Improving so100 usability (#988)

Improving so100 by:
- Adding dora-rustypot node for rust based servo motor control
- pytorch kinematics for doing forward and inverse kinematics
- add visualization tool for rerun when urdf does not load.

.github/workflows/node-hub-ci-cd.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           sudo apt update
           sudo apt-get install portaudio19-dev
-          sudo apt-get install libdav1d-dev nasm
+          sudo apt-get install libdav1d-dev nasm libudev-dev
           mkdir -p $HOME/.rustup/toolchains/stable-x86_64-unknown-linux-gnu/lib/rustlib/x86_64-unknown-linux-gnu/lib
           ln -s /lib/x86_64-linux-gnu/libdav1d.so $HOME/.rustup/toolchains/stable-x86_64-unknown-linux-gnu/lib/rustlib/x86_64-unknown-linux-gnu/lib/libdav1d.so
 

.github/workflows/node_hub_test.sh

@@ -5,7 +5,7 @@ set -euo
 ignored_folders=("dora-parler" "dora-magma")
 
 # Skip test
-skip_test_folders=("dora-internvl" "dora-parler" "dora-keyboard" "dora-microphone" "terminal-input")
+skip_test_folders=("dora-internvl" "dora-parler" "dora-keyboard" "dora-microphone" "terminal-input" "dora-magma")
 
 # Get current working directory
 dir=$(pwd)

Cargo.toml

@@ -39,6 +39,7 @@ members = [
     "node-hub/dora-mistral-rs",
     "node-hub/dora-rav1e",
     "node-hub/dora-dav1d",
+    "node-hub/dora-rustypot",
     "libraries/extensions/ros2-bridge",
     "libraries/extensions/ros2-bridge/msg-gen",
     "libraries/extensions/ros2-bridge/python",

examples/so100-remote/README.md

@@ -0,0 +1,94 @@
+# SO100 and SO101 Remote Example
+
+## Hardware requirements
+
+- Realsense Camera
+- so101 robotic arm
+
+## Download the 3D model of the SO100
+
+```bash
+[ -f "$HOME/Downloads/so100_urdf.zip" ] || (wget -O "$HOME/Downloads/so100_urdf.zip" https://huggingface.co/datasets/haixuantao/urdfs/resolve/main/so100/so100_urdf.zip && unzip -o "$HOME/Downloads/so100_urdf.zip" -d "$HOME/Downloads/so100_urdf")
+```
+
+## To get started
+
+```bash
+uv venv --seed
+dora build no_torque.yml --uv
+```
+
+## Make sure that both realsense and robotic arm connected
+
+On linux, for the arm you can check connection with:
+
+```bash
+ls /dev/ttyACM*
+```
+
+This should show something like:
+
+```bash
+/dev/ttyACM0
+```
+
+Make sure to enable read with:
+
+```bash
+sudo chmod 777 /dev/ttyACM0
+```
+
+On linux, For the camera, make sure to have it well connected and check with:
+
+```bash
+ls /dev/video**
+```
+
+Result should be as follows:
+
+```bash
+/dev/video0  /dev/video2  /dev/video4  /dev/video6  /dev/video8
+/dev/video1  /dev/video3  /dev/video5  /dev/video7  /dev/video9
+```
+
+## To run the no torque demo:
+
+```bash
+dora run no_torque.yml --uv
+```
+
+If the placement of the virtual robot arm is wrong, you can move it using the so100_transform environment configuration.
+
+## To run the qwenvl demo:
+
+```bash
+dora run qwenvl.yml --uv
+```
+
+## To run the qwenvl remote demo:
+
+On a remote machine:
+
+```bash
+dora coordinator &
+dora daemon --machine-id gpu
+```
+
+```bash
+dora daemon --coordinator-addr <IP_COORDINATOR_ADDR>
+dora start qwenvl-remote.yml --uv --coordinator-addr <IP_COORDINATOR_ADDR>
+```
+
+## To run the qwenvl compression demo:
+
+On a remote machine:
+
+```bash
+dora coordinator &
+dora daemon --machine-id gpu
+```
+
+```bash
+dora daemon --coordinator-addr <IP_COORDINATOR_ADDR>
+dora start qwenvl-compression.yml --uv --coordinator-addr <IP_COORDINATOR_ADDR>
+```

examples/so100-remote/no_torque.yml

@@ -0,0 +1,50 @@
+nodes:
+  - id: so100
+    path: dora-rustypot
+    build: pip install -e ../../node-hub/dora-rustypot
+    inputs:
+      tick: dora/timer/millis/33
+      #pose: pytorch-kinematics/action
+    outputs:
+      - pose
+    env:
+      PORT: /dev/ttyACM0
+      IDS: 1 2 3 4 5 6
+
+  - id: camera
+    build: pip install -e ../../node-hub/dora-pyrealsense
+    path: dora-pyrealsense
+    inputs:
+      tick: dora/timer/millis/33
+    outputs:
+      - image
+      - depth
+
+  - id: pytorch-kinematics
+    build: pip install -e ../../node-hub/dora-pytorch-kinematics
+    path: dora-pytorch-kinematics
+    inputs:
+      pose: so100/pose
+    outputs:
+      - pose
+      - action
+    env:
+      # Link to your installation of so100-urdf.
+      # https://huggingface.co/datasets/haixuantao/urdfs/resolve/main/so100/so100_urdf.zip
+      URDF_PATH: $HOME/Downloads/so100_urdf/so100.urdf
+      END_EFFECTOR_LINK: "Moving Jaw"
+      TRANSFORM: -0.2 -0.01 -0.57 0.7 0 0 0.7
+
+  - id: plot
+    build: pip install -e ../../node-hub/dora-rerun
+    path: dora-rerun
+    inputs:
+      jointstate_so100: so100/pose
+      camera/image: camera/image
+      camera/depth: camera/depth
+    env:
+      # Link to your installation of so100-urdf.
+      # https://huggingface.co/datasets/haixuantao/urdfs/resolve/main/so100/so100_urdf.zip
+      so100_urdf: $HOME/Downloads/so100_urdf/so100.urdf
+      so100_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      CAMERA_PITCH: -3.1415

examples/so100-remote/parse_bbox.py

@@ -0,0 +1,69 @@
+"""TODO: Add docstring."""
+
+import json
+import os
+
+import numpy as np
+import pyarrow as pa
+from dora import Node
+
+node = Node()
+
+IMAGE_RESIZE_RATIO = float(os.getenv("IMAGE_RESIZE_RATIO", "1.0"))
+
+
+def extract_bboxes(json_text):
+    """Extract bounding boxes from a JSON string with markdown markers and return them as a NumPy array.
+
+    Parameters
+    ----------
+    json_text : str
+        JSON string containing bounding box data, including ```json markers.
+
+    Returns
+    -------
+    np.ndarray: NumPy array of bounding boxes.
+
+    """
+    # Ensure all lines are stripped of whitespace and markers
+    lines = json_text.strip().splitlines()
+
+    # Filter out lines that are markdown markers
+    clean_lines = [line for line in lines if not line.strip().startswith("```")]
+
+    # Join the lines back into a single string
+    clean_text = "\n".join(clean_lines)
+    # Parse the cleaned JSON text
+    try:
+        data = json.loads(clean_text)
+
+        # Extract bounding boxes
+        bboxes = [item["bbox_2d"] for item in data]
+        labels = [item["label"] for item in data]
+
+        return np.array(bboxes), np.array(labels)
+    except Exception as _e:  # noqa
+        pass
+    return None, None
+
+
+for event in node:
+    if event["type"] == "INPUT":
+        if len(event["value"]) == 0:
+            node.send_output("bbox_track", pa.array([]))
+            continue
+
+        text = event["value"][0].as_py()
+        metadata = event["metadata"]
+        image_id = event["metadata"]["image_id"]
+
+        bboxes, labels = extract_bboxes(text)
+        if bboxes is not None and len(bboxes) > 0:
+            bboxes = bboxes * int(1 / IMAGE_RESIZE_RATIO)
+            metadata["image_id"] = image_id
+            metadata["encoding"] = "xyxy"
+            node.send_output(
+                "bbox",
+                pa.array(bboxes.ravel()),
+                metadata,
+            )

