Using the Visualizer Streaming Camera View

Using the Visualizer Streaming Camera View#

For general visualizer documentation, see Visualization.

The visualizer streaming camera view is a live monitoring and debugging tool. It composites per-environment ground-truth camera frames — RGB, depth, segmentation, or surface normals — into a single panel that updates every step. The panel can display cameras that follow the robots automatically, or stream from existing scene camera sensors.

This guide is accompanied by the run_tiled_camera_visualizer.py script in the IsaacLab/scripts/tutorials/07_visualizers directory.

Running this script demonstrates two ways to use the streaming camera view:

auto-created cameras pointed at and following moving AnymalD robots shown in the Kit visualizer
streaming from existing wrist-mounted robot cameras shown in the Newton visualizer

Note

The streaming camera view is supported in the Kit, Newton GL, Rerun, and Viser visualizers. The Newton RTX visualizer accepts the configuration but does not display the panel (experimental).

Code for run_tiled_camera_visualizer.py

# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""
This script demonstrates the visualizer tiled camera panel.

.. code-block:: bash

    # Kit visualizer tiled camera panel
    uv run python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
 --task Isaac-Velocity-Rough-AnymalD --num_envs 256 --viz kit

    # Newton visualizer tiled camera panel
    uv run python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
        --task IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor --num_envs 25 --viz newton

"""

from __future__ import annotations

import argparse
import contextlib
import sys

import gymnasium as gym
import torch

import isaaclab_tasks  # noqa: F401

with contextlib.suppress(ImportError):
    import isaaclab_tasks_experimental  # noqa: F401
from isaaclab.app import add_launcher_args, launch_simulation

from isaaclab_tasks.utils import resolve_task_config, setup_preset_cli

KIT_DEFAULT_TASK = "Isaac-Velocity-Rough-AnymalD"
NEWTON_DEFAULT_TASK = "IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor"
SUPPORTED_TILED_VISUALIZERS = {"kit", "newton", "newton_gl", "newton_rtx"}
UNSUPPORTED_TILED_VISUALIZERS = {"rerun", "viser"}


def _resolve_env_regex_path(prim_path: str) -> str:
    """Resolve scene config env namespace macros to the cloned-env regex."""
    return prim_path.format(ENV_REGEX_NS="/World/envs/env_.*")


def _requested_visualizers(args_cli: argparse.Namespace) -> list[str]:
    """Return requested visualizers, defaulting to Kit for this tutorial."""
    visualizers = args_cli.visualizer or ["kit"]
    visualizers = [str(visualizer).lower() for visualizer in visualizers]

    if "none" in visualizers:
        raise ValueError("This demo requires a tiled-camera visualizer. Use '--viz kit' or '--viz newton_gl'.")
    unsupported = sorted(set(visualizers) & UNSUPPORTED_TILED_VISUALIZERS)
    if unsupported:
        raise ValueError(
            "The visualizer tiled camera panel is only implemented for Kit and Newton. "
            f"Unsupported selection: {unsupported}."
        )
    unknown = sorted(set(visualizers) - SUPPORTED_TILED_VISUALIZERS)
    if unknown:
        raise ValueError(f"Unknown visualizer selection for this demo: {unknown}.")
    return visualizers


def _make_kit_visualizer_cfg(env_cfg):
    """Create the Kit streaming-camera visualizer for the selected task."""
    from isaaclab_visualizers.kit import KitVisualizerCfg

    visualizer_cfg = KitVisualizerCfg()
    visualizer_cfg.streaming_view = True
    visualizer_cfg.streaming_envs = 36

    ego_cam_cfg = getattr(env_cfg.scene, "ego_cam", None)
    if ego_cam_cfg is not None:
        visualizer_cfg.streaming_sensor_prim_path = _resolve_env_regex_path(ego_cam_cfg.prim_path)
        return visualizer_cfg

