Limit Dataset Card update to license

Restore the current public card content and retain only the approved CC BY 4.0 license update.

Signed-off-by: Clemens Volk <cvolk@nvidia.com>

README.md

@@ -1,20 +1,16 @@
 ---
 pretty_name: Arena DROID Camera Sensitivity Workflow Sample
 license: cc-by-4.0
-language:
-- en
 task_categories:
 - robotics
 tags:
-- other
-- tabular
 - robotics
-- synthetic
-- evaluation
 - simulation
 - policy-evaluation
 - sensitivity-analysis
 - isaaclab-arena
+language:
+- en
 size_categories:
 - n<1K
 ---
@@ -25,15 +21,15 @@ size_categories:
 
 Arena-DROID-Camera-Sensitivity-Workflow-Sample is a compact set of episode-level results generated by an Isaac Lab-Arena simulation experiment. It lets users run the documented camera sensitivity analysis without first executing the policy-evaluation sweep.
 
-The experiment evaluates an OpenPI `pi05` policy on a DROID Rubik's-cube pick-and-place task while independently varying the wrist-camera position on three axes. Each JSONL record contains the sampled camera offset, task instruction, episode outcome and length, timestamp, objective progress, and predicate events. The dataset does not contain robot trajectories, actions, observations, images, video, audio, simulation assets, software, or model weights.
+The experiment evaluates an OpenPI `pi05` policy on a DROID Rubik's-cube pick-and-place task while independently varying the wrist-camera position on three axes. Each JSONL record contains the sampled camera offset, task instruction, episode outcome and length, timestamp, objective progress, and predicate events. The dataset does not contain robot trajectories, actions, observations, images, video, audio, or model weights.
 
 | Dataset version | Records | Successful | Unsuccessful | Data file |
 |---|---:|---:|---:|---|
 | `arena_v0.2_lab_v3.0` | 10 | 7 | 3 | `episode_results_rebuild0.jsonl` |
 
-This is a workflow sample. Its size is sufficient to demonstrate the software path, but not to measure policy robustness or compare policy performance. This dataset is ready for commercial or non-commercial uses.
+This is a workflow sample. Its size is sufficient to demonstrate the software path, but not to measure policy robustness or compare policy performance.
 
-## Dataset Owner(s)
+## Dataset Owner
 
 - Owner: NVIDIA Corporation
 - Technical contact: [Isaac Lab-Arena maintainers](https://github.com/isaac-sim/IsaacLab-Arena/issues)
@@ -44,18 +40,11 @@ This is a workflow sample. Its size is sufficient to demonstrate the software pa
 - Simulation run: 2026-08-06
 - Hugging Face repository created: 2026-08-07
 
-## Versioning
-
-- Version: `arena_v0.2_lab_v3.0`
-- Dataset content commit: `6a879e4b09bc9911453dd8271081dfcf7d3aa9e1`
-- Data-file SHA-256: `329a25484c2edc997d74e540b3bfe615a99390270c696c29effb73cc86497ea8`
-- Previous version(s): No previous version. This is the initial release of this dataset.
-
 ## License/Terms of Use
 
 This dataset is governed by the Creative Commons Attribution 4.0 International License (CC-BY-4.0).
 
-## Intended Usage
+## Intended Use
 
 The dataset is intended to:
 
@@ -74,7 +63,7 @@ The dataset is not intended for:
 
 ### Data Collection Method
 
-**Synthetic.** The records were generated automatically in simulation. No human teleoperation or manual data collection was used during this evaluation run or included in the released payload. This statement does not describe the separate training data used to develop the OpenPI policy.
+The records were generated automatically in simulation. No human teleoperation or manual annotation was used during this evaluation run or included in the released payload. This statement does not describe the separate training data used to develop the OpenPI policy.
 
 The experiment used:
 
@@ -89,15 +78,18 @@ The dataset does not include the simulation assets, OpenPI software, or policy w
 
 ### Labeling Method
 
-**Automated.** Isaac Lab-Arena generated the `success`, progress, and event values automatically from the task's objective predicates. There are no human-provided labels.
+Isaac Lab-Arena generated the `success`, progress, and event values automatically from the task's objective predicates. There are no human-provided labels.
 
-## Dataset Format
+### Version and Provenance
 
-- Modality: Tabular
-- Format: JSON Lines (JSONL)
-- Data file: `episode_results_rebuild0.jsonl`
+- Dataset revision: `arena_v0.2_lab_v3.0`
+- Dataset content commit: `6a879e4b09bc9911453dd8271081dfcf7d3aa9e1`
+- Data-file SHA-256: `329a25484c2edc997d74e540b3bfe615a99390270c696c29effb73cc86497ea8`
+- Isaac Lab-Arena introduction: [pull request 1055](https://github.com/isaac-sim/IsaacLab-Arena/pull/1055)
 
-Each line is one completed episode with 10 top-level fields.
+## Dataset Format
+
+The dataset contains one newline-delimited JSON file, `episode_results_rebuild0.jsonl`. Each line is one completed episode.
 
 | Field | Type | Description |
 |---|---|---|
@@ -124,9 +116,8 @@ Hugging Face may present the records as an automatically inferred `train` split.
 