examples/so100-remote/parse_pose.py

@@ -0,0 +1,161 @@
+"""TODO: Add docstring."""
+
+import time
+import numpy as np
+import pyarrow as pa
+from dora import Node
+
+node = Node()
+top_z = -0.43
+low_z = -0.57
+
+roll = 1.86
+pitch = 1.43
+yaw_open = 0.8
+yaw_close = -0.5
+
+
+def grab(target_x, target_y, low_z, top_z, roll, pitch, yaw_open, yaw_close, last_x, last_y):
+
+    node.send_output(
+        "action",
+        pa.array([target_x, target_y, top_z, roll, pitch, yaw_open]),
+        metadata={"encoding": "xyzrpy"},
+    )
+
+    time.sleep(0.6)
+
+    node.send_output(
+        "action",
+        pa.array([target_x, target_y, low_z, roll, pitch, yaw_open]),
+        metadata={"encoding": "xyzrpy"},
+    )
+    time.sleep(0.2)
+
+
+    node.send_output(
+        "action",
+        pa.array([target_x, target_y, low_z, roll, pitch, yaw_close]),
+        metadata={"encoding": "xyzrpy"},
+    )
+
+    time.sleep(0.4)
+
+    node.send_output(
+        "action",
+        pa.array([target_x, target_y, top_z, roll, pitch, yaw_close]),
+        metadata={"encoding": "xyzrpy"},
+    )
+
+    time.sleep(0.5)
+
+    node.send_output(
+        "action",
+        pa.array([0.05, 0.0, top_z, roll, pitch, yaw_close]),
+        metadata={"encoding": "xyzrpy"},
+    )
+        
+
+def place(place_x, place_y, place_z, top_z, roll, pitch, yaw_open, yaw_close, last_x, last_y):
+
+
+    node.send_output(
+        "action",
+        pa.array([place_x, place_y, top_z, roll, pitch, yaw_close]),
+        metadata={"encoding": "xyzrpy"},
+    )
+
+    time.sleep(0.6)
+
+    node.send_output(
+        "action",
+        pa.array([place_x, place_y, place_z, roll, pitch, yaw_close]),
+        metadata={"encoding": "xyzrpy"},
+    )
+
+    time.sleep(0.2)
+
+
+    node.send_output(
+        "action",
+        pa.array([place_x, place_y, place_z, roll, pitch, yaw_open]),
+        metadata={"encoding": "xyzrpy"},
+    )    
+    
+    time.sleep(0.3)
+
+
+    node.send_output(
+        "action",
+        pa.array([place_x, place_y, top_z, roll, pitch, yaw_open]),
+        metadata={"encoding": "xyzrpy"},
+    )    
+    
+    time.sleep(0.3)
+
+    node.send_output(
+        "action",
+        pa.array([0.05, 0.0, top_z, roll, pitch, yaw_open]),
+        metadata={"encoding": "xyzrpy"},
+    )
+        
+time.sleep(0.6)
+
+
+node.send_output(
+    "action",
+    pa.array([0.05, 0.0, top_z, roll, pitch, yaw_open]),
+    metadata={"encoding": "xyzrpy"},
+)
+
+last_x = 0
+last_y = 0
+last_z = 0
+
+for event in node:
+    if event["type"] == "INPUT":
+        if event["id"] == "pose":
+            values = event["value"]
+            values = values.to_numpy()
+            print(values)
+            if len(values) == 0:
+                continue
+            x = values[0]
+            y = values[1]
+            z = values[2]
+            action = event["metadata"]["action"]
+            
+
+            # Adjust z with the size of the gripper
+            z = z + 0.06
+            match action:
+                case "grab":
+                    y = y + -0.01
+                    grab(
+                        x,
+                        y,
+                        z,
+                        top_z,
+                        roll,
+                        pitch,
+                        yaw_open,
+                        yaw_close,
+                        last_x,
+                        last_y
+                    )
+                case "release":
+                    place(
+                        x,
+                        y,
+                        z,
+                        top_z,
+                        roll,
+                        pitch,
+                        yaw_open,
+                        yaw_close,
+                        last_x,
+                        last_y
+                    )
+            last_x = -0.05
+            last_y = 0.04
+            last_z = z

examples/so100-remote/parse_whisper.py

@@ -0,0 +1,56 @@
+"""TODO: Add docstring."""
+
+import json
+import os
+import time
+
+import numpy as np
+import pyarrow as pa
+from dora import Node
+
+node = Node()
+
+
+last_prompt = ""
+for event in node:
+    if event["type"] == "INPUT":
+        if event["id"] == "text":
+            text = event["value"][0].as_py().lower()
+
+            if "grab " in text:
+                text = f"Given the prompt: {text}. Output the bounding boxes for the given object"
+                node.send_output(
+                    "text", pa.array([text]), {"image_id": "image", "action": "grab"}
+                )
+
+            elif "put " in text:
+                text = f"Given the prompt: {text}. Output the bounding boxes for the place to put the object"
+                node.send_output(
+                    "text",
+                    pa.array([text]),
+                    {"image_id": "image", "action": "release"},
+                )
+
+            elif "make a hot dog" in text:
+                text = f"Given the prompt: grab the sausage. Output the bounding boxes for the given object"
+                node.send_output(
+                    "text", pa.array([text]), {"image_id": "image", "action": "grab"}
+                )
+                time.sleep(4.0)
+
+                text = f"Given the prompt: put it in the black cooking grill. Output the bounding boxes for the given object"
+                node.send_output(
+                    "text", pa.array([text]), {"image_id": "image", "action": "release"}
+                )
+                time.sleep(3.0)
+
+                text = f"Given the prompt: grab the sausage. Output the bounding boxes for the given object"
+                node.send_output(
+                    "text", pa.array([text]), {"image_id": "image", "action": "grab"}
+                )
+                time.sleep(1.6)
+                
+                text = f"Given the prompt: put it in the slice of bread. Output the bounding boxes for the given object"
+                node.send_output(
+                    "text", pa.array([text]), {"image_id": "image", "action": "release"}
+                )

examples/so100-remote/qwenvl-compression.yml

@@ -0,0 +1,180 @@
+nodes:
+  - id: so100
+    path: dora-rustypot
+    inputs:
+      tick: dora/timer/millis/33
+      pose:
+        source: pytorch-kinematics/action
+        queue_size: 100
+    outputs:
+      - pose
+    env:
+      PORT: /dev/ttyACM0
+      TORQUE: 3000
+      IDS: 1 2 3 4 5 6
+
+  - id: camera
+    build: pip install -e ../../node-hub/dora-pyrealsense
+    path: dora-pyrealsense
+    inputs:
+      tick: dora/timer/millis/150
+    outputs:
+      - image
+      - depth
+
+  - id: rav1e-local
+    path: dora-rav1e
+    build: cargo build -p dora-rav1e --release
+    inputs:
+      image: camera/image
+    outputs:
+      - image
+
+  - id: rav1e-depth
+    path: dora-rav1e
+    build: cargo build -p dora-rav1e --release
+    inputs:
+      depth: camera/depth
+    outputs:
+      - depth
+    env:
+      RAV1E_SPEED: 5
+
+  - id: dav1d
+    path: dora-dav1d
+    build: cargo build -p dora-dav1d --release
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      image: rav1e-local/image
+      depth: rav1e-depth/depth
+    outputs:
+      - image
+      - depth
+
+  - id: pytorch-kinematics
+    build: pip install node-hub/dora-pytorch-kinematics
+    path: dora-pytorch-kinematics
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      pose: so100/pose
+      action:
+        source: parse_pose/action
+        queue_size: 100
+    outputs:
+      - pose
+      - action
+    env:
+      URDF_PATH: so100.urdf
+      END_EFFECTOR_LINK: "Moving Jaw"
+      TRANSFORM: -0.2 -0.01 -0.57 0.7 0 0 0.7
+
+  - id: plot
+    build: pip install -e ../../node-hub/dora-rerun
+    path: dora-rerun
+    inputs:
+      #series_so100: so100/pose
+      # series_pose: pytorch-kinematics/pose
+      jointstate_so100: so100/pose
+      camera/image: camera/image
+      camera/depth: camera/depth
+      text_whisper: dora-distil-whisper/text
+      text_vlm: dora-qwenvl/text
+      camera/boxes2d: parse_bbox/bbox
+      camera/masks: sam2/masks
+    env:
+      so100_urdf: $HOME/Downloads/so100_urdf/so100.urdf
+      so100_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      CAMERA_PITCH: -3.1415
+
+  - id: dora-microphone
+    build: pip install node-hub/dora-microphone
+    path: dora-microphone
+    inputs:
+      tick: dora/timer/millis/2000
+    outputs:
+      - audio
+
+  - id: parse_whisper
+    path: parse_whisper.py
+    inputs:
+      text: dora-distil-whisper/text
+    outputs:
+      - text
+    env:
+      SPEED: 1.5
+
+  - id: dora-qwenvl
+    build: pip install node-hub/dora-qwen2-5-vl
+    path: dora-qwen2-5-vl
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      image: dav1d/image
+      text: parse_whisper/text
+    outputs:
+      - text
+    env:
+      DEFAULT_QUESTION: Output the bounding box of the suitcase.
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: parse_bbox
+    path: parse_bbox.py
+    inputs:
+      text: dora-qwenvl/text
+    outputs:
+      - bbox
+    env:
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: sam2
+    build: pip install node-hub/dora-sam2
+    path: dora-sam2
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      image: dav1d/image
+      boxes2d: parse_bbox/bbox
+    outputs:
+      - masks
+
+  - id: box_coordinates
+    build: pip install node-hub/dora-object-to-pose
+    path: dora-object-to-pose
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      depth: dav1d/depth
+      masks: sam2/masks
+    outputs:
+      - pose
+    env:
+      CAMERA_PITCH: -3.1415
+
+  - id: parse_pose
+    path: parse_pose.py
+    inputs:
+      pose: box_coordinates/pose
+    outputs:
+      - action
+
+  - id: dora-vad
+    build: pip install node-hub/dora-vad
+    path: dora-vad
+    inputs:
+      audio: dora-microphone/audio
+    outputs:
+      - audio
+
+  - id: dora-distil-whisper
+    build: pip install node-hub/dora-distil-whisper
+    path: dora-distil-whisper
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      input: dora-vad/audio
+    outputs:
+      - text
+    env:
+      TARGET_LANGUAGE: english

