Real-World RL with Dexmal DOS-W1
================================

.. |huggingface| image:: /_static/svg/hf-logo.svg
   :width: 16px
   :height: 16px
   :class: inline-icon

.. figure:: https://raw.githubusercontent.com/RLinf/misc/main/pic/dos-w1.png
   :align: center
   :width: 80%

   Dexmal DOS-W1 dual-arm robot used for the Flow Matching single-arm pick workflow.

Train a Flow Matching policy on the Dexmal DOS-W1 dual-arm robot. The current recipe runs a single-arm pick task with AirBot services, RealSense cameras, keyboard-gated episodes, and SAC/RLPD-style real-world training.

Overview
--------

Train a visual flow policy for the DOS-W1 pick task.

.. grid:: 2 4 4 4
   :gutter: 2

   .. grid-item-card:: Models
      :text-align: center

      Flow policy · ResNet-10

   .. grid-item-card:: Algorithms
      :text-align: center

      SAC · RLPD optional

   .. grid-item-card:: Tasks
      :text-align: center

      Single-arm pick

   .. grid-item-card:: Hardware
      :text-align: center

      DOS-W1 · AirBot · RealSense

| **You'll do:** install DOS-W1 env → calibrate target joints → configure keyboard gating → collect optional demos → train.
| **Prerequisites:** :doc:`Installation </rst_source/start/installation>` · AirBot SDK on robot node · local network · safety operator.

Tasks
~~~~~

.. list-table::
   :header-rows: 1
   :widths: 24 24 24

   * - Task
     - Config / entry point
     - Description
   * - Dummy smoke test
     - ``dosw1_dummy_sac_mlp_pick.yaml``
     - Validate config wiring without cameras or hardware calls.
   * - Data collection
     - ``dosw1_collect_data``
     - Collect optional teleoperated demos for RLPD warm start.
   * - Training
     - ``dosw1_pick_sac_flow(_async)``
     - Train the flow policy with SAC on the pick task.

Observation and Action
~~~~~~~~~~~~~~~~~~~~~~

.. list-table::
   :header-rows: 1
   :widths: 24 24

   * - Field
     - Description
   * - Observation
     - ``cam_front``, ``cam_left``, ``cam_right`` images plus dual-arm joint/gripper state.
   * - Action
     - 14-D joint-space action: left/right 6 joints plus gripper widths.
   * - Reward
     - Dense reach/grasp/lift shaping with terminal success at ``target_lift_joint``.
   * - Prompt
     - ``Perform the DOSW1 dual-arm manipulation task.``

Hardware Setup
--------------

- **Robot**: DOS-W1 dual-arm with leader arms (used for teleoperation).
- **Cameras**: Up to three Intel RealSense cameras (serial numbers fed into
  ``cluster.node_groups[<dosw1>].hardware.configs[*].camera_serials``).
  The code also works with 1 camera if you set ``image_num: 1`` and only
  list one serial.
- **Training / Rollout Node**: A machine with at least one CUDA GPU
  (RTX 4090 or better recommended).
- **Robot Controller Node**: The DOS-W1 itself (or a small PC in the same
  local network) running the AirBot gRPC services.

.. warning::

   The training node and the DOS-W1 must be reachable over the **same local
   network**. The default AirBot gRPC ports are:

   - ``left_arm_port = 50051``  (left follower)
   - ``left_lead_port = 50050`` (left leader)
   - ``right_arm_port = 50053`` (right follower)
   - ``right_lead_port = 50052`` (right leader)

Installation
------------

The robot node and the training / rollout node share the same install command,
but only the robot node needs the official **AirBot SDK** (``airbot_py``
wheel + ``airbot_api`` source). The GPU node talks to the robot over gRPC
only and does not import ``airbot_py``.

