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 (cube-shaped primitives on a table) to target positions through reinforcement learning. The task uses a command-based goal specification, where the RL agent learns to reach sampled target poses.
This model is ready for non-commercial use only.
Governing Terms
Your usage of the Arena-Franka-Lift-Object-RL-Task is governed by the
NVIDIA License.
Deployment Geography:
Global
Release Date:
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):
Model Architecture:
Architecture Type: Multi-layer Perceptron
Network Architecture:
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]
Input Format:
- Robot State: Floating Point
Input Parameters:
- Robot State: One-Dimensional (1D) - Floating number vector
Other Properties Related to Input: Continuous-value vectors correspond to different motor controls on a robot, which depends on Degrees of Freedom of the robot embodiment.
Output:
Output Type(s): End-effector target pose
Output Format: Continuous-value vectors
Output Parameters: [One-Dimensional (1D)]
Other Properties Related to Output: Continuous-value vectors correspond to different motor controls on a robot, which depends on Degrees of Freedom of the robot embodiment.
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.
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.
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 Modality: Other: Robot simulation
Training Data Size: Not Applicable
Data Collection Method: Simulated Franka - Reinforcement Learning
Labeling Method: Not Applicable
Properties: The model was trained completely in simulation (IsaacLab-Arena) using a on-policy Reinforcement Learning method. In this case, the data was collected for a couple of episodes, then the algorithm ran gradient descent on the collected data to update the model weights. Once this step was completed, the collected data was discarded and a new batch of data was collected.
The simulation environment contained the robot itself, a cube sitting on a table, and a ground plane.
Evaluation
Data Modality: Other: Robot simulation
Data Collection Method: Simulated Franka - Reinforcement Learning
Labeling Method: Not Applicable
Properties: The evaluation was performed in simulation using the IsaacLab-Arena. The evaluation data consists of dynamically generated episodes of lifting tasks.
Inference:
Acceleration Engine(s): PyTorch
Test Hardwares
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
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.
