feat: use CDR at Go2 module and viewer boundaries

.github/workflows/message-codegen.yml

@@ -132,7 +132,7 @@ jobs:
         if: runner.os == 'Linux'
         run: |
           sudo apt-get update
-          sudo apt-get install -y liblcm-dev
+          sudo apt-get install -y liblcm-dev libportaudio2
           mkdir -p build/sdk-deps
           curl --fail --location --retry 3 https://github.com/eclipse-zenoh/zenoh-c/releases/download/1.10.1/zenoh-c-1.10.1-x86_64-unknown-linux-gnu-standalone.zip -o build/sdk-deps/zenoh-c.zip
           echo '9ee0f2d732b0f3042a7e1cd3076042a2bc3ac0415587c40bc3ed7b8b62fbde11  build/sdk-deps/zenoh-c.zip' | sha256sum --check
@@ -153,6 +153,13 @@ jobs:
           .venv/bin/python -m pytest --noconftest --import-mode=importlib -o addopts='' -o 'markers=native_e2e: compiled native consumer acceptance' -q dimos/message_codegen/test_project_native.py -k actual_coordinator
           .native-review/bin/python -I -c 'from dimos.memory import module; from dimos.memory.store import mcap; import pytest; import sys; assert all("site-packages" in str(value.__file__) for value in (module,mcap)); raise SystemExit(pytest.main(sys.argv[1:]))' --noconftest --import-mode=importlib -o addopts='' -q "$PWD/dimos/memory/test_module.py" "$PWD/dimos/memory/store/test_cdr_recording.py" "$PWD/dimos/memory/store/test_mcap.py"
           cargo test --manifest-path native/rust/dimos-module/Cargo.toml --locked --features cdr --lib --jobs 2
+      - name: Verify Go2 CDR boundaries with stubbed drivers and installed code
+        if: runner.os == 'Linux'
+        run: |
+          # Import-only SDK subset, pinned to uv.lock. Audio/hardware APIs are never called.
+          uv pip install --python .native-review/bin/python 'aiortc==1.14.0'
+          uv pip install --python .native-review/bin/python --no-deps 'unitree-webrtc-connect==2.1.2' 'wasmtime==41.0.0' 'pycryptodome==3.23.0' 'curl-cffi==0.15.0' 'sounddevice==0.5.5'
+          LCM_DEFAULT_URL=memq:// .native-review/bin/python -I -c 'from dimos.robot.unitree.go2 import connection; from dimos.robot.unitree.go2.blueprints.basic import unitree_go2_basic; import pytest; import sys; assert all("site-packages" in str(value.__file__) for value in (connection,unitree_go2_basic)); raise SystemExit(pytest.main(sys.argv[1:]))' --noconftest --import-mode=importlib -o addopts='' -q "$PWD/dimos/robot/unitree/go2/test_connection.py" "$PWD/dimos/robot/unitree/test_connection.py" "$PWD/dimos/robot/unitree/go2/blueprints/basic/test_unitree_go2_basic.py"
       - name: Verify generated browser schemas, relay encoding and web consumers
         if: runner.os == 'Linux'
         run: |

dimos/models/embedding/clip.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import CLIPModel as HFCLIPModel, CLIPProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/dino.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import Any, overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import AutoImageProcessor, AutoModel
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/siglip.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import SiglipModel as HFSiglipModel, SiglipProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/siglip2.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import SiglipModel as HFSiglipModel, SiglipProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/test_generated_images.py

