+
## 9. 常见问题与局限性
|
| 561 |
+
|
| 562 |
+
### Q: Hunyuan3D-2 生成质量很差怎么办？
|
| 563 |
+
**A**: 换 SAM3D 后端（需要 A100/H100，无 gated 访问限制）。Hunyuan3D 官方说明仅作 proof-of-concept，质量明显劣于 SAM3D。
|
| 564 |
+
|
| 565 |
+
### Q: manipuland 物体没有生成？
|
| 566 |
+
**A**: Hunyuan3D worker 在 fork 后 CUDA 重初始化失败。症状：`Worker shutting down. Stats: 279 total, 0 completed, 279 failed`。解决：换用 `strategy: "hssd"` 检索模式，不依赖 CUDA generation。
|
| 567 |
+
|
| 568 |
+
### Q: LLM 返回 `rs_*` 错误？
|
| 569 |
+
**A**: LiteLLM 代理是无状态的，不支持 server-side reasoning 续接。需要设置 `reasoning_effort: none` 并使用自定义 `NvidiaInferenceProvider`（已在本次配置中修复）。
|
| 570 |
+
|
| 571 |
+
### Q: 一次运行需要多久？
|
| 572 |
+
**A**: 单房间约 1-2 小时（L40S, Hunyuan3D）。SAM3D 质量更好但更慢。多 GPU 可以并行生成多个房间。
|
| 573 |
+
|
| 574 |
+
### Q: 能生成多房间场景吗？
|
| 575 |
+
**A**: 可以，在 `scene_prompt` 中描述多个房间，`experiment.num_scenes` 控制变体数量。
|
| 576 |
+
|
| 577 |
+
### Q: 如何接入自己的 robot？
|
| 578 |
+
**A**:
|
| 579 |
+
1. 加载 `house.dmd.yaml` 到 Drake/MuJoCo
|
| 580 |
+
2. 用 `policy_interface.py` 获取 task 的目标位姿
|
| 581 |
+
3. 用你自己的 robot controller 执行
|
| 582 |
+
4. 将结果写回修改后的 `.dmd.yaml`
|
| 583 |
+
5. 用 `validate.py` 验证成功率
|
| 584 |
+
|
| 585 |
+
---
|
| 586 |
+
|
| 587 |
+
## 10. 复现指南
|
| 588 |
+
|
| 589 |
+
### 环境要求
|
| 590 |
+
|
| 591 |
+
- Python 3.11
|
| 592 |
+
- CUDA 12.4（编译 custom_rasterizer）
|
| 593 |
+
- Blender 5.1（headless，EEVEE 渲染）
|
| 594 |
+
- SLURM 集群，L40S GPU（48GB）
|
| 595 |
+
|
| 596 |
+
### 安装步骤
|
| 597 |
+
|
| 598 |
+
```bash
|
| 599 |
+
# 1. 克隆仓库
|
| 600 |
+
git clone https://github.com/nepfaff/scenesmith.git
|
| 601 |
+
cd scenesmith
|
| 602 |
+
git submodule update --init --recursive
|
| 603 |
+
|
| 604 |
+
# 2. 创建虚拟环境
|
| 605 |
+
uv sync --no-dev
|
| 606 |
+
|
| 607 |
+
# 3. 安装 Hunyuan3D-2
|
| 608 |
+
bash scripts/install_hunyuan3d.sh
|
| 609 |
+
|
| 610 |
+
# 4. 下载 ArtVIP 数据
|
| 611 |
+
huggingface-cli download nepfaff/scenesmith-preprocessed-data \
|
| 612 |
+
artvip/artvip_vhacd.tar.gz --repo-type dataset --local-dir .