Robot Node
~~~~~~~~~~

The AirBot SDK is typically pre-deployed on a DOS-W1 robot, so the install
command below can pick it up automatically.

A. Clone RLinf Repository
^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: bash

   # For mainland China users, you can use the following for better download speed:
   # git clone https://ghfast.top/github.com/RLinf/RLinf.git
   git clone https://github.com/RLinf/RLinf.git
   cd RLinf

B. Install Dependencies
^^^^^^^^^^^^^^^^^^^^^^^

.. code:: bash

   # Mainland China users can add --use-mirror for better download speed.
   bash requirements/install.sh embodied --env dosw1
   source .venv/bin/activate

By default the installer looks for the AirBot SDK at:

- ``~/dos_w1/airbot/5.1.6/airbot_py-5.1.6-py3-none-any.whl``
- ``~/dos_w1/airbot/airbot_api``  (installed in editable mode)

If your SDK files live elsewhere, point the installer at the correct paths:

.. code:: bash

   export DOSW1_SDK_WHEEL=/path/to/airbot_py-5.1.6-py3-none-any.whl
   export DOSW1_API_PATH=/path/to/airbot_api
   bash requirements/install.sh embodied --env dosw1

Training / Rollout Nodes
~~~~~~~~~~~~~~~~~~~~~~~~

A. Clone RLinf Repository
^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: bash

   # For mainland China users, you can use the following for better download speed:
   # git clone https://ghfast.top/github.com/RLinf/RLinf.git
   git clone https://github.com/RLinf/RLinf.git
   cd RLinf

B. Install Dependencies
^^^^^^^^^^^^^^^^^^^^^^^

.. code:: bash

   # Mainland China users can add --use-mirror for better download speed.
   bash requirements/install.sh embodied --env dosw1
   source .venv/bin/activate

A GPU server typically does not have a ``~/dos_w1/airbot`` directory, so
the installer prints a warning and **skips** the AirBot SDK step — this is
expected. The remaining dependencies (``embodied`` extra, ``evdev``,
``opencv-python``, RLinf itself) are installed as usual.

.. note::

   Do **not** run only ``uv pip install -e .`` — it will not install the
   ``embodied`` extra and you will be missing environment-side dependencies
   (``evdev``, ``opencv-python``, RealSense bindings, …).

Download the Model
------------------

Download the ResNet-10 pretrained checkpoint used by the Flow Matching
policy (``actor.model.encoder_config.ckpt_name: resnet10_pretrained.pt``):

.. code:: bash

   # Method 1: git clone
   git lfs install
   git clone https://huggingface.co/RLinf/RLinf-ResNet10-pretrained

   # Method 2: huggingface-hub (mainland China: export HF_ENDPOINT=https://hf-mirror.com)
   pip install huggingface-hub
   hf download RLinf/RLinf-ResNet10-pretrained --local-dir RLinf-ResNet10-pretrained

Point ``actor.model.model_path`` and ``rollout.model.model_path`` in the YAML
at the downloaded directory.

Run It
------

Target Joint Calibration
~~~~~~~~~~~~~~~~~~~~~~~~

``target_grasp_joint`` and ``target_lift_joint`` must reflect the physical
layout of your workspace. The recommended way to calibrate them is to
teleoperate the follower arm to the desired pose via the leader arm and read
back the current joint positions.

1. On the robot node, start the follower service (for example
   ``sh ~/dos_w1/airbot/whole_start.sh``) and verify all panels come up
   without errors.

2. Activate the venv and run the teleoperation check script:

.. code-block:: bash

   source .venv/bin/activate

   python toolkits/realworld_check/test_dosw1_controller.py \
       --robot-url <ROBOT_IP> \
       --print-hz 5

3. Drag the left leader arm until the left follower arm is at the desired
   **grasp** pose. The terminal prints lines such as::

     [1713600000.000] left_joint=[-0.4725 -1.1332  0.6510  1.4082 -0.5987 -1.0904  0.0700]
     [1713600000.000] left_eef=[...] right_eef=[...]

   ``left_joint`` has **7** values: the first 6 are joint angles (radians),
   the last is the gripper width. Copy the **first 6** values into
   ``target_grasp_joint``.

4. Move the leader arm up to a safe lift pose and copy the new
   ``left_joint[:6]`` values into ``target_lift_joint``.

.. tip::

   ``left_eef`` and ``right_eef`` are useful for sanity-checking that the
   end-effector is where you expect, but the target fields expect **joint
   angles**, not ee poses.

Cluster Setup
~~~~~~~~~~~~~

Real-world training uses a two-node Ray cluster: one GPU node
(``actor``/``rollout``) and one robot node (``env``).