 ## Dataset Quantification
 
-- Record count: 10
-- Feature count: 10 top-level fields
-- Total data storage: 0.0121 MB (12,101 bytes)
+- JSONL records: 10
+- Data-file size: 12,101 bytes
 - Successful episodes: 7
 - Unsuccessful episodes: 3
 - Environment IDs: one (`env_id = 0`)
@@ -138,11 +129,11 @@ The released JSONL parses as 10 complete JSON objects. The recorded camera offse
 
 ## Personal Data and Third-Party Content
 
-A review of `episode_results_rebuild0.jsonl` at revision `arena_v0.2_lab_v3.0` found no names, email addresses, account identifiers, faces, voices, biometric information, or real-world sensor captures. The file contains experiment identifiers, fixed task text, numeric results, and timestamps without timezone offsets.
+A review of `episode_results_rebuild0.jsonl` at revision `arena_v0.2_lab_v3.0` found no names, email addresses, account identifiers, faces, voices, or real-world sensor captures. The file contains experiment identifiers, fixed task text, numeric results, and timestamps without timezone offsets.
 
-The JSONL contains textual identifiers referring to DROID, OpenPI, and a Rubik's cube. It does not include or redistribute related assets, software, datasets, model weights, logos, images, or product recordings. Isaac Lab-Arena and the OpenPI `pi05` policy stack were generation tools, not included content.
+The JSONL contains textual identifiers referring to DROID, OpenPI, and a Rubik's cube. It does not include or redistribute related assets, software, or model weights.
 
-## Key Considerations and Limitations
+## Limitations
 
 - Ten episodes are too few for reliable sensitivity, robustness, or performance conclusions.
 - All records come from one task, policy variant, seed, and simulation configuration.
@@ -150,7 +141,7 @@ The JSONL contains textual identifiers referring to DROID, OpenPI, and a Rubik's
 - Outcomes depend on the task predicates and thresholds used by the recorded workflow.
 - Simulation results do not establish real-world robot behavior or safety.
 - The timestamps do not include an explicit timezone offset.
-- The JSONL does not identify the exact OpenPI model checkpoint or software commit used to generate the run.
+- The JSONL does not identify the exact OpenPI model or software commit used to generate the run.
 - The downloadable sample has a `0.7` success rate. Metrics shown for other runs in the workflow documentation do not describe this file.
 
 ## Download and Use
@@ -167,19 +158,17 @@ hf download \
 
 Follow the [Isaac Lab-Arena sensitivity-analysis workflow](https://isaac-sim.github.io/IsaacLab-Arena/main/pages/example_workflows/sensitivity_analysis/sensitivity_analysis.html) to generate the example posterior report.
 
-## Reference(s)
+## Ethical Considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility and has established policies and practices to support the development of AI applications. Users should evaluate whether this dataset and the surrounding workflow are appropriate for their use case and should not treat this small simulation sample as evidence of safe or effective deployment.
+
+Please report security vulnerabilities or NVIDIA AI concerns through the [NVIDIA Product Security reporting page](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+## References
 
 - [Dataset repository](https://huggingface.co/datasets/nvidia/Arena-DROID-Camera-Sensitivity-Workflow-Sample)
 - [Pinned dataset revision](https://huggingface.co/datasets/nvidia/Arena-DROID-Camera-Sensitivity-Workflow-Sample/tree/arena_v0.2_lab_v3.0)
 - [Isaac Lab-Arena repository](https://github.com/isaac-sim/IsaacLab-Arena)
-- [Isaac Lab-Arena introduction pull request](https://github.com/isaac-sim/IsaacLab-Arena/pull/1055)
-- [Generation tool: OpenPI repository](https://github.com/Physical-Intelligence/openpi)
-- [Sensitivity-analysis workflow](https://isaac-sim.github.io/IsaacLab-Arena/main/pages/example_workflows/sensitivity_analysis/sensitivity_analysis.html)
+- [OpenPI repository](https://github.com/Physical-Intelligence/openpi)
 - [Sensitivity-analysis workflow source](https://github.com/isaac-sim/IsaacLab-Arena/blob/main/docs/pages/example_workflows/sensitivity_analysis/sensitivity_analysis.rst)
 - [Experiment configuration](https://github.com/isaac-sim/IsaacLab-Arena/blob/main/isaaclab_arena_environments/experiment_configs/droid_pnp_camera_sensitivity_openpi_experiment.yaml)
-
-## Ethical Considerations
-
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
-
-Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).