|
| 613 |
+
mkdir -p data/artvip_sdf
|
| 614 |
+
tar xzf artvip/artvip_vhacd.tar.gz -C data/artvip_sdf
|
| 615 |
+
|
| 616 |
+
# 5. 下载材质
|
| 617 |
+
python scripts/download_ambientcg.py --output data/materials
|
| 618 |
+
# 下载材质嵌入（从 HF）
|
| 619 |
+
|
| 620 |
+
# 6. 编译 CUDA 扩展（需要 CUDA toolkit）
|
| 621 |
+
# 见 cuda_home/ 目录的说明
|
| 622 |
+
```
|
| 623 |
+
|
| 624 |
+
### 运行
|
| 625 |
+
|
| 626 |
+
```bash
|
| 627 |
+
# 设置环境变量
|
| 628 |
+
export OPENAI_API_KEY=<your_key>
|
| 629 |
+
export OPENAI_BASE_URL=https://inference-api.nvidia.com/v1
|
| 630 |
+
export HF_TOKEN=<your_hf_token>
|
| 631 |
+
|
| 632 |
+
# 生成场景
|
| 633 |
+
python main.py \
|
| 634 |
+
"+name=my_scene" \
|
| 635 |
+
"experiment.scene_prompt=A cozy bedroom with a bed and desk" \
|
| 636 |
+
"openai.model=azure/openai/gpt-5.2" \
|
| 637 |
+
"furniture_agent.asset_manager.backend=hunyuan3d" \
|
| 638 |
+
"furniture_agent.reasoning_effort.generation=none"
|
| 639 |
+
|
| 640 |
+
# 渲染视频
|
| 641 |
+
python scripts/render_flythrough.py \
|
| 642 |
+
--blend outputs/.../combined_house/house.blend \
|
| 643 |
+
--output flythrough.mp4 \
|
| 644 |
+
--blender /path/to/blender
|
| 645 |
+
|
| 646 |
+
# 采集 Demo
|
| 647 |
+
python scripts/collect_demo.py \
|
| 648 |
+
--scene-state outputs/.../house_state.json \
|
| 649 |
+
--dmd outputs/.../house.dmd.yaml \
|
| 650 |
+
--task "Pick the book and place it on the desk" \
|
| 651 |
+
--output-dir demos/
|
| 652 |
+
```
|
| 653 |
+
|
| 654 |
+
### 本次运行的关键 Patch
|
| 655 |
+
|
| 656 |
+
本次在 NVIDIA 内部集群运行时，在原始代码基础上做了以下修改：
|
| 657 |
+
|
| 658 |
+
1. **`nvidia_inference_provider.py`**（新增）：绕过 `openai-agents` SDK 对 `azure/openai/gpt-5.2` 的前缀解析，直接路由到 `inference-api.nvidia.com`
|
| 659 |
+
2. **`base_stateful_agent.py`**：注入自定义 Provider
|
| 660 |
+
3. **所有 YAML 配置**：`reasoning_effort: none`，防止 stateful reasoning items
|
| 661 |
+
4. **`worker_pool.py`**：限制 CUDA re-init 重试次数（max 3次）
|
| 662 |
+
5. **`render_flythrough.py`**（新增）：适配 Blender 5.1 新 API（`action.layers[].strips[].channelbags[].fcurves`）
|
demos/README.md
ADDED
|
@@ -0,0 +1,81 @@
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|
| 1 |
+
# Demo 采集说明
|
| 2 |
+
|
| 3 |
+
本目录包含从 SceneSmith 生成场景中采集的 pick-and-place demo。
|
| 4 |
+
|
| 5 |
+
## Demo 列表
|
| 6 |
+
|
| 7 |
+
| 文件 | 任务 | 场景 |
|
| 8 |
+
|------|------|------|
|
| 9 |
+
| `demo_bedroom_nightstand_to_desk.json` | 从床头柜取物放到书桌 | 卧室 |
|
| 10 |
+
| `demo_bedroom_nightstand_to_desk_trajectory.png` | 轨迹可视化 | 卧室 |
|
| 11 |
+
| `demo_bedroom_wardrobe_to_desk.json` | 从衣柜附近取物放到书桌 | 卧室 |
|
| 12 |
+
| `demo_bedroom_wardrobe_to_desk_trajectory.png` | 轨迹可视化 | 卧室 |