1. Source the venv on **every** node and export ``RLINF_NODE_RANK`` before
   starting Ray. Ray freezes the interpreter and env vars at ``ray start``
   time, so anything set after will not be visible to the workers.

2. Start Ray:

.. code-block:: bash

   # GPU node (node rank 0, Ray head)
   export RLINF_NODE_RANK=0
   ray start --head --port=6379 --node-ip-address=<GPU_SERVER_IP>

   # Robot node (node rank 1, Ray worker)
   export RLINF_NODE_RANK=1
   ray start --address=<GPU_SERVER_IP>:6379

3. Verify with ``ray status`` that both nodes are connected.

Configuration File
~~~~~~~~~~~~~~~~~~

Use ``examples/embodiment/config/dosw1_pick_sac_flow.yaml`` as the canonical
template. The async variant with weight-sync decoupling is
``dosw1_pick_sac_flow_async.yaml``.

Fields that you should update for your setup:

.. code-block:: yaml

   cluster:
     num_nodes: 2
     component_placement:
       actor:   { node_group: "gpu",   placement: 0 }
       rollout: { node_group: "gpu",   placement: 0 }
       env:     { node_group: dosw1,   placement: 0 }
     node_groups:
       - label: "gpu"
         node_ranks: 0
       - label: dosw1
         node_ranks: 1
         # Robot connection info (gRPC URL / ports / camera serials) is
         # managed by the scheduler, not the env config.
         hardware:
           type: DOSW1
           configs:
             - robot_url: "<ROBOT_IP>"            # DOS-W1 gRPC address
               left_arm_port: 50051
               right_arm_port: 50053
               left_lead_port: 50050
               right_lead_port: 50052
               camera_serials:                    # RealSense serial numbers
                 - "<SERIAL_1>"
                 - "<SERIAL_2>"
                 - "<SERIAL_3>"
               node_rank: 1

   env:
     train:
       keyboard_intervention_wrapper: True
       override_cfg:
         is_dummy: False
         use_dense_reward: True
         target_grasp_joint: [...]                # from calibration above
         target_lift_joint:  [...]                # from calibration above
         max_joint_delta: 0.1                     # rad/step (~5.7°)
         action_scale: 1.0
         # ee-pose safety box (xyz in meters, inclusive)
         left_ee_pose_limit_min: [0.1, -0.35, 0.02]
         left_ee_pose_limit_max: [0.4,  0.08, 0.40]
         right_ee_pose_limit_min: [0.28, -0.01, 0.16]
         right_ee_pose_limit_max: [0.30,  0.01, 0.17]
         enable_human_in_loop: True

   actor:
     model:
       model_path: "/path/to/RLinf-ResNet10-pretrained"
       state_dim: 14        # 6 joints + 1 gripper per arm, dual arm
       action_dim: 14
       image_num: 3         # 1 → cam_left only; 3 → use all three cameras
   rollout:
     model:
       model_path: "/path/to/RLinf-ResNet10-pretrained"

.. warning::

   ``cluster.num_nodes`` must match the actual node count, and each
   ``node_ranks`` entry must equal the ``RLINF_NODE_RANK`` of the
   corresponding machine. Do **not** hand-craft a partial config by copying
   diff snippets — always start from the full
   ``dosw1_pick_sac_flow.yaml`` template.

Keyboard Intervention
~~~~~~~~~~~~~~~~~~~~~

DOS-W1 episodes are gated by a keyboard listener on the robot node. To
enable it, keep these two flags on in the YAML (they already are in the
provided templates):

.. code-block:: yaml

   env:
     train:
       keyboard_intervention_wrapper: True
       override_cfg:
         enable_human_in_loop: True

Before training, give the current user access to ``/dev/input`` event
devices (see the :doc:`franka` *Headless Keyboard Reward Wrapper* section
for the full explanation of ``RLINF_KEYBOARD_DEVICE``):

