spatial-computing

Design

Visualizes floorplans from URDF scene files and inserts/removes 3D assets with collision-aware placement on surfaces. Supports semantic instance matching via LLM (e.g., "put lamp on bookshelf", "delete sofa in living room"). Use when working with URDF/USD indoor scenes, floorplan visualization, object placement/deletion, or room-level scene editing.

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Floorplan & Object Placement/Deletion

Overview

Parse indoor scenes from URDF, generate 2D floorplans, or place/remove 3D objects in scenes and write back to URDF/USD. After successful insertion/deletion, the corresponding file is automatically updated based on whether urdf_path/usd_path is provided.

When to use: Use this skill when you need to generate floorplans from URDF, place/delete objects on specified rooms/furniture surfaces, or batch update URDF/USD files.

⚠️ USD updates require room-cli: To update USD files, you must use room-cli instead of python -m, and specify the USD file via --usd_path. room-cli runs on Blender Python which includes the bpy module for OBJ→USD conversion; using python -m with --usd_path will fail with ModuleNotFoundError: No module named 'bpy'.
# ✅ Correct: use room-cli to update both URDF and USD
room-cli -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --usd_path .../scene.usdc ...

Smart File Naming Strategy:

Default behavior: First operation creates scene_updated.urdf, subsequent operations automatically overwrite it
No file bloat: Prevents *_updated_updated.urdf files from accumulating
Safe: Original scene.urdf is never modified unless explicitly requested
Works for both insert and delete: Seamless continuous scene editing

Best Practices & Constraints

1. Workflow for Continuous Scene Editing

Recommended workflow for multiple insert/delete operations:

# Step 1: View current scene
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --list_instances

# Step 2: First insert → creates scene_updated.urdf
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --asset_path .../apple.obj --instance_key apple_1

# Step 3: Second insert → overwrites scene_updated.urdf
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene_updated.urdf \
  --asset_path .../lamp.obj --instance_key lamp_1

# Step 4: Delete operation → overwrites scene_updated.urdf
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene_updated.urdf \
  --delete_instance apple_1

Key benefits:

✅ No multiple *_updated_updated.urdf files
✅ Original file scene.urdf always preserved
✅ Continuous insert/delete operations are seamless

Result: Clean workflow with only two files:

scene.urdf (original, untouched)
scene_updated.urdf (final state)

2. When to Use Different Strategies

Strategy	Use Case	Example
suffix (default)	Standard workflow, continuous editing	Most scenarios
timestamp	Version tracking, backup before risky changes	`scene_20260311_180235.urdf`
overwrite	Confident single operation, no backup needed	Automated pipelines

3. Performance Optimization: Batch Insert

Problem: CLI commands re-parse URDF and process all meshes on every call, leading to slow performance when inserting multiple objects.

Solution: Use --batch_insert_config with JSON config for 3-4x speedup:

Step 1: Create JSON config file (batch_chairs.json):

[
    {
        "asset_path": "path/to/chair1.obj",
        "instance_key": "chair_1",
        "beside_instance": "table_dining_7178300",
        "in_room": "dining_room_0_floor"
    },
    {
        "asset_path": "path/to/chair2.obj",
        "instance_key": "chair_2",
        "beside_instance": "table_dining_7178300",
        "in_room": "dining_room_0_floor"
    },
    {
        "asset_path": "path/to/chair3.obj",
        "instance_key": "chair_3",
        "beside_instance": "table_dining_7178300",
        "in_room": "dining_room_0_floor"
    }
]

Step 2: Run batch insertion:

# Update URDF only
room-cli -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --batch_insert_config batch_chairs.json

# Update both URDF and USD
room-cli -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --usd_path .../scene.usdc \
  --batch_insert_config batch_chairs.json

JSON Config Fields:

asset_path (required): Path to asset mesh file (.obj)
instance_key (required): Unique instance identifier
beside_instance: Place beside target instance (on floor). Must be exact name.
on_instance: Place on top of target instance. Must be exact name.
in_room: Limit placement to specified room. Must be exact name.
beside_distance: Max distance from target (default: 0.5m)
place_strategy: "random" or "top" (default: "random")