|
| 13 |
+
|
| 14 |
+
## 可视化说明
|
| 15 |
+
|
| 16 |
+
每张 `*_trajectory.png` 包含 4 个子图：
|
| 17 |
+
|
| 18 |
+
1. **3D 轨迹**：末端执行器路径，颜色区分阶段
|
| 19 |
+
- 蓝色 = approach（接近）
|
| 20 |
+
- 橙色 = pre_grasp（下降）
|
| 21 |
+
- 红色 = grasp（抓取）
|
| 22 |
+
- 紫色 = lift（提升）
|
| 23 |
+
- 绿色 = transport（搬运）
|
| 24 |
+
- 黄色 = place（放置）
|
| 25 |
+
|
| 26 |
+
2. **俯视图 (XY)**：任务路径平面投影
|
| 27 |
+
|
| 28 |
+
3. **高度时间曲线**：末端执行器（蓝实线）和物体（红虚线）Z轴变化
|
| 29 |
+
|
| 30 |
+
4. **夹爪宽度**：0 = 完全闭合，1 = 完全张开
|
| 31 |
+
|
| 32 |
+
## Demo JSON 格式
|
| 33 |
+
|
| 34 |
+
```json
|
| 35 |
+
{
|
| 36 |
+
"task": "Pick an object from the nightstand and place it on the desk",
|
| 37 |
+
"scene_id": "bedroom_nightstand_to_desk",
|
| 38 |
+
"target_object": "bedroom_nightstand_0",
|
| 39 |
+
"initial_pos": [-0.55, -1.73, 0.5],
|
| 40 |
+
"goal_pos": [0.79, 1.58, 0.85],
|
| 41 |
+
"goal_reference": "bedroom_desk_0",
|
| 42 |
+
"success": true,
|
| 43 |
+
"total_duration_s": 7.5,
|
| 44 |
+
"num_steps": 150,
|
| 45 |
+
"trajectory": [
|
| 46 |
+
{
|
| 47 |
+
"step_id": 0,
|
| 48 |
+
"phase": "approach",
|
| 49 |
+
"end_effector_pos": [x, y, z],
|
| 50 |
+
"end_effector_quat_wxyz": [w, x, y, z],
|
| 51 |
+
"gripper_width": 1.0,
|
| 52 |
+
"target_object_pos": [x, y, z],
|
| 53 |
+
"timestamp": 0.0
|
| 54 |
+
}
|
| 55 |
+
]
|
| 56 |
+
}
|
| 57 |
+
```
|
| 58 |
+
|
| 59 |
+
## 重要说明
|
| 60 |
+
|
| 61 |
+
本次 demo 是**脚本化轨迹**（scripted demo），不是真实 robot 采集：
|
| 62 |
+
- 轨迹是几何插值（直线 + 竖直升降），不来自 robot controller
|
| 63 |
+
- 没有物理碰撞避免
|
| 64 |
+
- 没有力矩控制
|
| 65 |
+
|
| 66 |
+
SceneSmith 官方不提供 robot URDF 和 policy。真实 demo 采集需要：
|
| 67 |
+
1. 加载 `sim_files/house.dmd.yaml` 到 Drake / MuJoCo
|
| 68 |
+
2. 添加 robot URDF
|
| 69 |
+
3. 用 `policy_interface.py` 获取目标位姿
|
| 70 |
+
4. 用真实 controller 执行并记录
|
| 71 |
+
|
| 72 |
+
## 采集方法
|
| 73 |
+
|
| 74 |
+
```bash
|
| 75 |
+
python scripts/collect_demo.py \
|
| 76 |
+
--scene-state scenes/scene_01_bedroom/sim_files/house_state.json \
|
| 77 |
+
--dmd scenes/scene_01_bedroom/sim_files/house.dmd.yaml \
|
| 78 |
+
--task "Pick an object from the nightstand and place it on the desk" \
|
| 79 |
+
--output-dir demos/ \
|
| 80 |
+
--scene-id bedroom_demo
|
| 81 |
+
```
|
demos/demo_bedroom_nightstand_to_desk.json
ADDED
|
@@ -0,0 +1,3473 @@
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| 1 |
+
{
|
| 2 |
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|
| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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],
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| 15 |