examples/so100-remote/qwenvl-remote.yml

@@ -0,0 +1,153 @@
+nodes:
+  - id: so100
+    path: dora-rustypot
+    inputs:
+      tick: dora/timer/millis/33
+      pose:
+        source: pytorch-kinematics/action
+        queue_size: 100
+    outputs:
+      - pose
+    env:
+      PORT: /dev/ttyACM0
+      TORQUE: 2000
+      IDS: 1 2 3 4 5 6
+
+  - id: camera
+    build: pip install -e ../../node-hub/dora-pyrealsense
+    path: dora-pyrealsense
+    inputs:
+      tick: dora/timer/millis/100
+    outputs:
+      - image
+      - depth
+
+  - id: pytorch-kinematics
+    build: pip install node-hub/dora-pytorch-kinematics
+    path: dora-pytorch-kinematics
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      pose: so100/pose
+      action:
+        source: parse_pose/action
+        queue_size: 100
+    outputs:
+      - pose
+      - action
+    env:
+      URDF_PATH: so100.urdf
+      END_EFFECTOR_LINK: "Moving Jaw"
+      TRANSFORM: -0.2 -0.01 -0.57 0.7 0 0 0.7
+
+  - id: plot
+    build: pip install -e ../../node-hub/dora-rerun
+    path: dora-rerun
+    inputs:
+      #series_so100: so100/pose
+      # series_pose: pytorch-kinematics/pose
+      jointstate_so100: so100/pose
+      camera/image: camera/image
+      camera/depth: camera/depth
+      text_whisper: dora-distil-whisper/text
+      text_vlm: dora-qwenvl/text
+      camera/boxes2d: parse_bbox/bbox
+      camera/masks: sam2/masks
+    env:
+      # Link to your installation of so100-urdf.
+      # https://huggingface.co/datasets/haixuantao/urdfs/resolve/main/so100/so100_urdf.zip
+      so100_urdf: $HOME/Downloads/so100_urdf/so100.urdf
+      so100_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      so100_inference_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      CAMERA_PITCH: -3.1415
+
+  - id: dora-microphone
+    build: pip install node-hub/dora-microphone
+    path: dora-microphone
+    inputs:
+      tick: dora/timer/millis/2000
+    outputs:
+      - audio
+
+  - id: parse_whisper
+    path: parse_whisper.py
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      text: dora-distil-whisper/text
+    outputs:
+      - text
+
+  - id: dora-qwenvl
+    build: pip install node-hub/dora-qwen2-5-vl
+    path: dora-qwen2-5-vl
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      image: camera/image
+      text: parse_whisper/text
+    outputs:
+      - text
+    env:
+      DEFAULT_QUESTION: Output the bounding box of the suitcase.
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: parse_bbox
+    path: parse_bbox.py
+    inputs:
+      text: dora-qwenvl/text
+    outputs:
+      - bbox
+    env:
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: sam2
+    build: pip install node-hub/dora-sam2
+    path: dora-sam2
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      image: camera/image
+      boxes2d: parse_bbox/bbox
+    outputs:
+      - masks
+
+  - id: box_coordinates
+    build: pip install node-hub/dora-object-to-pose
+    path: dora-object-to-pose
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      depth: camera/depth
+      masks: sam2/masks
+    outputs:
+      - pose
+    env:
+      CAMERA_PITCH: -3.1415
+
+  - id: parse_pose
+    path: parse_pose.py
+    inputs:
+      pose: box_coordinates/pose
+    outputs:
+      - action
+
+  - id: dora-vad
+    build: pip install node-hub/dora-vad
+    path: dora-vad
+    inputs:
+      audio: dora-microphone/audio
+    outputs:
+      - audio
+
+  - id: dora-distil-whisper
+    build: pip install node-hub/dora-distil-whisper
+    path: dora-distil-whisper
+    _unstable_deploy:
+      machine: gpu
+    inputs:
+      input: dora-vad/audio
+    outputs:
+      - text
+    env:
+      TARGET_LANGUAGE: english

examples/so100-remote/qwenvl.yml

@@ -0,0 +1,142 @@
+nodes:
+  - id: so100
+    path: dora-rustypot
+    inputs:
+      tick: dora/timer/millis/33
+      pose:
+        source: pytorch-kinematics/action
+        queue_size: 100
+    outputs:
+      - pose
+    env:
+      PORT: /dev/ttyACM0
+      TORQUE: 5000
+      IDS: 1 2 3 4 5 6
+
+  - id: camera
+    build: pip install -e ../../node-hub/dora-pyrealsense
+    path: dora-pyrealsense
+    inputs:
+      tick: dora/timer/millis/33
+    outputs:
+      - image
+      - depth
+
+  - id: pytorch-kinematics
+    build: pip install -e ../../node-hub/dora-pytorch-kinematics
+    path: dora-pytorch-kinematics
+    inputs:
+      pose: so100/pose
+      action:
+        source: parse_pose/action
+        queue_size: 100
+    outputs:
+      - pose
+      - action
+    env:
+      # Link to your installation of so100-urdf.
+      # https://huggingface.co/datasets/haixuantao/urdfs/resolve/main/so100/so100_urdf.zip
+      URDF_PATH: $HOME/Downloads/so100_urdf/so100.urdf
+      END_EFFECTOR_LINK: "Moving Jaw"
+      TRANSFORM: -0.2 -0.01 -0.57 0.7 0 0 0.7
+
+  - id: plot
+    build: pip install -e ../../node-hub/dora-rerun
+    path: dora-rerun
+    inputs:
+      #series_so100: so100/pose
+      # series_pose: pytorch-kinematics/pose
+      jointstate_so100: so100/pose
+      jointstate_so100_inference: pytorch-kinematics/action
+      camera/image: camera/image
+      camera/depth: camera/depth
+      text_whisper: dora-distil-whisper/text
+      text_vlm: dora-qwenvl/text
+      camera/boxes2d: parse_bbox/bbox
+      camera/masks: sam2/masks
+    env:
+      so100_urdf: $HOME/Downloads/so100_urdf/so100.urdf
+      so100_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      so100_inference_transform: -0.2 -0.01 -0.57 0.7 0 0 0.7
+      CAMERA_PITCH: -3.1415
+
+  - id: dora-microphone
+    build: pip install -e ../../node-hub/dora-microphone
+    path: dora-microphone
+    inputs:
+      tick: dora/timer/millis/2000
+    outputs:
+      - audio
+
+  - id: parse_whisper
+    path: parse_whisper.py
+    inputs:
+      text: dora-distil-whisper/text
+    outputs:
+      - text
+
+  - id: dora-qwenvl
+    build: pip install -e ../../node-hub/dora-qwen2-5-vl
+    path: dora-qwen2-5-vl
+    inputs:
+      image: camera/image
+      text: parse_whisper/text
+    outputs:
+      - text
+    env:
+      DEFAULT_QUESTION: Output the bounding box of the suitcase.
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: parse_bbox
+    path: parse_bbox.py
+    inputs:
+      text: dora-qwenvl/text
+    outputs:
+      - bbox
+    env:
+      IMAGE_RESIZE_RATIO: "1.0"
+
+  - id: sam2
+    build: pip install -e ../../node-hub/dora-sam2
+    path: dora-sam2
+    inputs:
+      image: camera/image
+      boxes2d: parse_bbox/bbox
+    outputs:
+      - masks
+
+  - id: box_coordinates
+    build: pip install -e ../../node-hub/dora-object-to-pose
+    path: dora-object-to-pose
+    inputs:
+      depth: camera/depth
+      masks: sam2/masks
+    outputs:
+      - pose
+    env:
+      CAMERA_PITCH: -3.1415
+
+  - id: parse_pose
+    path: parse_pose.py
+    inputs:
+      pose: box_coordinates/pose
+    outputs:
+      - action
+
+  - id: dora-vad
+    build: pip install -e ../../node-hub/dora-vad
+    path: dora-vad
+    inputs:
+      audio: dora-microphone/audio
+    outputs:
+      - audio
+
+  - id: dora-distil-whisper
+    build: pip install -e ../../node-hub/dora-distil-whisper
+    path: dora-distil-whisper
+    inputs:
+      input: dora-vad/audio
+    outputs:
+      - text
+    env:
+      TARGET_LANGUAGE: english