.. code-block:: bash

   sudo usermod -aG input $USER
   # log out and back in for the group change to take effect

Supported keys during a run:

.. list-table::
   :header-rows: 1
   :widths: 10 90

   * - Key
     - Effect
   * - ``s``
     - During free-teleop / reset, request the start of a new episode.
   * - ``r``
     - Abort the current episode and return to free-teleop (not saved).
   * - ``d``
     - Mark the current episode as "manually done" and save it.
   * - ``p``
     - Switch from ``MODEL`` or ``TELEOP`` into ``PAUSE``.
   * - ``t``
     - From ``PAUSE``, switch into ``TELEOP`` (policy replaced by leader arm).
   * - ``m``
     - From ``PAUSE``, switch back to ``MODEL`` (policy drives the arm).

When ``manual_episode_control_only`` is set, the ``p`` / ``t`` / ``m``
shortcuts are ignored — the run stays in leader-arm teleop mode and only
``s`` / ``r`` / ``d`` are active.

Data Collection (optional, for RLPD)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you want to warm-start training with teleoperated demos, collect them
with the provided script. ``dosw1_collect_data.yaml`` already enables
``enable_human_in_loop`` and the keyboard wrapper, and runs on a **single
robot node** (``cluster.num_nodes: 1``).

On the robot node:

.. code-block:: bash

   source .venv/bin/activate

   bash examples/embodiment/collect_data.sh dosw1_collect_data

Edit the ``cluster.node_groups[<dosw1>].hardware.configs`` entry in
``examples/embodiment/config/dosw1_collect_data.yaml`` (``robot_url``,
the four gRPC ports and ``camera_serials``) to match your hardware
before running.

A typical collection loop:

1. The env enters free-teleop. Use the leader arms to position the
   follower arms at the initial pose.
2. Press ``s`` to start an episode.
3. Teleoperate the grasp + lift.
4. Press ``d`` to save a successful demo, or ``r`` to discard and retry.
5. The script exits after ``runner.num_data_episodes`` (default ``20``)
   saved episodes.

Saved trajectories land in ``<log_path>/demos/``. Plug that path into the
training config:

.. code-block:: yaml

   algorithm:
     demo_buffer:
       load_path: "/path/to/logs/dosw1-collect/<timestamp>/demos"

Remove the ``demo_buffer`` block if you do not want RLPD.

Testing the Setup (Optional)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Before wiring up the real robot, run the tiny dummy config shipped with the
CI suite. It sets ``is_dummy: true`` (no hardware calls) and uses a
state-only MLP so it runs on a single node without cameras:

.. code-block:: bash

   export REPO_PATH=$(pwd)
   ray start --head

   python examples/embodiment/train_embodied_agent.py \
       --config-path $REPO_PATH/tests/e2e_tests/embodied/ \
       --config-name dosw1_dummy_sac_mlp_pick \
       runner.max_epochs=1

Use this to validate the config tree and cluster plumbing end-to-end. It is
**not** a real-robot training recipe — the image pipeline is disabled.