⚠️ Batch insert does NOT support fuzzy/semantic matching. beside_instance, on_instance, and in_room require exact names. Use --list_instances to get the exact instance / room names first:
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --list_instances

When to Use:

✅ Inserting 2+ objects at once
✅ Performance-critical workflows
✅ Automated scene generation pipelines

⚠️ Batch config file cleanup: The JSON config file for --batch_insert_config is a temporary file and must not be left in the project root directory. Always:

Create the JSON config in the same directory as the target scene (e.g., .../House_seed5/batch_fruits.json).
Delete the JSON config file immediately after the batch command finishes, regardless of success or failure.

3. Important Constraints

USD prim hierarchy: When updating a USD file, inserted assets must be authored under the stage's defaultPrim (for existing room exports this is usually /World), for example /World/<instance_key>. Do not write inserted objects as pseudo-root children like /<instance_key> because USD references in IsaacSim load the defaultPrim only; root-level siblings outside defaultPrim will be omitted.

❌ Wrong: Using scene.urdf for all operations (ignores previous changes)

# This will NOT see apple_1 from previous operation
python -m ... --urdf_path scene.urdf --asset_path lamp.obj

✅ Right: Chain operations using scene_updated.urdf

# This WILL see apple_1 and add lamp_1
python -m ... --urdf_path scene_updated.urdf --asset_path lamp.obj

LLM Environment (Required for Semantic Matching)

Before using resolve_instance_with_llm for semantic matching in Python, configure the LLM API and ensure access to the interface. Prompt the user if access is unavailable.

# Use the project-provided env (Azure + proxy, etc.), if outputs/env.sh exists:
source outputs/env.sh

Core Convention: Placement/Deletion/Query Requests Must Use This Skill's Interface

When users request "put A somewhere", "delete A", "find A", or "visualize urdf", you must implement it using this skill's interface:

User Request Example	Corresponding Parameter & Usage
Put A on B (e.g., "put lamp on bookshelf")	`on_instance` (instance name, obtained from `--list_instances`)
Put A beside B (e.g., "put chair beside table")	`beside_instance` (instance name, obtained from `--list_instances`); placed on floor near target
Put A in a room (e.g., "put table in living room")	`in_room` (room name, obtained from `--list_instances`)
Put A beside B in a room (e.g., "put chair beside table in kitchen")	`beside_instance` + `in_room`
Put A on B in a room (e.g., "put apple on table in living room")	Decomposed into "apple" and "living room" as `in_room` and `on_instance`
Delete A (e.g., "delete lamp")	`delete_instance` (instance name or semantic description, supports fuzzy matching with LLM)
Delete A in a room (e.g., "delete sofa in living room")	`delete_instance` + `delete_in_room` (only deletes if instance is in specified room)
Find A (e.g., "find lamp", "where is the bed")	`query_instance` (returns center coordinates [x, y, z], supports fuzzy matching with LLM)

| output_strategy | "suffix" / "timestamp" / "overwrite" | File naming strategy for output files. Default is "suffix" (non-destructive). | | Visualize scene.urdf | cli.main --urdf_path .../scene.urdf --output_path .../floorplan.png; output_path defaults to same directory as urdf |

When no match is found, prompt "The object/room does not exist, please re-enter" and provide the current scene object or room list.
Instance names should not use the <link name="..."> from URDF. Recommended: Run --list_instances before placement/deletion/query to view current instance name list, and select the closest semantic match.

CLI Examples

Tip: The URDF file is typically located at <room_folder>/urdf/export_scene/scene.urdf (e.g., outputs/rooms/Kitchen_seed0/urdf/export_scene/scene.urdf).

Example 1: View Instance Names and Room Names in Current Scene

# View instance names and room names in current scene (to fill in --on_instance / --in_room)
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --list_instances

Example 2: Visualize Floorplan Only

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png

Example 3: Put Lamp on Bookshelf (Place on an Object)

--on_instance can be filled with the instance name returned by --list_instances or a semantic description.