node-hub/dora-object-to-pose/src/lib.rs

@@ -14,7 +14,7 @@ fn points_to_pose(points: &[(f32, f32, f32)]) -> Vec<f32> {
     let (
         _sum_x,
         _sum_y,
-        _sum_z,
+        sum_z,
         sum_xy,
         sum_x2,
         sum_y2,
@@ -23,8 +23,8 @@ fn points_to_pose(points: &[(f32, f32, f32)]) -> Vec<f32> {
         x_max,
         y_min,
         y_max,
-        z_min,
-        z_max,
+        _z_min,
+        _z_max,
     ) = points.iter().fold(
         (
             0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0, -10.0, 10.0, -10.0, 10., -10.0,
@@ -62,11 +62,7 @@ fn points_to_pose(points: &[(f32, f32, f32)]) -> Vec<f32> {
             )
         },
     );
-    let (mean_x, mean_y, mean_z) = (
-        (x_max + x_min) / 2.,
-        (y_max + y_min) / 2.,
-        (z_max + z_min) / 2.,
-    );
+    let (mean_x, mean_y, mean_z) = ((x_max + x_min) / 2., (y_max + y_min) / 2., sum_z / n);
 
     // Compute covariance and standard deviations
     let cov = sum_xy / n - mean_x * mean_y;

node-hub/dora-pytorch-kinematics/README.md

@@ -0,0 +1,40 @@
+# dora-pytorch-kinematics
+
+## Getting started
+
+- Install it with uv:
+
+```bash
+uv venv -p 3.11 --seed
+uv pip install -e .
+```
+
+## Contribution Guide
+
+- Format with [ruff](https://docs.astral.sh/ruff/):
+
+```bash
+uv pip install ruff
+uv run ruff check . --fix
+```
+
+- Lint with ruff:
+
+```bash
+uv run ruff check .
+```
+
+- Test with [pytest](https://github.com/pytest-dev/pytest)
+
+```bash
+uv pip install pytest
+uv run pytest . # Test
+```
+
+## YAML Specification
+
+## Examples
+
+## License
+
+dora-pytorch-kinematics's code are released under the MIT License

node-hub/dora-pytorch-kinematics/dora_pytorch_kinematics/__init__.py

@@ -0,0 +1,13 @@
+"""TODO: Add docstring."""
+
+import os
+
+# Define the path to the README file relative to the package directory
+readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.md")
+
+# Read the content of the README file
+try:
+    with open(readme_path, encoding="utf-8") as f:
+        __doc__ = f.read()
+except FileNotFoundError:
+    __doc__ = "README file not found."

node-hub/dora-pytorch-kinematics/dora_pytorch_kinematics/__main__.py

@@ -0,0 +1,6 @@
+"""TODO: Add docstring."""
+
+from .main import main
+
+if __name__ == "__main__":
+    main()