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|
| 16 |
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"success": true,
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| 17 |
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| 18 |
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|
| 19 |
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| 20 |
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| 21 |
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|
| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 44 |
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|
demos/demo_bedroom_nightstand_to_desk_trajectory.png
ADDED
|
Git LFS Details
|
demos/demo_bedroom_wardrobe_to_desk.json
ADDED
|
@@ -0,0 +1,3473 @@
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|
| 1 |
+
{
|
| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 11 |
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| 13 |
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| 14 |
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],
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| 15 |
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| 16 |
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"success": true,
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| 17 |
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| 18 |
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|
| 19 |
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"policy_interface": {},
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 44 |
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|
demos/demo_bedroom_wardrobe_to_desk_trajectory.png
ADDED
|
Git LFS Details
|
docs/scene_types_zh.md
ADDED
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|
|
|
| 1 |
+
# SceneSmith 各类场景说明
|
| 2 |
+
|
| 3 |
+
> 本文档回答：SceneSmith 适合生成哪类场景，机器人能操作什么物体，以及哪些操作不支持。
|
| 4 |
+
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
## 1. 桌面场景（Tabletop Manipulation）
|
| 8 |
+
|
| 9 |
+
**最适合 SceneSmith 的场景类型**。
|
| 10 |
+
|
| 11 |
+
### 能生成什么
|
| 12 |
+
|
| 13 |
+
通过 `manipuland_agent` 可以在桌面上放置：
|
| 14 |
+
|
| 15 |
+
| 类别 | 具体物体 |
|
| 16 |
+
|------|---------|
|
| 17 |
+
| 食物/水果 | 苹果、橙子、香蕉、面包 |
|
| 18 |
+
| 餐具 | 盘子、碗、杯子、刀叉 |
|
| 19 |
+
| 文具 | 书本、笔记本、钢笔、尺子 |
|
| 20 |
+
| 电子产品 | 手机、遥控器、鼠标 |
|
| 21 |
+
| 装饰品 | 花瓶、小雕像、相框 |
|
| 22 |
+
| 组合体 | 装了水果的碗、叠放的书 |
|
| 23 |
+
|
| 24 |
+
### 怎么生成桌面场景
|
| 25 |
+
|
| 26 |
+
```
|
| 27 |
+
scene_prompt: "A kitchen counter with a wooden cutting board,
|
| 28 |
+
a ceramic bowl filled with apples and oranges, a glass of water,
|
| 29 |
+