Visualization and Results
~~~~~~~~~~~~~~~~~~~~~~~~~

Once calibration, demos, and the YAML are ready, launch training from the
GPU node:

.. code-block:: bash

   # Synchronous pipeline
   bash examples/embodiment/run_realworld.sh dosw1_pick_sac_flow

   # Async pipeline (decoupled rollout / learner weight sync)
   bash examples/embodiment/run_realworld_async.sh dosw1_pick_sac_flow_async

The scripts pick up logs in ``logs/<timestamp>-<config>/``.

Key Safety Mechanisms
---------------------

``DOSW1Env._execute_model_action`` applies three layers of safety before any
command is sent to the SDK:

1. **Per-step joint clamp** — target joints are clipped to
   ``current ± max_joint_delta`` (default ``0.1`` rad ≈ 5.7° per step).
2. **Absolute joint limits** — clipped to
   ``[joint_limit_min, joint_limit_max]`` (default ``±π``; tighten for your
   workspace).
3. **End-effector safety box** — a binary search along the joint-space
   interpolation finds the largest fraction whose forward-kinematics result
   still fits inside ``left_ee_pose_limit_min/max`` /
   ``right_ee_pose_limit_min/max``. Out-of-box motions are truncated
   automatically.

All three apply identically in TELEOP mode, so an operator with the leader
arm cannot push the follower arm outside the workspace.

Visualization and Results
-------------------------

**TensorBoard**

On the Ray head node:

.. code-block:: bash

   tensorboard --logdir ./logs --port 6006

**Key Metrics**

- **Environment**: ``env/episode_len``, ``env/return``, ``env/reward``,
  ``env/success_once`` (unnormalized episodic success rate — the primary
  signal to monitor).
- **Training (SAC)**: ``train/sac/critic_loss``, ``train/sac/actor_loss``,
  ``train/sac/alpha_loss``, ``train/sac/alpha``, ``train/actor/entropy``,
  ``train/actor/grad_norm``, ``train/critic/grad_norm``.
- **Replay buffer**: ``train/replay_buffer/size``,
  ``train/replay_buffer/{mean,max,min,std}_reward``,
  ``train/replay_buffer/utilization``.

Troubleshooting
---------------

**``ImportError: airbot_sdk is not installed``** (robot node)
  The AirBot wheel was not found at the default path.
  Re-run the installer with ``DOSW1_SDK_WHEEL`` / ``DOSW1_API_PATH``
  pointing at your SDK files. For a pipeline-only smoke test,
  set ``env.train.override_cfg.is_dummy: true`` instead.

**``TimeoutError: Timed out waiting for DOSW1 state from AirbotRobot``**
  The gRPC services are not responding within 5 s. Verify:

  - The robot is powered and the follower/leader services are running
    (``sh ~/dos_w1/airbot/whole_start.sh``).
  - ``robot_url`` and the four ports under
    ``cluster.node_groups[<dosw1>].hardware.configs`` match the robot's
    configuration.
  - The GPU node can ``ping`` the robot and TCP-connect on 50050–50053.

**``RuntimeError: DOSW1SDKAdapter is not connected``**
  Connect was never called successfully. Re-check the preceding logs for
  ``[DOSW1SDK] Connecting via AirbotRobot``.

**No cameras / ``Camera ... is not producing frames``**
  Check ``rs-enumerate-devices`` on the robot node. Make sure every serial
  listed under ``cluster.node_groups[<dosw1>].hardware.configs[*].camera_serials``
  is visible, and that the USB cables are seated.
  If you are running on a headless server, set
  ``override_cfg.enable_camera_player: false`` to disable the preview
  window (training is unaffected).

**Reward stays at 0 / phase stuck in ``reach``**
  Most common causes:

  - ``is_dummy`` is still ``true`` in the active config.
  - ``target_grasp_joint`` is unreachable — re-calibrate with
    ``test_dosw1_controller.py`` and confirm the arm physically reaches it.
  - ``joint_reward_sharpness`` is too high (dense reward saturates to 0
    away from the target). Try lowering to ``1.0``.

**``Missing key runner`` / Hydra config errors**
  You are composing from a partial snippet instead of the full template.
  Always start from ``examples/embodiment/config/dosw1_pick_sac_flow.yaml``
  (or ``dosw1_pick_sac_flow_async.yaml``) and override fields in place; for
  quick checks use
  ``tests/e2e_tests/embodied/dosw1_dummy_sac_mlp_pick.yaml``.

**Training is unstable / diverges**
  A few SAC knobs that commonly help:

  - Lower ``actor.optim.lr`` and ``actor.critic_optim.lr`` (e.g. ``1e-4``).
  - Raise ``algorithm.replay_buffer.min_buffer_size`` so training waits
    until the buffer has more on-policy data.
  - Shorten the effective horizon via ``algorithm.gamma`` if the task is
    short (``0.8 – 0.9`` is often a good starting point for pick).
  - Collect a few teleoperated demos and enable RLPD via
    ``algorithm.demo_buffer.load_path``.