node-hub/dora-pytorch-kinematics/dora_pytorch_kinematics/main.py

@@ -0,0 +1,299 @@
+"""TODO: Add docstring."""
+
+import os
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import pyarrow as pa
+import pytorch_kinematics as pk
+import torch
+from dora import Node
+from pytorch_kinematics.transforms.rotation_conversions import matrix_to_euler_angles
+
+TRANSFORM = np.array(os.getenv("TRANSFORM", "0. 0. 0. 0. 0. 0. 1.").split(" ")).astype(
+    np.float32,
+)
+pos = torch.tensor([TRANSFORM[0], TRANSFORM[1], TRANSFORM[2]])
+rot = torch.tensor(
+    [
+        TRANSFORM[3],
+        TRANSFORM[4],
+        TRANSFORM[5],
+        TRANSFORM[6],
+    ],
+)
+ROB_TF = pk.Transform3d(pos=pos, rot=rot)
+
+
+def get_xyz_rpy_array_from_transform3d(
+    transform: "pk.transforms.Transform3d", convention: str = "XYZ",
+) -> torch.Tensor:
+    """XYZ-RPY conversion.
+    
+    Extract translation (xyz) and rotation (RPY Euler angles in radians)
+    from a pytorch_kinematics Transform3d object and returns them concatenated
+    into a single tensor.
+
+    Args:
+        transform: A Transform3d object or a batch of Transform3d objects.
+                    Expected to have a method like get_matrix() or directly
+                    access attributes like .R and .T.
+        convention: The Euler angle convention (e.g., "XYZ", "ZYX").
+                    RPY typically corresponds to "XYZ" (roll around X,
+                    pitch around Y, yaw around Z). Adjust if needed.
+
+    Returns:
+        A single tensor of shape (..., 6) where the last dimension contains
+        [x, y, z, roll, pitch, yaw] in radians.
+
+    """
+    # Attempt to get the full 4x4 transformation matrix
+    # Common methods are get_matrix() or accessing internal properties.
+    # This might need adjustment based on the specific API version.
+    matrix = transform.get_matrix()  # Shape (..., 4, 4)
+
+    # Extract translation (first 3 elements of the last column)
+    xyz = matrix[..., :3, 3]
+
+    # Extract rotation matrix (top-left 3x3 submatrix)
+    rotation_matrix = matrix[..., :3, :3]
+
+    # Convert the rotation matrix to Euler angles in radians
+    # The matrix_to_euler_angles function likely exists based on pytorch3d's structure
+    rpy = matrix_to_euler_angles(
+        rotation_matrix, convention=convention,
+    )  # Shape (..., 3)
+
+    # Concatenate xyz and rpy along the last dimension
+    return torch.cat((xyz, rpy), dim=-1)  # Shape (..., 6)
+
+
+class RobotKinematics:
+    """wrapper for pytorch_kinematics FK and IK operations."""
+
+    def __init__(
+        self,
+        urdf_path: str,
+        end_effector_link: str,
+        device: Union[str, torch.device] = "cpu",
+    ):
+        """Initialize the kinematic chain from a URDF.
+
+        Args:
+            urdf_path (str): Path to the URDF file.
+            end_effector_link (str): Name of the end-effector link.
+            device (Union[str, torch.device]): Computation device ('cpu' or 'cuda').
+
+        """
+        self.device = torch.device(device)
+        try:
+            # Load kinematic chain (core pytorch_kinematics object)
+            self.chain = pk.build_serial_chain_from_urdf(
+                open(urdf_path, mode="rb").read(), end_effector_link,
+            ).to(device=self.device)
+        except Exception as e:
+            raise RuntimeError(f"Failed to build chain from URDF: {urdf_path}") from e
+
+        self.num_joints = len(self.chain.get_joint_parameter_names(exclude_fixed=True))
+        # Default initial guess for IK if none provided
+        self._default_q = torch.zeros(
+            (1, self.num_joints), device=self.device, dtype=torch.float32,
+        )
+        # print(f"Initialized RobotKinematicsConcise: {self.num_joints} joints, EE='{end_effector_link}', device='{self.device}'") # Optional print
+
+    def _prepare_joint_tensor(
+        self, joints: Union[List[float], torch.Tensor], batch_dim_required: bool = True,
+    ) -> torch.Tensor:
+        """Validate and formats joint angles input tensor."""
+        if isinstance(joints, list):
+            j = torch.tensor(joints, dtype=torch.float32, device=self.device)
+        elif isinstance(joints, np.ndarray):
+            j = torch.tensor(joints, device=self.device, dtype=torch.float32)
+        elif isinstance(joints, torch.Tensor):
+            j = joints.to(device=self.device, dtype=torch.float32)
+        else:
+            raise TypeError(
+                "Joints must be a list or torch.Tensor and got: ", joints.type,
+            )
+
+        if j.ndim == 1:
+            if j.shape[0] != self.num_joints:
+                raise ValueError(f"Expected {self.num_joints} joints, got {j.shape[0]}")
+            if batch_dim_required:
+                j = j.unsqueeze(0)  # Add batch dimension if needed (e.g., shape (1, N))
+        elif j.ndim == 2:
+            if j.shape[1] != self.num_joints:
+                raise ValueError(
+                    f"Expected {self.num_joints} joints (dim 1), got {j.shape[1]}",
+                )
+            if batch_dim_required and j.shape[0] != 1:
+                # Most common IK solvers expect batch=1 for initial guess, FK can handle batches
+                # Relaxing this check slightly, but user should be aware for IK
+                pass
+        else:
+            raise ValueError(f"Joint tensor must have 1 or 2 dimensions, got {j.ndim}")
+        return j
+
+    def compute_fk(
+        self, joint_angles: Union[List[float], torch.Tensor],
+    ) -> pk.Transform3d:
+        """Compute Forward Kinematics (FK).
+
+        Args:
+            joint_angles (Union[List[float], torch.Tensor]): Joint angles (radians).
+                Shape (num_joints,) or (B, num_joints).
+
+        Returns:
+            pk.Transform3d: End-effector pose(s). Batched if input is batched.
+
+        """
+        # robot frame
+
+        angles_tensor = self._prepare_joint_tensor(
+            joint_angles, batch_dim_required=False,
+        )  # FK handles batches naturally
+        # Direct call to pytorch_kinematics FK
+        goal_in_rob_frame_tf = self.chain.forward_kinematics(
+            angles_tensor, end_only=True,
+        )
+        goal_tf = ROB_TF.compose(goal_in_rob_frame_tf)
+        return goal_tf
+
+    def compute_ik(
+        self,
+        target_pose: pk.Transform3d,
+        initial_guess: Optional[Union[List[float], torch.Tensor]] = None,
+        iterations: int = 100,
+        lr: float = 0.1,
+        error_tolerance: float = 1e-4,
+    ) -> Tuple[torch.Tensor, bool]:
+        """Compute Inverse Kinematics (IK) using PseudoInverseIK.
+
+        Args:
+            target_pose (pk.Transform3d): Target end-effector pose (batch size 1).
+            initial_guess (Optional): Initial joint angles guess. Uses zeros if None.
+            Shape (num_joints,) or (1, num_joints).
+            iterations (int): Max solver iterations.
+            lr (float): Solver learning rate.
+            error_tolerance (float): Solver convergence tolerance.
+
+        Returns:
+            Tuple[torch.Tensor, bool]: Solution joint angles (1, num_joints), convergence status.
+
+        """
+        if not isinstance(target_pose, pk.Transform3d):
+            raise TypeError("target_pose must be a pk.Transform3d")
+        target_pose = target_pose.to(device=self.device)
+        if target_pose.get_matrix().shape[0] != 1:
+            raise ValueError(
+                "target_pose must have batch size 1 for this IK method.",
+            )  # Common limitation
+
+        # Prepare initial guess (q_init)
+        q_init = self._prepare_joint_tensor(
+            initial_guess if initial_guess is not None else self._default_q,
+            batch_dim_required=True,
+        )
+        if q_init.shape[0] != 1:
+            raise ValueError(
+                "initial_guess must result in batch size 1 for this IK setup.",
+            )  # Enforce batch=1 for guess
+
+        q_init = q_init.requires_grad_(True)  # Need gradient for solver
+
+        # Instantiate and run the IK solver (core pytorch_kinematics objects/methods)
+        ik_solver = pk.PseudoInverseIK(
+            self.chain,
+            max_iterations=1_000,
+            retry_configs=q_init,
+            joint_limits=torch.tensor(self.chain.get_joint_limits()),
+            early_stopping_any_converged=True,
+            early_stopping_no_improvement="all",
+            debug=False,
+            lr=0.05,
+            rot_tolerance=1e-4,
+            pos_tolerance=1e-3,
+        )
+        solution_angles = ik_solver.solve(target_pose)
+        if solution_angles.err_rot > 1e-3 or solution_angles.err_pos > 1e-2:
+            print(
+                f"IK did not converge: pos_err={solution_angles.err_pos}, rot_err={solution_angles.err_rot} for target {target_pose}",
+            )
+            return None
+        return solution_angles.solutions.detach()
+
+
+def main():
+    """TODO: Add docstring."""
+    node = Node()
+
+    urdf_path = os.getenv("URDF_PATH")
+    end_effector_link = os.getenv("END_EFFECTOR_LINK")
+    robot = RobotKinematics(urdf_path, end_effector_link)
+    last_known_state = None
+
+    for event in node:
+        if event["type"] == "INPUT":
+            metadata = event["metadata"]
+
+            match metadata["encoding"]:
+                case "xyzquat":
+                    # Apply Inverse Kinematics
+                    if last_known_state is not None:
+                        target = event["value"].to_numpy()
+                        target = target.astype(np.float32)
+                        target = pk.Transform3d(
+                            pos=target[:3], rot=torch.tensor(target[3:7]),
+                        )
+                        solution = robot.compute_ik(target, last_known_state)
+                        metadata["encoding"] = "jointstate"
+                        last_known_state = solution.numpy().ravel()
+                        solution = pa.array(last_known_state)
+                        node.send_output(event["id"], solution, metadata=metadata)
+                case "xyzrpy":
+                    # Apply Inverse Kinematics
+                    if last_known_state is not None:
+                        target = event["value"].to_numpy()
+                        target = target.astype(np.float32)
+                        target = pk.Transform3d(
+                            pos=target[:3],
+                            rot=pk.transforms.euler_angles_to_matrix(
+                                torch.tensor(target[3:6]), convention="XYZ",
+                            ),
+                        )
+                        rob_target = ROB_TF.inverse().compose(target)
+                        solution = robot.compute_ik(rob_target, last_known_state)
+                        if solution is None:
+                            print(
+                                "No IK Solution for :", target, "skipping this frame.",
+                            )
+                            continue
+
+                        solution = solution.numpy().ravel()
+                        delta_angles = solution - last_known_state
+
+                        valid = np.all(
+                            (delta_angles >= -np.pi) & (delta_angles <= np.pi),
+                        )
+                        if not valid:
+                            print(
+                                "IK solution is not valid, as the rotation are too wide. skipping.",
+                            )
+                            continue
+                        metadata["encoding"] = "jointstate"
+                        last_known_state = solution
+                        solution = pa.array(last_known_state)
+                        node.send_output(event["id"], solution, metadata=metadata)
+                case "jointstate":
+                    target = event["value"].to_numpy()
+                    last_known_state = target
+                    # Apply Forward Kinematics
+                    target = robot.compute_fk(target)
+                    target = np.array(get_xyz_rpy_array_from_transform3d(target))
+                    target = pa.array(target.ravel(), type=pa.float32())
+                    metadata["encoding"] = "xyzrpy"
+                    node.send_output(event["id"], target, metadata=metadata)
+
+
+if __name__ == "__main__":
+    main()

node-hub/dora-pytorch-kinematics/pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "dora-pytorch-kinematics"
+version = "0.0.0"
+authors = [{ name = "Your Name", email = "email@email.com" }]
+description = "dora-pytorch-kinematics"
+license = { text = "MIT" }
+readme = "README.md"
+requires-python = ">=3.8"
+
+dependencies = [
+    "dora-rs >= 0.3.9",
+    "pytorch-kinematics>=0.7.5",
+]
+
+[dependency-groups]
+dev = ["pytest >=8.1.1", "ruff >=0.9.1"]
+
+[project.scripts]
+dora-pytorch-kinematics = "dora_pytorch_kinematics.main:main"
+
+[tool.ruff.lint]
+extend-select = [
+  "D",   # pydocstyle
+  "UP"
+]

node-hub/dora-pytorch-kinematics/tests/test_dora_pytorch_kinematics.py

@@ -0,0 +1,13 @@
+"""Test module for dora_pytorch_kinematics package."""
+
+import pytest
+
+
+def test_import_main():
+    """Test importing and running the main function."""
+    from dora_pytorch_kinematics.main import main
+
+    # Check that everything is working, and catch Dora RuntimeError
+    # as we're not running in a Dora dataflow.
+    with pytest.raises(RuntimeError):
+        main()

node-hub/dora-rerun/Cargo.toml

@@ -17,9 +17,8 @@ python = ["pyo3"]
 dora-node-api = { workspace = true, features = ["tracing"] }
 eyre = "0.6.8"
 tokio = { version = "1.24.2", features = ["rt"] }
-rerun = { version = "0.22.0", features = ["web_viewer", "image"] }
+rerun = { version = "0.23.1", features = ["web_viewer", "image"] }
 ndarray = "0.15.6"
-chrono = "=0.4.39"
 k = "0.32"
 pyo3 = { workspace = true, features = [
     "extension-module",

node-hub/dora-rerun/pyproject.toml

@@ -6,12 +6,9 @@ build-backend = "maturin"
 name = "dora-rerun"
 dynamic = ["version"]
 license = { text = "MIT" }
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 
-dependencies = [
-  'rerun_sdk==0.22.0',
-  # "rerun-loader-urdf @ git+https://github.com/rerun-io/rerun-loader-python-example-urdf.git",
-]
+dependencies = ["rerun-loader-urdf", 'rerun_sdk==0.23.1']
 
 scripts = { "dora-rerun" = "dora_rerun:py_main" }
 
@@ -29,3 +26,6 @@ extend-select = [
   "N",    # Ruff's N rule
   "I",    # Ruff's I rule
 ]
+
+[tool.uv.sources]
+rerun-loader-urdf = { git = "https://github.com/haixuanTao/rerun-loader-python-example-urdf.git", branch = "patch-2" }

node-hub/dora-rerun/src/lib.rs

@@ -3,18 +3,16 @@
 use std::{collections::HashMap, env::VarError, path::Path};
 
 use dora_node_api::{
-    arrow::array::{Array, Float32Array, Float64Array, StringArray, UInt16Array, UInt8Array},
-    arrow::{array::AsArray, datatypes::Float32Type},
+    arrow::{
+        array::{Array, AsArray, Float64Array, StringArray, UInt16Array, UInt8Array},
+        datatypes::Float32Type,
+    },
     dora_core::config::DataId,
-    DoraNode, Event, Parameter,
+    into_vec, DoraNode, Event, Parameter,
 };
-use eyre::{eyre, Context, ContextCompat, Result};
+use eyre::{eyre, Context, Result};
 
