AI导哈

AI2-THOR 机器人导航 MCP 服务器

AI2-THOR MCP服务器为LLM智能体提供AI2-THOR家庭机器人仿真控制能力，支持基于视觉的目标识别、nav2风格的异步路径规划与行驶，以及俯视视频录制，适合作为真实nav2机器人MCP接口的仿真验证与开发平台。

  • by pj0620
  • Python
  • 混合部署
  • 更新于
在 GitHub 查看 pj0620/ai2thor-mcp

使用说明

ai2thor-mcp

An MCP server that gives an LLM agent control of a simulated home robot in AI2-THOR. The robot is camera-only (no arms): it looks around, discovers destinations — visible objects and drivable passages detected from depth — and drives to them with nav2-style asynchronous navigation. An overhead camera can record mp4 videos of what it does.

Built as the simulation backend for GPTPet; the tool contract mirrors ROS2 nav2's NavigateToPose semantics (async goal, status polling, cancel, preemption) so a real robot running nav2+SLAM can implement the same MCP interface later.

Tools

Tool What it does
get_current_view() Camera frame annotated with numbered destination markers, plus a JSON list: objects (green boxes, e.g. "Fridge, 2.3 m ahead, closed, contains 2 items") and passages (cyan boxes: doorways/hallways/openings wide enough for the robot). Ids are stable; markers restart each view.
set_nav_goal(destination_id, speed_mps=0.35) Plans a navmesh path to a destination and starts driving in the background. Returns waypoints + ETA immediately. An active goal is preempted.
get_nav_status() idle | planning | following | succeeded | canceled | failed(reason) plus distance traveled/remaining, percent, ETA, waypoint counts.
cancel_nav() Stops the robot within one step.
list_destinations() Everything seen so far, across views. Object ids re-resolve to current positions; passage ids are fixed world waypoints.
get_map() Top-down map: objects, robot pose, remaining path, destinations, goal.
do_move(action, moveMagnitude) / do_rotate(action, degrees) Manual control; preempts active navigation (nav2 teleop behavior).
start_overhead_recording(mode) mode="follow" (default): camera hovers ~2.3 m above the robot and tracks it. mode="scene": fixed orthographic view of the whole floor plan. One recording at a time.
end_overhead_recording() Finalizes and returns the path of a browser-playable H.264 mp4 (real-time: idle gaps are held frames) under data/recordings/.

Plus an MCP resource view://rgb with the raw camera frame.

Setup

Requires Python 3.10–3.12 and uv. The first run downloads the AI2-THOR Unity build (~0.5 GB) and opens a Unity window; the simulator boots lazily on the first tool call.

uv sync            # add --extra dev for the test suite

Running

# streamable HTTP on 0.0.0.0:8000 (endpoint: /mcp, stateless)
uv run python/ai2thor_mcp/main.py --http

# stdio transport
uv run python/ai2thor_mcp/main.py

# pick a scene (default FloorPlan2, a single kitchen; RoboTHOR apartments
# like FloorPlan_Train1_3 have real doorways and corridors)
uv run python/ai2thor_mcp/main.py --http --scene FloorPlan_Train1_3

HTTP is stateless, so multiple MCP clients share the same simulator instance.

Connecting clients

Claude Code:

claude mcp add --transport http ai2thor http://localhost:8000/mcp

Claude Desktop only speaks stdio in claude_desktop_config.json, so bridge it with mcp-remote:

"ai2thor": {
  "command": "npx",
  "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
}

How it works

python/ai2thor_mcp/
├── main.py        MCP tools and wiring
├── sim.py         single lock-guarded owner of the AI2-THOR Controller
├── perception.py  depth → world backprojection; passage detection
├── describe.py    template descriptions + destination registry
├── annotate.py    numbered set-of-marks drawing on the frame
├── nav.py         nav2-style background goal follower
├── recorder.py    overhead mp4 recording (paced ffmpeg writer thread)
├── utils.py       top-down map rendering
└── config.py      all tunables (robot width, speeds, detection thresholds…)
  • Passages come from the depth image: points are backprojected to world space, binned into a polar free-space profile, and gaps are found by level-set scanning (a passage is a run of bins clear well beyond its flanking obstacles). Floor-visibility and navmesh-reachability checks reject windows/mirrors, and a minimum-width filter (MIN_PASSAGE_WIDTH_M = 0.65) keeps only gaps the robot fits through.
  • Path planning uses AI2-THOR's navmesh (GetShortestPath); a follower thread walks the corners at the requested speed with collision retries and one replan, so get_nav_status shows honest live progress.
  • Recording captures the third-party camera frame on every sim step; a paced writer streams one frame per tick to a bundled ffmpeg (imageio-ffmpeg), so the mp4 tracks wall-clock time without ever blocking the simulator. Note: AI2-THOR 5.0 can't remove a camera once added, so after the first recording every step renders it (small cost).

Testing

uv run --extra dev pytest regression -m "not live"   # offline, no Unity
uv run --extra dev pytest regression -m live         # boots Unity, drives the robot

Offline tests cover the geometry with analytic depth scenes (synthetic doorways must be found, narrow gaps and plain walls must not), the nav state machine against a kinematic fake sim, and the recorder's hold-to-wall-clock writer frame-by-frame. scripts/capture_fixture.py regenerates the captured real-frame fixture in data/.

Notes

  • Agent mode is locobot (differential-drive, no manipulation).
  • data/recordings/ is git-ignored output; data/example_* are test fixtures.
  • With Claude Code, the project skill /restart-ai2thor-mcp restarts the server on port 8000 and health-checks it.

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