Create README.md

README.md

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+---
+tags:
+- robotics
+---
+
+<div align="center">
+  <a href="https://github.com/isaac-sim/IsaacLab-Arena">
+    <img src="https://img.shields.io/badge/GitHub-grey?logo=GitHub" alt="GitHub Badge">
+  </a>
+  <a href="https://fictional-disco-qm1zq12.pages.github.io/html/index.html">
+    <img src="https://img.shields.io/badge/Website-green" alt="Website Badge">
+  </a>
+</div>
+
+## Description:
+
+Arena-Franka-Lift-Object-RL-Task is a model trained for the object lifting task in one of IsaacLab-Arena example workflows. The Franka Panda robot learns to grasp and lift objects to target positions through reinforcement learning. The task uses a command-based goal specification, where the RL agent learns to reach sampled target poses.<br><br>
+This model is ready for non-commercial use only.
+
+## License/Terms of Use
+[Nvidia License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyIsIm5jaWQiOiJzby15b3V0LTg3MTcwMS12dDQ4In0=)<br>
+You are responsible for ensuring that your use of NVIDIA provided Models complies with all applicable laws. <br>
+
+### Deployment Geography:
+Global
+
+### Release Date:
+
+GitHub: 03/30/2026 via [isaac-sim/IsaacLab-Arena](https://github.com/isaac-sim/IsaacLab-Arena/)
+Hugging Face: 03/30/2026 via [nvidia/Arena-Franka-Lift-Object-RL-Task](https://huggingface.co/nvidia/Arena-Franka-Lift-Object-RL-Task)
+
+### Use Case:
+Researchers, Academics, Open-Source Community: AI-driven robotics research and algorithm development.
+Developers: Integrate and customize AI for various robotic applications.
+Startups & Companies: Accelerate robotics development and reduce training costs.
+
+## Reference(s):
+RSL‑RL: A Learning Library for Robotics Research, arXiv preprint (2025), arXiv:2509.10771
+
+## Model Architecture:
+
+**Architecture Type:**  Multi-layer Perceptron
+
+**Network Architecture:** 
+The model is trained to move the Franka end effector lifting the object to commanded target position. The model itself is a MLP network trained with on-policy PPO using RSL-RL PPO framework.
+
+**Number of Model Parameters:**
+105,871
+
+## Input:
+**Input Type:**
+* Robot State: End-effector pose capsulating position and rotation [x, y, z, w, x, y, z]<br>
+
+**Input Format:**
+* Robot State: Floating Point<br>
+
+**Input Parameters:**
+* Robot State: One-Dimensional (1D) - Floating number vector<br>
+
+## Output:
+**Output Type(s):** End-effector target pose<br>
+**Output Format:** Continuous-value vectors<br>
+**Output Parameters:** [One-Dimensional (1D)] <br>
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+
+## Model Version(s):
+Version 0.1.
+
+# Training, Testing, and Evaluation:
+**Training & Testing:** The model was trained completely in simulation (IsaacLab-Arena) using a on-policy Reinforcement Learning method.
+
+**Data Collection Method:** N/A (Reinforcement Learning Only)
+
+**Labeling Method:** N/A
+
+**Properties:** There was no data collected or used for training. The model was trained completely in simulation (IsaacLab-Arena) using a on-policy Reinforcement Learning method. In this case, the data will be collected for a couple of episodes, then the algorithm will run gradient descent on these collected data to update the model weights. Once this step is done, the collected data will be discarded and a new batch of data will be collected and so on.
+
+As for the simulation environment, it contains the robot itself, a cube sitting on a table, and a ground plane.
+
+All the training, testing, and evaluation was done in the same simulation environment. No data has been collected in any of these procedures.
+
+
+## Inference:
+**Acceleration Engine(s):** PyTorch
+
+**Test Hardwares** 
+All of the below:
+* NVIDIA Ampere
+* NVIDIA Blackwell
+* NVIDIA Jetson
+* NVIDIA Hopper
+* NVIDIA Lovelace
+
+**Supported Operating System(s):**
+* Linux
+
+## Software Integration
+
+**Runtime Engine(s):** Not Applicable
+
+**Supported Hardware Microarchitecture Compatibility:**
+All of the below:
+* NVIDIA Ampere
+* NVIDIA Blackwell
+* NVIDIA Jetson
+* NVIDIA Hopper
+* NVIDIA Lovelace
+
+**Preferred/Supported Operating System(s):** Linux
+
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
+
+## 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.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).