    visualizer_cfg.streaming_sensor_prim_path = None
    visualizer_cfg.streaming_cam_eye = (3.0, 3.0, 3.0)
    visualizer_cfg.streaming_cam_target_prim_path = "/World/envs/*/Robot/base"
    # Here is an alternative eye position for a top down view
    # visualizer_cfg.streaming_cam_eye = (0.0, 0.0, 5.0)
    return visualizer_cfg


def _make_newton_visualizer_cfg(env_cfg):
    """Create the Newton streaming-camera visualizer for the selected task."""
    from isaaclab_visualizers.newton import NewtonGLVisualizerCfg

    visualizer_cfg = NewtonGLVisualizerCfg()
    visualizer_cfg.streaming_view = True
    visualizer_cfg.streaming_envs = 12

    ego_cam_cfg = getattr(env_cfg.scene, "ego_cam", None)
    if ego_cam_cfg is not None:
        visualizer_cfg.streaming_sensor_prim_path = _resolve_env_regex_path(ego_cam_cfg.prim_path)
        return visualizer_cfg

    # Here are other robot mounted camera options for this environment
    # visualizer_cfg.streaming_sensor_prim_path = "/World/envs/env_.*/Robot/left_arm_camera_sim_view_frame/left_camera"
    # visualizer_cfg.streaming_sensor_prim_path = (
    #     "/World/envs/env_.*/Robot/right_arm_camera_sim_view_frame/right_camera"
    # )
    visualizer_cfg.streaming_sensor_prim_path = None
    visualizer_cfg.streaming_cam_eye = (3.0, 3.0, 3.0)
    visualizer_cfg.streaming_cam_target_prim_path = "/World/envs/*/Robot/base"
    return visualizer_cfg


def _configure_visualizers(env_cfg, args_cli: argparse.Namespace) -> None:
    """Attach tiled camera visualizer configs to the environment simulation config."""
    visualizers = _requested_visualizers(args_cli)
    args_cli.visualizer = visualizers
    env_cfg.sim.visualizer_cfgs = [
        _make_kit_visualizer_cfg(env_cfg) if visualizer == "kit" else _make_newton_visualizer_cfg(env_cfg)
        for visualizer in visualizers
    ]


def _resolve_task(args_cli: argparse.Namespace) -> str:
    """Resolve the task for the selected visualizer."""
    if args_cli.task is not None:
        return args_cli.task
    if "newton" in _requested_visualizers(args_cli):
        return NEWTON_DEFAULT_TASK
    return KIT_DEFAULT_TASK


# add argparse arguments
parser = argparse.ArgumentParser(description="Showcase the Kit/Newton visualizer tiled camera panel.")
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
# append AppLauncher cli args
add_launcher_args(parser)
args_cli, hydra_args = setup_preset_cli(parser)
args_cli.task = _resolve_task(args_cli)
sys.argv = [sys.argv[0]] + hydra_args


def main():
    """Run a random-action environment with a tiled camera visualizer."""
    # parse configuration via Hydra (supports preset selection, e.g. presets=newton_mjwarp)
    env_cfg, _ = resolve_task_config(args_cli.task, "")
    _configure_visualizers(env_cfg, args_cli)

    with launch_simulation(env_cfg, args_cli):
        # override with CLI arguments
        env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
        env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device

        # create environment
        env = gym.make(args_cli.task, cfg=env_cfg)

        # print info (this is vectorized environment)
        print(f"[INFO]: Gym observation space: {env.observation_space}")
        print(f"[INFO]: Gym action space: {env.action_space}")
        env.reset()

        # keep stepping until all visualizer windows have been closed
        sim = env.unwrapped.sim
        if not sim.visualizers:
            print("[WARN]: No visualizers found. Exiting.")
            env.close()
            return

        while True:
            if sim.visualizers and not any(v.is_running() and not v.is_closed for v in sim.visualizers):
                break
            with torch.inference_mode():
                actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
                env.step(actions)

        env.close()


if __name__ == "__main__":
    main()

Example One: Following AnymalD Robots#

The Kit Visualizer shows the streaming camera view in a separate tab inside the main Viewport window, labelled Streaming View. The highlighted tab area in the figures below shows where to toggle between the interactive viewport and the streaming camera view.