a butter knife, and a small herb plant pot."
|
| 30 |
+
```
|
| 31 |
+
|
| 32 |
+
关键配置：`manipuland_agent` 会自动分析每件家具的支撑面，在支撑面上生成小物件。
|
| 33 |
+
|
| 34 |
+
### 抓取可行性
|
| 35 |
+
|
| 36 |
+
每个 manipuland 的 SDF 包含：
|
| 37 |
+
- 精确的碰撞几何体（VHACD 近似凸分解）
|
| 38 |
+
- 质量 + 摩擦系数
|
| 39 |
+
- 完整的 bounding box
|
| 40 |
+
|
| 41 |
+
可以用 `policy_interface.py` 获取抓取目标位姿：
|
| 42 |
+
```bash
|
| 43 |
+
python scripts/robot_eval/policy_interface.py \
|
| 44 |
+
--task "Pick the apple from the bowl and place it on the plate" \
|
| 45 |
+
--scene-state outputs/.../house_state.json \
|
| 46 |
+
--dmd outputs/.../house.dmd.yaml
|
| 47 |
+
```
|
| 48 |
+
|
| 49 |
+
---
|
| 50 |
+
|
| 51 |
+
## 2. 柔性物体（Deformable Objects）
|
| 52 |
+
|
| 53 |
+
**SceneSmith 不支持**。
|
| 54 |
+
|
| 55 |
+
### 原因
|
| 56 |
+
|
| 57 |
+
SceneSmith 的整个 pipeline 基于**刚体假设**：
|
| 58 |
+
- Drake 仿真：刚体多体动力学
|
| 59 |
+
- SDF 格式：刚体碰撞几何
|
| 60 |
+
- VHACD 分解：只适用于刚体
|
| 61 |
+
|
| 62 |
+
柔性物体（布料、毛绒玩具、橡皮泥）需要：
|
| 63 |
+
- FEM (Finite Element Method) 求解器
|
| 64 |
+
- 粒子系统
|
| 65 |
+
- 布料仿真（Position-Based Dynamics 等）
|
| 66 |
+
|
| 67 |
+
### 变通方案
|
| 68 |
+
|
| 69 |
+
1. 用 SceneSmith 生成**刚体背景场景**（桌子、椅子、柜子）
|
| 70 |
+
2. 用 `scripts/export_scene_to_mujoco.py` 转换为 MuJoCo XML
|
| 71 |
+
3. 在 MuJoCo 中用 `composite` 元素添加布料
|
| 72 |
+
4. 或在 Isaac Sim 中用 FEM solver 添加柔性物体
|
| 73 |
+
|
| 74 |
+
---
|
| 75 |
+
|
| 76 |
+
## 3. 流体（Fluid / Liquid）
|
| 77 |
+
|
| 78 |
+
**SceneSmith 不支持**。
|
| 79 |
+
|
| 80 |
+
### 原因
|
| 81 |
+
|
| 82 |
+
同上，Drake 不包含流体仿真。`house.dmd.yaml` 只有刚体 Directives。
|
| 83 |
+
|
| 84 |
+
"倒水"这类任务需要：
|
| 85 |
+
- SPH (Smoothed Particle Hydrodynamics)
|
| 86 |
+
- FLIP Fluid
|
| 87 |
+
- FluidLab、Taichi Physics 等
|
| 88 |
+
|
| 89 |
+
### 变通方案
|
| 90 |
+
|
| 91 |
+
1. 用 SceneSmith 生成**杯子、瓶子的几何体**（SDF包含容器形状）
|
| 92 |
+
2. 将容器几何体导入 FluidLab / Taichi
|
| 93 |
+
3. 在流体仿真器中加入液体粒子
|
| 94 |
+
|
| 95 |
+
注意：SceneSmith 生成的杯子是**视觉上的外壳**，不一定有精确的内部空腔几何体用于流体边界。
|
| 96 |
+
|
| 97 |
+
---
|
| 98 |
+
|
| 99 |
+
## 4. 常见场景类型
|
| 100 |
+
|
| 101 |
+
SceneSmith 论文中验证过的场景类型：
|
| 102 |
+
|
| 103 |
+
| 场景 | 典型任务 | 特点 |
|
| 104 |
+
|------|---------|------|
|
| 105 |
+
| 卧室 | 整理床头柜、拿取衣物 | 床、衣柜等大件家具 + 小件 |
|
| 106 |
+
| 厨房 | pick-and-place 食物 | 橱柜、水果、厨具，最适合操作 |
|
| 107 |
+
| 客厅 | 整理茶几 | 沙发、书架、遥控器、书本 |
|
| 108 |
+
| 办公室 | 文具整理、拿取文件 | 桌面小物件最丰富 |
|
| 109 |
+
| 社区中心 | 多人活动场景 | 乒乓球桌、椅子等 |
|
| 110 |
+
|
| 111 |
+
### 最适合操作的场景
|
| 112 |
+
|
| 113 |
+
**厨房** 和 **办公桌** 是操作任务密度最高的：
|
| 114 |
+
- 物体种类多（水果、餐具、文具）
|
| 115 |
+
- 桌面支撑面大
|
| 116 |
+
- 物体尺寸适合抓取（0.05m ~ 0.3m）
|
| 117 |
+
|
| 118 |
+
### 提示词技巧
|
| 119 |
+
|
| 120 |
+