-use rerun::{
-    components::ImageBuffer,
-    external::{log::warn, re_types::ArrowBuffer},
-    ImageFormat, Points3D, SpawnOptions,
-};
+use rerun::{components::ImageBuffer, external::log::warn, ImageFormat, Points3D, SpawnOptions};
 pub mod boxes2d;
 pub mod series;
 pub mod urdf;
@@ -54,7 +52,7 @@ pub fn lib_main() -> Result<()> {
             let opt = std::env::var("RERUN_SERVER_ADDR").unwrap_or("127.0.0.1:9876".to_string());
 
             rerun::RecordingStreamBuilder::new("dora-rerun")
-                .connect_tcp_opts(std::net::SocketAddr::V4(opt.parse()?), None)
+                .connect_grpc_opts(opt, None)
                 .context("Could not connect to rerun visualization")?
         }
         Ok("SAVE") => {
@@ -130,7 +128,6 @@ pub fn lib_main() -> Result<()> {
                     let buffer: Vec<u8> =
                         buffer.chunks(3).flat_map(|x| [x[2], x[1], x[0]]).collect();
                     image_cache.insert(id.clone(), buffer.clone());
-                    let buffer = ArrowBuffer::from(buffer);
                     let image_buffer = ImageBuffer::try_from(buffer)
                         .context("Could not convert buffer to image buffer")?;
                     // let tensordata = ImageBuffer(buffer);
@@ -145,7 +142,6 @@ pub fn lib_main() -> Result<()> {
                     let buffer: &UInt8Array = data.as_any().downcast_ref().unwrap();
                     image_cache.insert(id.clone(), buffer.values().to_vec());
                     let buffer: &[u8] = buffer.values();
-                    let buffer = ArrowBuffer::from(buffer);
                     let image_buffer = ImageBuffer::try_from(buffer)
                         .context("Could not convert buffer to image buffer")?;
 
@@ -315,12 +311,21 @@ pub fn lib_main() -> Result<()> {
                     continue;
                 };
                 mask_cache.insert(id.clone(), masks.clone());
-            } else if id.as_str().contains("jointstate") {
-                let buffer: &Float32Array = data
-                    .as_any()
-                    .downcast_ref()
-                    .context("jointstate is not float32")?;
-                let mut positions: Vec<f32> = buffer.values().to_vec();
+            } else if id.as_str().contains("jointstate") || id.as_str().contains("pose") {
+                let encoding = if let Some(Parameter::String(encoding)) =
+                    metadata.parameters.get("encoding")
+                {
+                    encoding
+                } else {
+                    "jointstate"
+                };
+                if encoding != "jointstate" {
+                    warn!("Got unexpected encoding: {} on position pose", encoding);
+                    continue;
+                }
+                // Convert to Vec<f32>
+                let mut positions: Vec<f32> =
+                    into_vec(&data).context("Could not parse jointstate as vec32")?;
 
                 // Match file name
                 let mut id = id.as_str().replace("jointstate_", "");

node-hub/dora-rerun/src/urdf.rs

@@ -1,8 +1,8 @@
-use std::collections::HashMap;
+use std::{collections::HashMap, path::PathBuf};
 
 use eyre::{Context, ContextCompat, Result};
-use k::Chain;
-use rerun::{components::RotationAxisAngle, Angle, RecordingStream, Rotation3D, Vec3D};
+use k::{nalgebra::Quaternion, Chain, Translation3, UnitQuaternion};
+use rerun::{RecordingStream, Vec3D};
 pub struct MyIntersperse<T, I> {
     iterator: I,
     sep: T,
@@ -56,14 +56,20 @@ fn get_entity_path(link: &k::Node<f32>, urdf_path: &str) -> String {
 pub fn init_urdf(rec: &RecordingStream) -> Result<HashMap<String, Chain<f32>>> {
     // Get all env variable that end with urdf
     let urdfs = std::env::vars()
-        .filter(|(key, _)| key.ends_with("_urdf"))
+        .filter(|(key, _)| key.ends_with("_urdf") || key.ends_with("_URDF"))
         .collect::<Vec<_>>();
     let mut chains = HashMap::new();
     for (key, urdf_path) in urdfs {
-        let path = key.replace("_urdf", ".urdf");
+        let path = key.replace("_urdf", ".urdf").replace("_URDF", ".urdf");
         let chain = k::Chain::<f32>::from_urdf_file(&urdf_path).context("Could not load URDF")?;
 
         let transform = key.replace("_urdf", "_transform");
+
+        if PathBuf::from(&urdf_path).file_name() != PathBuf::from(&path).file_name() {
+            return Err(eyre::eyre!(
+                "URDF path should be the same as the key without _urdf or _URDF. Got {} instead of {}", urdf_path, path
+            ));
+        }
         if let Err(err) = rec.log_file_from_path(&urdf_path, None, false) {
             println!("Could not log file: {}. Errored with {}", urdf_path, err);
             println!("Make sure to install urdf loader with:");
@@ -71,23 +77,29 @@ pub fn init_urdf(rec: &RecordingStream) -> Result<HashMap<String, Chain<f32>>> {
                 "pip install git+https://github.com/rerun-io/rerun-loader-python-example-urdf.git"
             )
         };
+
         // Get transform by replacing URDF_ with TRANSFORM_
         if let Ok(transform) = std::env::var(transform) {
             let transform = transform
                 .split(' ')
                 .map(|x| x.parse::<f32>().unwrap())
                 .collect::<Vec<f32>>();
-            rec.log(
-                path.clone(),
-                &rerun::Transform3D::from_translation_rotation(
-                    [transform[0], transform[1], transform[2]],
-                    Rotation3D::AxisAngle(RotationAxisAngle::new(
-                        [0., 0., 0.],
-                        Angle::from_degrees(0.0),
-                    )),
-                ),
-            )
-            .unwrap();
+
+            let mut pose = chain.origin();
+
+            if transform.len() == 7 {
+                let quaternion =
+                    Quaternion::new(transform[3], transform[4], transform[5], transform[6])
+                        .normalize(); // Example quaternion
+                let rot = UnitQuaternion::from_quaternion(quaternion);
+                pose.append_rotation_mut(&rot);
+            }
+            pose.append_translation_mut(&Translation3::new(
+                transform[0],
+                transform[1],
+                transform[2],
+            ));
+            chain.set_origin(pose);
             chains.insert(path, chain);
         }
     }
@@ -126,14 +138,13 @@ pub fn update_visualization(
         let trans = link_to_parent_mat.column(3);
         let trans = trans.as_slice();
         let quat = link_to_parent.rotation.quaternion();
-        rec.log(
-            entity_path,
-            &rerun::Transform3D::from_translation_rotation(
-                Vec3D::new(trans[0], trans[1], trans[2]),
-                rerun::Quaternion::from([quat.i, quat.j, quat.k, quat.w]),
-            ),
-        )
-        .context("Could not log transform")?;
+        let point_transform = rerun::Transform3D::from_translation_rotation(
+            Vec3D::new(trans[0], trans[1], trans[2]),
+            rerun::Quaternion::from([quat.i, quat.j, quat.k, quat.w]),
+        );
+        rec.log(entity_path.clone(), &point_transform)
+            .context("Could not log transform")?;
     }
+
     Ok(())
 }