@@ -29,20 +29,25 @@
 from dimos.msgs.image import image_from_array, image_view
 
 
-@pytest.mark.parametrize("model_type", [CLIPModel,DINOModel,SigLIPModel,SigLIP2Model])
-def test_generated_embeddings_keep_zero_source_time_and_convert_rgb(mocker,model_type):
-    processor,network=mocker.Mock(),mocker.Mock()
-    features=torch.tensor([[3.0,4.0]])
-    processor.return_value=BatchFeature(data={"pixel_values":torch.zeros(1,3,4,8)})
-    network.get_image_features.return_value=features
-    network.return_value=BaseModelOutputWithPooling(last_hidden_state=features[:,None,:],pooler_output=features)
-    mocker.patch.object(model_type,"_processor",new_callable=PropertyMock,return_value=processor)
-    mocker.patch.object(model_type,"_model",new_callable=PropertyMock,return_value=network)
-    image=image_from_array(np.full((4,8,3),[0,0,255],dtype=np.uint8),encoding="bgr8",
-        header=Header(frame_id="camera",stamp=Time(sec=0,nanosec=0)))
-    model=model_type(device="cpu")
-    result=model.embed(image)
+@pytest.mark.parametrize("model_type", [CLIPModel, DINOModel, SigLIPModel, SigLIP2Model])
+def test_generated_embeddings_keep_zero_source_time_and_convert_rgb(mocker, model_type):
+    processor, network = mocker.Mock(), mocker.Mock()
+    features = torch.tensor([[3.0, 4.0]])
+    processor.return_value = BatchFeature(data={"pixel_values": torch.zeros(1, 3, 4, 8)})
+    network.get_image_features.return_value = features
+    network.return_value = BaseModelOutputWithPooling(
+        last_hidden_state=features[:, None, :], pooler_output=features
+    )
+    mocker.patch.object(model_type, "_processor", new_callable=PropertyMock, return_value=processor)
+    mocker.patch.object(model_type, "_model", new_callable=PropertyMock, return_value=network)
+    image = image_from_array(
+        np.full((4, 8, 3), [0, 0, 255], dtype=np.uint8),
+        encoding="bgr8",
+        header=Header(frame_id="camera", stamp=Time(sec=0, nanosec=0)),
+    )
+    model = model_type(device="cpu")
+    result = model.embed(image)
     assert result.timestamp == 0.0
-    torch.testing.assert_close(result.vector,torch.tensor([0.6,0.8]))
-    assert processor.call_args.kwargs["images"][0].getpixel((0,0)) == (255,0,0)
-    np.testing.assert_array_equal(image_view(image)[0,0],[0,0,255])
+    torch.testing.assert_close(result.vector, torch.tensor([0.6, 0.8]))
+    assert processor.call_args.kwargs["images"][0].getpixel((0, 0)) == (255, 0, 0)
+    np.testing.assert_array_equal(image_view(image)[0, 0], [0, 0, 255])

dimos/models/embedding/treid.py

@@ -19,11 +19,11 @@
 
 from functools import cached_property
 
-from dimos.messages.sensor_msgs.msg import Image
 import torch
 import torch.nn.functional as functional
 from torchreid import utils as torchreid_utils
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import LocalModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, EmbeddingModelConfig
 from dimos.msgs.image import image_to_bgr

dimos/models/segmentation/edge_tam.py

@@ -20,14 +20,14 @@
 import tempfile
 from typing import TYPE_CHECKING, Any, Protocol, TypedDict
 
-from dimos.messages.sensor_msgs.msg import Image
 from hydra.utils import instantiate
 import numpy as np
 from numpy.typing import NDArray
 from omegaconf import OmegaConf
 from PIL import Image as PILImage
 import torch
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import default_torch_device
 from dimos.msgs.image import image_to_bgr, image_to_jpeg
 from dimos.perception.detection.detectors.base import Detector

dimos/models/segmentation/test_yoloe.py

@@ -16,11 +16,11 @@
 from typing import Any
 from unittest.mock import MagicMock
 
-from dimos.messages.sensor_msgs.msg import Image
-from dimos.messages.std_msgs.msg import Header
 import numpy as np
 from ultralytics.models.yolo.yoloe import YOLOEVPSegPredictor
 
+from dimos.messages.sensor_msgs.msg import Image
+from dimos.messages.std_msgs.msg import Header
 from dimos.models.segmentation.yoloe import YoloeBoxSegmenter
 from dimos.msgs.image import image_from_array, image_to_bgr
 from dimos.msgs.time import time_from_seconds, to_seconds

dimos/models/segmentation/yoloe.py

@@ -19,11 +19,11 @@
 from dataclasses import replace
 from typing import Protocol
 