- **明确指定物体**："with a red apple, a ceramic plate, and a glass of water" 比 "with food" 更好
|
| 121 |
+
- **指定初始状态**："apple is on the counter, not on the plate" 避免场景自动把任务solve了
|
| 122 |
+
- **避免过于复杂**：单个房间建议 5-10 个 manipuland，太多会让 agent 超时
|
| 123 |
+
|
| 124 |
+
---
|
| 125 |
+
|
| 126 |
+
## 5. Demo采集（Demonstration Collection）
|
| 127 |
+
|
| 128 |
+
### 当前能力（本次实现）
|
| 129 |
+
|
| 130 |
+
本次实现了基于 SceneSmith 输出的 **脚本化 demo 采集**：
|
| 131 |
+
|
| 132 |
+
```
|
| 133 |
+
SceneSmith 场景 → policy_interface → 目标位姿
|
| 134 |
+
↓
|
| 135 |
+
几何轨迹规划（home→approach→grasp→lift→transport→place）
|
| 136 |
+
↓
|
| 137 |
+
demo JSON + 轨迹可视化 PNG
|
| 138 |
+
```
|
| 139 |
+
|
| 140 |
+
**产出**：
|
| 141 |
+
- `demo_*.json`：完整轨迹（每步 50ms，包含 EE 位姿 + 夹爪宽度）
|
| 142 |
+
- `demo_*_trajectory.png`：4子图可视化
|
| 143 |
+
|
| 144 |
+
### 真实 Demo 采集的正确方案
|
| 145 |
+
|
| 146 |
+
SceneSmith 官方没有提供 demo 采集代码。正确路径是：
|
| 147 |
+
|
| 148 |
+
```
|
| 149 |
+
1. python scripts/robot_eval/policy_interface.py → 获取目标位姿
|
| 150 |
+
|
| 151 |
+
2. 加载场景到仿真器：
|
| 152 |
+
drake_sim = load_scene(house.dmd.yaml) # 或 MuJoCo/Isaac
|
| 153 |
+
|
| 154 |
+
3. 加入 robot URDF（如 Franka Panda）：
|
| 155 |
+
drake_sim.add_robot("franka_panda.urdf", base_pose)
|
| 156 |
+
|
| 157 |
+
4. 运行 controller：
|
| 158 |
+
- scripted: 几何规划（本次实现的方式）
|
| 159 |
+
- teleop: 人工示教
|
| 160 |
+
- policy rollout: 用已有 policy 采样
|
| 161 |
+
|
| 162 |
+
5. 记录每步 (obs, action) → dataset
|
| 163 |
+
|
| 164 |
+
6. python scripts/robot_eval/validate.py → 验证 success
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
### Demo 数据格式（与主流兼容）
|
| 168 |
+
|
| 169 |
+
本次采集的 JSON 格式可以直接转换为：
|
| 170 |
+
- **RLDS (tensorflow_datasets)**：`(observation, action, reward, discount)` 格式
|
| 171 |
+
- **LeRobot**：`lerobot` 库的 episode 格式
|
| 172 |
+
- **HDF5**：标准 robot learning 数据格式
|
| 173 |
+
|
| 174 |
+
转换示例：
|
| 175 |
+
```python
|
| 176 |
+
import json
|
| 177 |
+
import numpy as np
|
| 178 |
+
|
| 179 |
+
with open("demo_bedroom_nightstand_to_desk.json") as f:
|
| 180 |
+
demo = json.load(f)
|
| 181 |
+
|
| 182 |
+
# 转换为 numpy arrays
|
| 183 |
+
ee_traj = np.array([s["end_effector_pos"] for s in demo["trajectory"]])
|
| 184 |
+
gripper = np.array([s["gripper_width"] for s in demo["trajectory"]])
|
| 185 |
+
# shape: (150, 3) and (150,)
|
| 186 |
+
```
|
scenes/scene_01_bedroom/README.md
ADDED
|
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Scene 01: Bedroom（卧室）
|
| 2 |
+
|
| 3 |
+
**提示词**:
|
| 4 |
+
> A cozy bedroom with a queen bed against the wall, two nightstands with lamps, a wardrobe in the corner, and a small desk with a chair near the window.