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --asset_path .../lamp.obj --instance_key lamp_on_bookcase --on_instance 书柜

Example 4: Put Table in Living Room (Place in a Room)

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --asset_path .../table.obj --instance_key table_1 \
  --in_room living_room

Example 5: Put Apple on Table in Living Room (Room + on Object)

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --asset_path .../apple.obj --instance_key apple_1 \
  --in_room living_room --on_instance table --place_strategy top

Example 7: Delete an Object (Exact Name)

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --delete_instance bed_192207

Example 8: Delete Object with Fuzzy Matching (Semantic Description)

Requires LLM environment (see "LLM Environment" section).

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --delete_instance "沙发"

Example 9: Delete Object in Specific Room

Only deletes the instance if it's located in the specified room.

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --output_path .../floorplan.png \
  --delete_instance "沙发" --delete_in_room "客厅"

Update both URDF and USD using room-cli:

room-cli -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf --usd_path .../scene.usdc \
  --output_path .../floorplan.png \
  --delete_instance "沙发" --delete_in_room "客厅"

Example 10: Query Instance Position (Exact Name)

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --query_instance bed_192207

Expected output:

📍 Instance 'bed_192207' center: (-0.9250, -6.5830, 0.5000)

Example 11: Query Instance Position with Fuzzy Matching

Requires LLM environment (see "LLM Environment" section).

python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --query_instance "床"

Alternative Strategies

Timestamp - Unique versioning for each operation:

# Output: scene_20260311_180235.urdf
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --asset_path .../apple.obj --instance_key apple_1 \
  --output_strategy timestamp

Overwrite - Directly overwrite original (use with caution):

# Overwrites: scene.urdf
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path .../scene.urdf \
  --asset_path .../apple.obj --instance_key apple_1 \
  --output_strategy overwrite

Query Instance Position

Query the center coordinates of an instance in the scene. Supports fuzzy matching with LLM.

CLI Interface:

# Exact instance name
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path scene.urdf \
  --query_instance bed_192207

# Fuzzy matching (requires GPT)
source outputs/env.sh
python -m embodied_gen.skills.spatial-computing.cli.main \
  --urdf_path scene.urdf \
  --query_instance "床"

6. Common Parameters

Parameter	Meaning
`in_room`	Limit placement to specified room
`on_instance`	Place on top of specified instance; must be exact instance name (obtained via `resolve_instance_with_llm`)
`beside_instance`	Place beside specified instance on the floor; must be exact instance name (obtained via `resolve_instance_with_llm`). Mutually exclusive with `on_instance`
`beside_distance`	Max distance (meters) from target instance for beside placement. Default `0.5`. Increase if placement fails
`place_strategy`	`"random"` random placement (default, e.g., bookshelf with 3 layers will randomly select one), `"top"` select highest surface
`rotation_rpy`	Not required by default; pass (roll, pitch, yaw) radians for special orientations
`delete_instance`	Instance name or semantic description to delete (supports fuzzy matching with LLM). Cannot delete protected items (walls, floors)
`delete_in_room`	Optional room constraint for deletion - only delete if instance is in this room
`query_instance`	Instance name or semantic description to query center coordinates (supports fuzzy matching with LLM). Returns [x, y, z] position

Roaming Trajectory Generation (Optional)

Generate a smooth, collision-free robot roaming trajectory on a floorplan and overlay it on the floorplan image. Opt-in and off by default: the normal floorplan visualization never draws a trajectory unless you run this CLI. Doors are treated as open passages (excluded from obstacles).

CLI Interface:

python -m embodied_gen.scripts.room_gen.gen_trajectory \
  --urdf_path .../scene.urdf \
  --output_dir .../trajectory \
  --clearance 0.4 --num_waypoints 8

Outputs <stem>_trajectory.json (equidistant {x, y, rot, t} waypoints at a constant speed) and .png (red-path overlay). rot in degrees: 0° = +Y (12 o'clock), counter-clockwise positive, tangent to the path (forward heading); t is the timestamp in seconds.

Next Steps

For complete API, configuration, errors, and dependencies, see REFERENCE.md.