-from dimos.messages.sensor_msgs.msg import Image
 import numpy as np
 from numpy.typing import NDArray
 from ultralytics.models.yolo.yoloe import YOLOEVPSegPredictor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.msgs.image import image_to_bgr
 from dimos.perception.detection.detectors.yoloe import Yoloe2DDetector, YoloePromptMode
 from dimos.perception.detection.type.detection2d.bbox import Bbox, Detection2DBBox

dimos/robot/unitree/go2/blueprints/basic/test_unitree_go2_basic.py

@@ -0,0 +1,61 @@
+# Copyright 2025-2026 Dimensional Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Go2 blueprint rendering consumes the same generated values as live ports."""
+
+import numpy as np
+
+from dimos.messages.builtin_interfaces.msg import Time
+from dimos.messages.dimos_msgs.msg import LineSegment3D, LineSegments3D
+from dimos.messages.geometry_msgs.msg import Point, Pose, PoseArray, PoseStamped, Quaternion
+from dimos.messages.std_msgs.msg import Header
+from dimos.msgs.occupancy import occupancy_from_array
+from dimos.msgs.pointcloud import pointcloud_from_xyz
+from dimos.robot.unitree.go2.blueprints.basic import unitree_go2_basic as view
+from dimos.robot.unitree.go2.connection import GO2Connection
+
+
+def test_camera_and_pose_overrides_accept_generated_port_values():
+    camera = view._convert_camera_info(GO2Connection.camera_info_static)
+    assert camera[0][0] == "/world/color_image"
+    assert camera[0][1].resolution.as_arrow_array().to_pylist() == [[1280.0, 720.0]]
+    pose = Pose(Point(1.0, 2.0, 3.0), Quaternion(0.0, 0.0, 0.0, 1.0))
+    entries = dict(view._plot_odom(PoseStamped(Header(Time(0, 0), "world"), pose)))
+    assert "world/odom/message" in entries
+    assert "world/odom" in entries
+    assert entries["plots/odom/x"].scalars.as_arrow_array().to_pylist() == [1.0]
+    assert entries["plots/odom/y"].scalars.as_arrow_array().to_pylist() == [2.0]
+
+
+def test_map_and_pgo_overrides_preserve_rendered_geometry():
+    header = Header(Time(0, 0), "world")
+    cloud = pointcloud_from_xyz(np.array([[1.0, 2.0, 0.2], [9.0, 8.0, -0.1]]), header=header)
+    points = view._convert_global_map(cloud).positions.as_arrow_array().to_pylist()
+    np.testing.assert_allclose(points, [[1.0, 2.0, 0.2]])
+    pose = Pose(Point(1.0, 2.0, 3.0), Quaternion(0.0, 0.0, 0.0, 1.0))
+    keyframes = dict(view._convert_pgo_keyframes(PoseArray(header, [pose])))
+    assert keyframes["world/pgo/keyframes"].positions.as_arrow_array().to_pylist() == [
+        [1.0, 2.0, 3.0]
+    ]
+    edges = LineSegments3D(header, [LineSegment3D(Point(1.0, 2.0, 3.0), Point(4.0, 5.0, 6.0), 0.9)])
+    loops = dict(view._convert_pgo_loops(edges))
+    assert loops["world/pgo/loops"].strips.as_arrow_array().to_pylist() == [
+        [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
+    ]
+    grid = occupancy_from_array(np.array([[0, 100]], dtype=np.int8), header=header, resolution=0.5)
+    mesh = view._convert_navigation_costmap(grid)
+    np.testing.assert_allclose(
+        mesh.vertex_positions.as_arrow_array().to_pylist(),
+        [[0.0, 0.0, 0.015], [1.0, 0.0, 0.015], [1.0, 0.5, 0.015], [0.0, 0.5, 0.015]],
+    )

dimos/robot/unitree/go2/blueprints/basic/unitree_go2_basic.py

@@ -23,7 +23,11 @@
 
 from dimos.core.coordination.blueprints import autoconnect
 from dimos.core.global_config import global_config
+from dimos.msgs.geometry_msgs.Pose import Pose as NumericalPose
+from dimos.msgs.nav_msgs.OccupancyGrid import OccupancyGrid as NumericalGrid
+from dimos.msgs.occupancy import occupancy_view
 from dimos.robot.unitree.go2.connection import GO2Connection
+from dimos.visualization.rerun.cdr import camera_pinhole, cloud_archetype, generated_archetypes
 from dimos.visualization.vis_module import vis_module
 
 if TYPE_CHECKING:
@@ -32,30 +36,32 @@
     from rerun._baseclasses import Archetype
     from rerun.blueprint import Blueprint
 