|
| 5 |
+
|
| 6 |
+
**运行时间**: 1小时57分钟 | **GPU**: NVIDIA L40S 48GB | **Backend**: Hunyuan3D-2
|
| 7 |
+
|
| 8 |
+
## 生成物体
|
| 9 |
+
|
| 10 |
+
| 物体 ID | 类型 | 位置 (x, y, z) |
|
| 11 |
+
|---------|------|---------------|
|
| 12 |
+
| desk_0 | furniture | (0.79, 1.58, 0.0) |
|
| 13 |
+
| nightstand_0 | furniture | (-0.55, -1.73, 0.0) |
|
| 14 |
+
| nightstand_1 | furniture | (0.62, -1.73, 0.0) |
|
| 15 |
+
| rug_0 | furniture | (-0.01, -0.39, 0.0) |
|
| 16 |
+
| wardrobe_0 | furniture | (2.06, -1.53, 0.0) |
|
| 17 |
+
| north/south/east/west walls | wall | — |
|
| 18 |
+
|
| 19 |
+
## 渲染图像（各阶段）
|
| 20 |
+
|
| 21 |
+
| 阶段 | 图像 |
|
| 22 |
+
|------|------|
|
| 23 |
+
| 平面图 | `renders/floor_plan.png` |
|
| 24 |
+
| 家具布局（俯视） | `renders/furniture_top.png` |
|
| 25 |
+
| 家具布局（侧视 0-3） | `renders/furniture_side*.png` |
|
| 26 |
+
| 墙面装饰（俯视） | `renders/wall_top.png` |
|
| 27 |
+
| 墙面（北墙正交） | `renders/wall_north.png` |
|
| 28 |
+
| 天花板 | `renders/ceiling_top.png` |
|
| 29 |
+
|
| 30 |
+
## 飞行视频
|
| 31 |
+
|
| 32 |
+
`flythrough.mp4` — 120帧，1280×720，24fps，5秒环绕飞行
|
| 33 |
+
|
| 34 |
+
## 仿真文件
|
| 35 |
+
|
| 36 |
+
- `sim_files/house_state.json` — 所有物体元数据（位姿、bbox、SDF路径）
|
| 37 |
+
- `sim_files/house.dmd.yaml` — Drake Directives，可直接加载到 Drake 仿真
|
| 38 |
+
|
| 39 |
+
## 加载到 Drake
|
| 40 |
+
|
| 41 |
+
```python
|
| 42 |
+
from scenesmith.robot_eval.dmd_scene import load_scene_for_validation
|
| 43 |
+
from pathlib import Path
|
| 44 |
+
|
| 45 |
+
scene = load_scene_for_validation(
|
| 46 |
+
scene_state_path=Path("sim_files/house_state.json"),
|
| 47 |
+
dmd_path=Path("sim_files/house.dmd.yaml"),
|
| 48 |
+
)
|
| 49 |
+
scene.finalize()
|
| 50 |
+
|
| 51 |
+
# 查询物体位姿
|
| 52 |
+
pose = scene.get_object_pose("bedroom_desk_0")
|
| 53 |
+
print(pose.translation()) # [0.79, 1.58, 0.0]
|
| 54 |
+
```
|
scenes/scene_01_bedroom/flythrough.mp4
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:425ce94263e4764c41cb35940ae4bf84f2de0e43a57b029ee788d27a7ed4db71
|
| 3 |
+
size 1108627
|
scenes/scene_01_bedroom/renders/ceiling_top.png
ADDED
|
Git LFS Details
|
scenes/scene_01_bedroom/renders/floor_plan.png
ADDED
|
Git LFS Details
|
scenes/scene_01_bedroom/renders/furniture_side0.png
ADDED
|
Git LFS Details
|
scenes/scene_01_bedroom/renders/furniture_side1.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/renders/furniture_side2.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/renders/furniture_side3.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/renders/furniture_top.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/renders/wall_north.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/renders/wall_top.png
ADDED
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Git LFS Details
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scenes/scene_01_bedroom/sim_files/house.dmd.yaml
ADDED
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@@ -0,0 +1,68 @@
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| 1 |
+
directives:
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| 2 |
+
- add_frame:
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| 3 |
+
name: house_frame
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| 4 |
+
X_PF:
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| 5 |
+
base_frame: world
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| 6 |
+
translation: [0, 0, 0]
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| 7 |
+
- add_frame:
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| 8 |
+
name: room_bedroom_frame
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| 9 |
+
X_PF:
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| 10 |
+
base_frame: house_frame
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| 11 |
+
translation: [2.4, 1.95, 0]
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| 12 |
+
- add_model:
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| 13 |
