# RoboLab-EgoX Policy rollouts recorded on NVIDIA’s [RoboLab](https://github.com/NVLabs/RoboLab) manipulation benchmark, as native `.mcap` episodes. Each take carries three synchronized camera views with a matching 16-bit depth stream, per-camera intrinsics, joint positions, actions, end-effector pose, and the task instruction. Takes keep the benchmark’s own success label, so failed rollouts sit alongside successful ones: 632 of the 4,000 takes succeeded, spanning all 28 tasks and 99 background scenes. In 153 of the 4,000 takes the camera streams carry 80 frames while depth and telemetry carry 81, so align streams by timestamp rather than by index. **Details** - Dataset name: `robolab` - Dataset source: [https://huggingface.co/datasets/Voxel51/RoboLab-EgoX](https://huggingface.co/datasets/Voxel51/RoboLab-EgoX) - Dataset size: 21.85 GB - Dataset license: Apache-2.0 - Tags: `multimodal, mcap, robotics, manipulation, depth` - Supported splits: `N/A` - ZooDataset class: [`RoboLabDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.RoboLabDataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("robolab") # Rollouts the policy got right view = dataset.match({"success": True}) session = fo.launch_app(dataset) ``` ```shell fiftyone zoo datasets load robolab fiftyone app launch robolab ```