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Choose the simulation route that matches your goal
Mobile ALOHA is a bimanual mobile manipulation system: it extends ALOHA with a mobile base and a whole-body teleoperation interface for collecting demonstrations. Its research uses supervised behavior cloning and reports benefits from co-training with static ALOHA data. The MuJoCo guide, however, is about loading and controlling a particular model; its workflows should not be confused with the paper’s learning experiments.
| Route | What it does | Physical robot needed? |
|---|---|---|
| MuJoCo viewer | Loads aloha_v1.xml so you can inspect the model, move joints with controls, and view its cameras. |
No |
| Python test script | Runs the guide’s scripted motion test, aloha_ctrl_test.py. |
No, for the documented test route. |
| ROS master-arm following | Uses ROS joint topics from a real master arm to drive the simulated arms through aloha_ctrl.py. |
Yes, for the real-master input described by the guide. |
Set up the documented Mobile ALOHA model
AgileX’s guide, published March 30, 2024, identifies Ubuntu 20.04, MuJoCo 2.1 binaries, and mujoco-py as its software environment. It also notes that the legacy Python package has requirements that include graphics libraries. Treat these details as a historical recipe for that environment: the guide does not establish compatibility with current MuJoCo, Python, Ubuntu, or ROS releases, nor does it document a migration path.
The model directory described in the guide includes MuJoCo XML model descriptions and STL meshes. The XML entry point is aloha_v1.xml; keep the accompanying mesh assets available at the locations expected by the model so MuJoCo can resolve its geometry.
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- Use the guide’s stated Ubuntu 20.04 environment and its MuJoCo 2.1 plus
mujoco-pystack if you are reproducing that recipe. - Obtain the model and its associated STL mesh files from the AgileX guide’s linked materials, preserving their directory structure.
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Inspect joints and camera views in the viewer
Once the XML model is open, use the viewer to examine the simulated robot. The guide describes joint-angle readouts and controls for manually moving joints; the displayed angles and control values are in radians. It also describes enabling three camera views to see the scene from the model’s cameras.
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- Use the angle readouts to observe joint positions as you manipulate controls.
- Move a joint with its viewer control to inspect how the articulated model responds.
- Enable the camera views when you need to check the rendered viewpoints rather than only the main scene view.
Run the scripted motion test
For a basic scripted check, the guide identifies aloha_ctrl_test.py as its Python test script. This is a separate route from following a real master arm: the documented viewer and scripted test do not require physical hardware. The post describes the script but does not provide independently verified results or a current-environment guarantee.
Follow a real master arm through ROS
The ROS workflow in the guide is specifically for making the simulated arms follow a real-world master arm. It uses the left and right joint topics /master/joint_left and /master/joint_right, then runs aloha_ctrl.py. Follow the sequence below as the guide’s instructions, rather than as a verified recipe for modern ROS installations.
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- Start the ROS master with
roscore. - Start the master-arm ROS node or nodes.
- Confirm that both
/master/joint_leftand/master/joint_rightare being published. - Run
aloha_ctrl.pyto start the simulated following workflow.
If either joint topic is absent, the simulation cannot receive that arm’s described input. The guide does not specify a current ROS distribution, node package version, or recovery procedure for connection or message-format errors, so those details must match the ROS and master-arm software actually installed.
Do not confuse this model guide with Google DeepMind’s Aloha Sim
Google DeepMind’s Aloha Sim is a separate Python library containing ALOHA robot-learning and evaluation tasks. Its README provides a task-oriented viewer example, Python unittest discovery, and recommends setting MUJOCO_GL=egl because simulation may otherwise be slow. Those instructions belong to the Aloha Sim repository; they do not establish that AgileX’s Mobile ALOHA XML model, meshes, or control scripts are included in that library.
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| Project | Primary asset or purpose | Documented control or test path |
|---|---|---|
| AgileX Mobile ALOHA guide | aloha_v1.xml model and mesh assets |
Native viewer, aloha_ctrl_test.py, or ROS master-arm following with aloha_ctrl.py |
| Google DeepMind Aloha Sim | Python library of ALOHA simulation learning and evaluation tasks | Task viewer, Python unittest discovery, and its MUJOCO_GL=egl rendering hint |
What the Mobile ALOHA research result does—and does not—measure
The 2024 Mobile ALOHA paper reports that, with 50 demonstrations for each task, co-training can increase success rates by up to 90% in its evaluated mobile manipulation experiments. That is an experimental learning result, not a MuJoCo speed, physics-accuracy, or sim-to-real benchmark, and it is not a guaranteed result for arbitrary tasks.
For source context, see the Mobile ALOHA project page, the AgileX MuJoCo guide, the Google DeepMind Aloha Sim README, the MuJoCo README, and the Mobile ALOHA paper.
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