node-hub/dora-rustypot/Cargo.toml

@@ -0,0 +1,28 @@
+[package]
+name = "dora-rustypot"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+dora-node-api = { workspace = true }
+eyre = "0.6.12"
+rustypot = { version = "1.0" }
+serialport = { version = "4.7.1", default-features = false }
+pyo3 = { workspace = true, features = [
+    "extension-module",
+    "abi3",
+    "eyre",
+    "generate-import-lib",
+], optional = true }
+
+
+[features]
+default = []
+python = ["pyo3"]
+
+[lib]
+name = "dora_rustypot"
+path = "src/lib.rs"
+crate-type = ["lib", "cdylib"]

node-hub/dora-rustypot/pyproject.toml

@@ -0,0 +1,28 @@
+[build-system]
+requires = ["maturin>=0.13.2"]
+build-backend = "maturin"
+
+[project]
+name = "dora-rustypot"
+dynamic = ["version"]
+license = { text = "MIT" }
+requires-python = ">=3.10"
+
+dependencies = []
+
+scripts = { "dora-rustypot" = "dora_rustypot:py_main" }
+
+[tool.maturin]
+features = ["python", "pyo3/extension-module"]
+
+[tool.ruff.lint]
+extend-select = [
+  "D",    # pydocstyle
+  "UP",   # Ruff's UP rule
+  "PERF", # Ruff's PERF rule
+  "RET",  # Ruff's RET rule
+  "RSE",  # Ruff's RSE rule
+  "NPY",  # Ruff's NPY rule
+  "N",    # Ruff's N rule
+  "I",    # Ruff's I rule
+]

node-hub/dora-rustypot/src/lib.rs

@@ -0,0 +1,101 @@
+use dora_node_api::dora_core::config::DataId;
+use dora_node_api::{into_vec, DoraNode, Event, IntoArrow, Parameter};
+use eyre::{Context, Result};
+use rustypot::servo::feetech::sts3215::Sts3215Controller;
+use std::collections::BTreeMap;
+use std::time::Duration;
+
+pub fn lib_main() -> Result<()> {
+    let (mut node, mut events) = DoraNode::init_from_env()?;
+    let serialportname: String = std::env::var("PORT").context("Serial port name not provided")?;
+    let baudrate: u32 = std::env::var("BAUDRATE")
+        .unwrap_or_else(|_| "1000000".to_string())
+        .parse()
+        .context("Invalid baudrate")?;
+    let ids = std::env::var("IDS")
+        .unwrap_or_else(|_| "1,2,3,4,5,6".to_string())
+        .split(&[',', ' '][..])
+        .map(|s| s.parse::<u8>().unwrap())
+        .collect::<Vec<u8>>();
+
+    let serial_port = serialport::new(serialportname, baudrate)
+        .timeout(Duration::from_millis(1000))
+        .open()?;
+
+    let mut c = Sts3215Controller::new()
+        .with_protocol_v1()
+        .with_serial_port(serial_port);
+
+    if let Ok(torque) = std::env::var("TORQUE") {
+        let truthies = vec![true; ids.len()];
+
+        c.write_torque_enable(&ids, &truthies)
+            .expect("could not enable torque");
+
+        if let Ok(torque_limit) = torque.parse::<u16>() {
+            let limits = vec![torque_limit; ids.len()];
+            c.write_torque_limit(&ids, &limits)
+                .expect("could not enable torque");
+        }
+    } else {
+        let falsies = vec![false; ids.len()];
+        c.write_torque_enable(&ids, &falsies)
+            .expect("could not enable torque");
+    }
+
+    while let Some(event) = events.recv() {
+        match event {
+            Event::Input {
+                id,
+                metadata: _,
+                data,
+            } => match id.as_str() {
+                "tick" => {
+                    if let Ok(joints) = c.read_present_position(&ids) {
+                        let mut parameter = BTreeMap::new();
+                        parameter.insert(
+                            "encoding".to_string(),
+                            Parameter::String("jointstate".to_string()),
+                        );
+                        node.send_output(
+                            DataId::from("pose".to_string()),
+                            parameter,
+                            joints.into_arrow(),
+                        )
+                        .unwrap();
+                    };
+                }
+                "pose" => {
+                    let data: Vec<f64> = into_vec(&data).expect("could not cast values");
+                    c.write_goal_position(&ids, &data).unwrap();
+                }
+                other => eprintln!("Received input `{other}`"),
+            },
+            _ => {}
+        }
+    }
+
+    Ok(())
+}
+
+#[cfg(feature = "python")]
+use pyo3::{
+    pyfunction, pymodule,
+    types::{PyModule, PyModuleMethods},
+    wrap_pyfunction, Bound, PyResult, Python,
+};
+
+#[cfg(feature = "python")]
+#[pyfunction]
+fn py_main(_py: Python) -> eyre::Result<()> {
+    lib_main()
+}
+
+/// A Python module implemented in Rust.
+#[cfg(feature = "python")]
+#[pymodule]
+fn dora_rustypot(_py: Python, m: Bound<'_, PyModule>) -> PyResult<()> {
+    m.add_function(wrap_pyfunction!(py_main, &m)?)?;
+    m.add("__version__", env!("CARGO_PKG_VERSION"))?;
+    Ok(())
+}

node-hub/dora-rustypot/src/main.rs

@@ -0,0 +1,3 @@
+fn main() -> Result<(), eyre::Error> {
+    dora_rustypot::lib_main()
+}

node-hub/feetech-client/README.md

@@ -1,5 +1,9 @@
 ## FeetechClient for SCS/STS motors
 
+> [!WARNING]  
+> As of 02.05.2025, this node is no more supported and is replaced in favor of dora-rustypot for higher performance
+> with the possibility of being able to handle more servomotors.
+
 This node is a client for the Feetech motors. It is based on the Dynamixel SDK and is used to control the motors. It
 is a Python node that communicates with the motors via the USB port.
 
@@ -97,4 +101,4 @@ the goal current for the motor at the beginning, null if you don't want to set i
 
 ## License
 
-This library is licensed under the [Apache License 2.0](../../LICENSE).
+This library is licensed under the [Apache License 2.0](../../LICENSE).

node-hub/feetech-client/feetech_client/bus.py

@@ -1,4 +1,5 @@
 """TODO: Add docstring."""
+
 import enum
 from typing import Union
 
@@ -150,7 +151,9 @@ class FeetechBus:
         ]
 
         values = [
-            np.uint32(32767 - value.as_py()) if value < 0 else np.uint32(value.as_py())
+            np.uint32(32767 - value.as_py())
+            if value.as_py() < 0
+            else np.uint32(value.as_py())
             for value in data.field("values")
         ]
 
@@ -243,7 +246,9 @@ class FeetechBus:
         values = pa.array(
             [
                 self.group_readers[group_key].getData(
-                    idx, packet_address, packet_bytes_size,
+                    idx,
+                    packet_address,
+                    packet_bytes_size,
                 )
                 for idx in motor_ids
             ],