-    from dimos.msgs.geometry_msgs.PoseArray import PoseArray
-    from dimos.msgs.geometry_msgs.PoseStamped import PoseStamped
-    from dimos.msgs.geometry_msgs.Twist import Twist
-    from dimos.msgs.nav_msgs.LineSegments3D import LineSegments3D
-    from dimos.msgs.nav_msgs.OccupancyGrid import OccupancyGrid
-    from dimos.msgs.sensor_msgs.CameraInfo import CameraInfo
-    from dimos.msgs.sensor_msgs.PointCloud2 import PointCloud2
-    from dimos.msgs.std_msgs.Float32 import Float32
+    from dimos.messages.dimos_msgs.msg import LineSegments3D
+    from dimos.messages.geometry_msgs.msg import PoseArray, PoseStamped, Twist
+    from dimos.messages.nav_msgs.msg import OccupancyGrid
+    from dimos.messages.sensor_msgs.msg import CameraInfo, PointCloud2
+    from dimos.messages.std_msgs.msg import Float32
     from dimos.visualization.rerun.bridge import RerunData, RerunMulti
 
 
 def _convert_camera_info(camera_info: CameraInfo) -> RerunData:
-    return camera_info.to_rerun(
-        image_topic="/world/color_image",
-        optical_frame="camera_optical",
-    )
+    return [("/world/color_image", camera_pinhole(camera_info))]
 
 
 def _convert_global_map(grid: PointCloud2) -> Archetype:
-    return grid.to_rerun(bottom_cutoff=0)
+    return cloud_archetype(grid, bottom_cutoff=0)
 
 
 def _convert_navigation_costmap(grid: OccupancyGrid) -> Archetype:
-    return grid.to_rerun(
+    # Reuse the existing numerical texture renderer behind an explicit value adapter.
+    p, q = grid.info.origin.position, grid.info.origin.orientation
+    numerical = NumericalGrid(
+        grid=occupancy_view(grid),
+        resolution=grid.info.resolution,
+        origin=NumericalPose(p.x, p.y, p.z, q.x, q.y, q.z, q.w),
+        frame_id=grid.header.frame_id,
+    )
+    return numerical.to_rerun(
         colormap="Accent",
         z_offset=0.015,
         opacity=0.2,
@@ -66,7 +72,10 @@ def _convert_navigation_costmap(grid: OccupancyGrid) -> Archetype:
 def _convert_pgo_keyframes(keyframes: PoseArray) -> RerunMulti:
     import rerun as rr
 
-    positions = keyframes.positions()
+    positions = np.asarray(
+        [[pose.position.x, pose.position.y, pose.position.z] for pose in keyframes.poses],
+        dtype=np.float32,
+    ).reshape(-1, 3)
     return [
         ("world/pgo/keyframes", rr.Points3D(positions, colors=[[255, 0, 0]], radii=[0.025])),
         ("world/pgo/path", rr.LineStrips3D([positions], colors=[[255, 255, 255]], radii=[0.01])),
@@ -76,7 +85,13 @@ def _convert_pgo_keyframes(keyframes: PoseArray) -> RerunMulti:
 def _convert_pgo_loops(edges: LineSegments3D) -> RerunMulti:
     import rerun as rr
 
-    segments = edges.segments.astype(np.float32)
+    segments = np.asarray(
+        [
+            [[edge.start.x, edge.start.y, edge.start.z], [edge.end.x, edge.end.y, edge.end.z]]
+            for edge in edges.segments
+        ],
+        dtype=np.float32,
+    ).reshape(-1, 2, 3)
     strips = rr.LineStrips3D(segments, colors=[[231, 76, 60]], radii=[0.008])
     return [("world/pgo/loops", strips)]
 
@@ -90,10 +105,12 @@ def _plot_telemetry(name: str, msg: Float32) -> RerunMulti:
 def _plot_odom(odom: PoseStamped) -> RerunMulti:
     import rerun as rr
 
+    entries = generated_archetypes(odom, "world/odom")
+    assert entries is not None
     return [
-        ("world/odom", odom.to_rerun()),
-        ("plots/odom/x", rr.Scalars(odom.x)),
-        ("plots/odom/y", rr.Scalars(odom.y)),
+        *entries,
+        ("plots/odom/x", rr.Scalars(odom.pose.position.x)),
+        ("plots/odom/y", rr.Scalars(odom.pose.position.y)),
     ]