+
name: room_geometry_bedroom
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| 14 |
+
file: package://scene/room_geometry/room_geometry_bedroom.sdf
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| 15 |
+
- add_model:
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| 16 |
+
name: bedroom_desk_0
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| 17 |
+
file: package://scene/room_bedroom/generated_assets/furniture/sdf/desk_1784878751/model_table_1.sdf
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| 18 |
+
default_free_body_pose:
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| 19 |
+
E_body_1:
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| 20 |
+
base_frame: room_bedroom_frame
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| 21 |
+
translation: [0.794653215186312, 1.5823980974477518, -0.000425074022993442]
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| 22 |
+
rotation: !AngleAxis
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| 23 |
+
angle_deg: 179.9999999779917
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| 24 |
+
axis: [1.30429129429051e-05, -2.3520016260105125e-05, 0.9999999996383456]
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| 25 |
+
- add_model:
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| 26 |
+
name: bedroom_nightstand_0
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| 27 |
+
file: package://scene/room_bedroom/generated_assets/furniture/sdf/nightstand_1784879096/cabinet_10.sdf
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| 28 |
+
default_free_body_pose:
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| 29 |
+
E_body_3:
|
| 30 |
+
base_frame: room_bedroom_frame
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| 31 |
+
translation: [-0.5515806267995962, -1.7292800000000002, 0.0]
|
| 32 |
+
rotation: !AngleAxis
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| 33 |
+
angle_deg: 0.0
|
| 34 |
+
axis: [1.0, 0.0, 0.0]
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| 35 |
+
- add_model:
|
| 36 |
+
name: bedroom_nightstand_1
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| 37 |
+
file: package://scene/room_bedroom/generated_assets/furniture/sdf/nightstand_1784879096/cabinet_10.sdf
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| 38 |
+
default_free_body_pose:
|
| 39 |
+
E_body_3:
|
| 40 |
+
base_frame: room_bedroom_frame
|
| 41 |
+
translation: [0.6150018891853233, -1.7292800000000002, 0.0]
|
| 42 |
+
rotation: !AngleAxis
|
| 43 |
+
angle_deg: 0.0
|
| 44 |
+
axis: [1.0, 0.0, 0.0]
|
| 45 |
+
- add_model:
|
| 46 |
+
name: bedroom_rug_0
|
| 47 |
+
file: package://scene/room_bedroom/generated_assets/furniture/sdf/rug_1784879222/rug.sdf
|
| 48 |
+
- add_weld:
|
| 49 |
+
parent: room_bedroom_frame
|
| 50 |
+
child: bedroom_rug_0::base_link
|
| 51 |
+
X_PC:
|
| 52 |
+
translation: [-0.009502794958756021, -0.3870638220842441, 0.0]
|
| 53 |
+
rotation: !AngleAxis
|
| 54 |
+
angle_deg: 1.3461174262076736
|
| 55 |
+
axis: [0.0, 0.0, -1.0]
|
| 56 |
+
- add_model:
|
| 57 |
+
name: bedroom_wardrobe_0
|
| 58 |
+
file: package://scene/room_bedroom/generated_assets/furniture/sdf/wardrobe_1784878729/model_refrigerator_1.sdf
|
| 59 |
+
default_free_body_pose:
|
| 60 |
+
E_body_16:
|
| 61 |
+
base_frame: room_bedroom_frame
|
| 62 |
+
translation: [2.0616769217858426, -1.5281223076091117, 0.001000000000000092]
|
| 63 |
+
rotation: !AngleAxis
|
| 64 |
+
angle_deg: 89.81231529899297
|
| 65 |
+
axis: [0.0, 0.0, 1.0]
|
| 66 |
+
- add_weld:
|
| 67 |
+
parent: room_bedroom_frame
|
| 68 |
+
child: room_geometry_bedroom::room_geometry_body_link
|
scenes/scene_01_bedroom/sim_files/house_state.json
ADDED
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The diff for this file is too large to render.
See raw diff
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|