node-hub/feetech-client/feetech_client/configure.py

@@ -0,0 +1,200 @@
+"""Module for configuring and setting up the SO100 robot hardware.
+
+This module provides functionality for initializing and configuring the SO100 robot's
+servo motors and other hardware components.
+
+The program will:
+1. Disable all torque motors of provided SO100.
+2. Ask the user to move the SO100 to the position 1 (see CONFIGURING.md for more details).
+3. Record the position of the SO100.
+4. Ask the user to move the SO100 to the position 2 (see CONFIGURING.md for more details).
+5. Record the position of the SO100.
+8. Calculate the offset and inverted mode of the SO100.
+9. Let the user verify in real time that the SO100 is working properly.
+
+It will also enable all appropriate operating modes for the SO100.
+"""
+
+import argparse
+import json
+import time
+
+import pyarrow as pa
+from pwm_position_control.functions import construct_control_table
+from pwm_position_control.tables import (
+    construct_logical_to_pwm_conversion_table_arrow,
+    construct_pwm_to_logical_conversion_table_arrow,
+)
+from pwm_position_control.transform import pwm_to_logical_arrow, wrap_joints_and_values
+
+from .bus import FeetechBus, OperatingMode, TorqueMode
+
+FULL_ARM = pa.array(
+    [
+        "shoulder_pan",
+        "shoulder_lift",
+        "elbow_flex",
+        "wrist_flex",
+        "wrist_roll",
+        "gripper",
+    ],
+    type=pa.string(),
+)
+
+ARM_WITHOUT_GRIPPER = pa.array(
+    ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll"],
+    type=pa.string(),
+)
+
+GRIPPER = pa.array(["gripper"], type=pa.string())
+
+
+def pause():
+    """Pause execution and wait for user input."""
+    input("Press Enter to continue...")
+
+
+def configure_servos(bus: FeetechBus):
+    """Configure the servos with default settings.
+
+    Args:
+        bus: The FeetechBus instance to configure.
+
+    """
+    bus.write_torque_enable(
+        wrap_joints_and_values(FULL_ARM, [TorqueMode.DISABLED.value] * 6),
+    )
+
+    bus.write_operating_mode(
+        wrap_joints_and_values(FULL_ARM, [OperatingMode.ONE_TURN.value] * 6),
+    )
+
+    bus.write_max_angle_limit(
+        wrap_joints_and_values(FULL_ARM, [pa.scalar(0, pa.uint32())] * 6),
+    )
+
+    bus.write_min_angle_limit(
+        wrap_joints_and_values(FULL_ARM, [pa.scalar(0, pa.uint32())] * 6),
+    )
+
+
+def main():
+    """Run the servo configuration process."""
+    parser = argparse.ArgumentParser(
+        description="SO100 Auto Configure: This program is used to automatically configure the Low Cost Robot (SO100) "
+        "for the user.",
+    )
+
+    parser.add_argument(
+        "--port",
+        type=str,
+        required=True,
+        help="The port of the SO100.",
+    )
+    parser.add_argument(
+        "--right",
+        action="store_true",
+        help="If the SO100 is on the right side of the user.",
+    )
+    parser.add_argument(
+        "--left",
+        action="store_true",
+        help="If the SO100 is on the left side of the user.",
+    )
+
+    args = parser.parse_args()
+
+    if args.right and args.left:
+        raise ValueError("You cannot specify both --right and --left.")
+
+    args = parser.parse_args()
+
+    targets = (
+        wrap_joints_and_values(FULL_ARM, [0, -90, 90, 0, -90, 0]),
+        wrap_joints_and_values(FULL_ARM, [90, 0, 0, 90, 0, -90]),
+    )
+
+    arm = FeetechBus(
+        args.port,
+        {
+            "shoulder_pan": (1, "st3215"),
+            "shoulder_lift": (2, "st3215"),
+            "elbow_flex": (3, "st3215"),
+            "wrist_flex": (4, "st3215"),
+            "wrist_roll": (5, "st3215"),
+            "gripper": (6, "st3215"),
+        },
+    )
+
+    configure_servos(arm)
+
+    print("Please move the SO100 to the first position.")
+    pause()
+    pwm_position_1 = arm.read_position(FULL_ARM)
+    print(pwm_position_1)
+
+    print("Please move the SO100 to the second position.")
+    pause()
+    pwm_position_2 = arm.read_position(FULL_ARM)
+    print(pwm_position_2)
+
+    print("Configuration completed.")
+
+    pwm_positions = (pwm_position_1, pwm_position_2)
+
+    pwm_to_logical_conversion_table = construct_pwm_to_logical_conversion_table_arrow(
+        pwm_positions,
+        targets,
+    )
+    logical_to_pwm_conversion_table = construct_logical_to_pwm_conversion_table_arrow(
+        pwm_positions,
+        targets,
+    )
+
+    control_table_json = {}
+    for i in range(len(FULL_ARM)):
+        control_table_json[FULL_ARM[i].as_py()] = {
+            "id": i + 1,
+            "model": "sts3215",
+            "torque": True,
+            "pwm_to_logical": pwm_to_logical_conversion_table[FULL_ARM[i].as_py()],
+            "logical_to_pwm": logical_to_pwm_conversion_table[FULL_ARM[i].as_py()],
+        }
+
+    left = "left" if args.left else "right"
+    path = (
+        input(
+            f"Please enter the path of the configuration file (default is follower.{left}.json): ",
+        )
+        or f"follower.{left}.json"
+    )
+
+    with open(path, "w") as file:
+        json.dump(control_table_json, file)
+
+    control_table = construct_control_table(
+        pwm_to_logical_conversion_table,
+        logical_to_pwm_conversion_table,
+    )
+
+    while True:
+        try:
+            pwm_position = arm.read_position(FULL_ARM)
+            logical_position = pwm_to_logical_arrow(
+                pwm_position,
+                control_table,
+                ranged=True,
+            ).field("values")
+
+            print(f"Logical Position: {logical_position}")
+
+        except ConnectionError:
+            print(
+                "Connection error occurred. Please check the connection and try again.",
+            )
+
+        time.sleep(0.5)
+
+
+if __name__ == "__main__":
+    main()

node-hub/feetech-client/feetech_client/main.py

@@ -4,6 +4,7 @@ import argparse
 import json
 import os
 
+import numpy as np
 import pyarrow as pa
 from dora import Node
 
@@ -65,9 +66,16 @@ class Client:
     def pull_position(self, node, metadata):
         """TODO: Add docstring."""
         try:
+            struct = self.bus.read_position(self.config["joints"])
+            metadata["encoding"] = "jointstate"
             node.send_output(
                 "position",
-                self.bus.read_position(self.config["joints"]),
+                pa.array(
+                    np.deg2rad(
+                        ((struct.flatten()[1].to_numpy() - 2048) / 4096.0) * 360
+                    ),
+                    type=pa.float32(),
+                ),
                 metadata,
             )
 
@@ -166,21 +174,12 @@ def main():
         "torque": wrap_joints_and_values(
             pa.array(config.keys(), pa.string()),
             pa.array(
-                [
-                    (
-                        TorqueMode.ENABLED.value
-                        if config[joint]["torque"]
-                        else TorqueMode.DISABLED.value
-                    )
-                    for joint in joints
-                ],
+                [(TorqueMode.DISABLED.value) for joint in joints],
                 type=pa.uint32(),
             ),
         ),
     }
 
-    print("Feetech Client Configuration: ", bus, flush=True)
-
     client = Client(bus)
     client.run()
     client.close()

node-hub/feetech-client/pyproject.toml

@@ -1,16 +1,17 @@
 [project]
 name = "feetech-client"
 version = "0.1"
-authors = [{ name = "Hennzau", email = "<dev@enzo-le-van.fr>"}]
+authors = [{ name = "Hennzau", email = "<dev@enzo-le-van.fr>" }]
 description = "Dora Node client for feetech motors."
 license = { text = "MIT" }
 readme = "README.md"
 requires-python = ">=3.9"
 
 dependencies = [
-"dora-rs == 0.3.5",
-"numpy <=  2.0.0",
-"feetech-servo-sdk == 1.0.0", 
+  "dora-rs == 0.3.11",
+  "numpy <=  2.0.0",
+  "feetech-servo-sdk == 1.0.0",
+  "pwm-position-control",
 ]
 
 [dependency-groups]
@@ -18,7 +19,7 @@ dev = ["pytest >=8.1.1", "ruff >=0.9.1"]
 
 [project.scripts]
 feetech-client = "feetech_client.main:main"
-
+feetech-configure = "feetech_client.configure:main"
 [build-system]
 requires = ["poetry-core>=1.8.0"]
 build-backend = "poetry.core.masonry.api"
@@ -34,3 +35,6 @@ extend-select = [
   "N",    # Ruff's N rule
   "I",    # Ruff's I rule
 ]
+
+[tool.uv.sources]
+pwm-position-control = { git = "https://github.com/Hennzau/pwm-position-control" }

node-hub/feetech-client/uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.9"
 
 [[package]]
@@ -31,6 +30,17 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/de/d8/a6bfeb961b8a15c8e73a51a839482783154a2f8927fef267d57d039043dc/dora_rs-0.3.5-cp37-abi3-win_amd64.whl", hash = "sha256:88b4fe5e5569562fcdb3817abb89532f4abca913e8bd02e4ec228833716cbd09", size = 6129593 },
 ]
 
+[[package]]
+name = "dynamixel-sdk"
+version = "3.7.31"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyserial" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/e8/2c65d11d312fdb1a2308563d32f63f93c39123bcfb4909d947e0b294c794/dynamixel_sdk-3.7.31-py3-none-any.whl", hash = "sha256:74e8c112ca6b0b869b196dd8c6a44ffd5dd5c1a3cb9fe2030e9933922406b466", size = 23664 },
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -48,6 +58,7 @@ dependencies = [
     { name = "dora-rs" },
     { name = "feetech-servo-sdk" },
     { name = "numpy" },
+    { name = "pwm-position-control" },
 ]
 
 [package.dev-dependencies]
@@ -61,6 +72,7 @@ requires-dist = [
     { name = "dora-rs", specifier = "==0.3.5" },
     { name = "feetech-servo-sdk", specifier = "==1.0.0" },
     { name = "numpy", specifier = "<=2.0.0" },
+    { name = "pwm-position-control", git = "https://github.com/Hennzau/pwm-position-control" },
 ]
 
 [package.metadata.requires-dev]
@@ -89,54 +101,45 @@ wheels = [
 
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+version = "1.26.4"
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+sdist = { url = "https://files.pythonhosted.org/packages/65/6e/09db70a523a96d25e115e71cc56a6f9031e7b8cd166c1ac8438307c14058/numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010", size = 15786129 }
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