Kit visualizer interactive viewport for AnymalD robots — Kit visualizer showing the default interactive viewport.#

Kit visualizer streaming camera view for AnymalD robots — Kit visualizer showing the streaming camera view generated for selected AnymalD robots.#

Note, you can also display the main visualizer camera and the streaming camera view side by side for dual monitoring.

To run the tutorial with the args for this example, use:

uv run python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
    --task Isaac-Velocity-Rough-AnymalD --num_envs 256 --viz kit

Within the script, you’ll find the KitVisualizerCfg configuration used to generate this example. You can use this config as a template for your own use cases.

In this example, a set of cameras is created to point toward each robot’s base prim and follow its motion. The camera’s position, relative to the prim, is set by the streaming_cam_eye field of KitVisualizerCfg. For this demo, the camera is offset by (3.0, 3.0, 3.0) from each robot base. If you change streaming_cam_eye (for example, to (0, 0, 5)), the panel will show a top-down view instead.

In this example, there are 256 total environments, and we randomly sample 36 to stream to the camera view.

The Kit visualizer streaming camera view does not require an additional camera option.

Example Two: Streaming from Robot-Mounted Cameras#

The Newton visualizer provides a streaming camera view in a lightweight OpenGL window. The panel is hidden by default. To open it, expand the Streaming View section in the left-hand sidebar and change the toggle from Hide to Open.

Newton visualizer interactive view for the Galbot cube stacking environment — Newton visualizer showing the default interactive viewport.#

Newton visualizer streaming camera view for Galbot wrist cameras — Newton visualizer showing the selected Galbot head-camera feeds in the streaming camera panel.#

In this example, we use the Galbot cube stacking environment, which comes with built-in wrist-mounted cameras. This setup provides an egocentric view of the gripper, table, and cubes in each selected environment.

To launch this example, run:

uv run python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
    --task IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor --num_envs 25 --viz newton_gl

Within the script, the NewtonGLVisualizerCfg is configured to stream images from the existing camera sensor located at /World/envs/env_.*/Robot/head_camera_sim_view_frame/head_camera. This path points to the head camera, but you can edit the streaming_sensor_prim_path field of NewtonGLVisualizerCfg in the script to show a different existing camera if needed.

In this demo, 25 environments are simulated, and 12 camera feeds are shown in the panel by default.

Configuration notes#

To customize streaming camera behavior, edit the highlighted VisualizerCfg fields in run_tiled_camera_visualizer.py:

For auto-created cameras, streaming_cam_target_prim_path chooses the followed prim and streaming_cam_eye sets the camera offset from that prim. Defaults to None, which causes the visualizer to adopt the first scene camera it discovers at init — no explicit path is needed when a TiledCamera sensor is already in the scene.
For existing scene cameras, streaming_sensor_prim_path must match an Isaac Lab Camera sensor prim path in the selected task.
streaming_envs controls how many environment tiles are shown. Pass an int to randomly sample that many environments, or a list[int] to pin specific environment indices.
streaming_gt_types selects which ground-truth types are shown — e.g. ["rgb", "depth", "segmentation"].
streaming_depth_min / streaming_depth_max set the depth colormap range in metres.

See Streaming Camera View for the full field reference.

Troubleshooting#

If a generated view fails with a missing prim error, verify that streaming_cam_target_prim_path resolves in each selected environment (common template forms: /World/envs/*/..., /World/envs/env_.*/...). In most cases you can leave it as None and let the visualizer adopt an existing scene camera automatically.
If an existing-camera view reports that no Isaac Lab camera owns the prim, check that streaming_sensor_prim_path matches a Camera sensor in the task.
If the depth panel shows a flat color, adjust streaming_depth_min and streaming_depth_max to bracket the expected depth range in your scene.
If the view is too expensive, reduce streaming_envs, --num_envs, or the camera resolution.