so-arm101-lift-isaaclab

SO-ARM101 Cube Lifting Policy

This model is a reinforcement learning policy trained for the SO-ARM101 robot arm to perform cube lifting tasks in Isaac Lab.

Model Description

• Task: Lift a cube and bring it to a target position
• Robot: SO-ARM101 (6-DOF robotic arm)
• Framework: Isaac Lab 2.3.0 (on Isaac Sim 5.1.0)
• Algorithm: RSL-RL (Robotic Systems Lab - Reinforcement Learning)
• Environment: Isaac-SO-ARM101-Lift-Cube-v0
• Training: 1950 iterations with 4096 parallel environments

Training Details

Environment Configuration

• Observation Space: Joint positions, velocities, cube pose, target position
• Action Space: Joint position commands (6 DOF)
• Reward Function: Based on distance to target, cube height, and success criteria
• Episode Length: Variable (task-dependent)

Training Parameters

• Parallel Environments: 4096
• Total Iterations: 1950
• Training Time: ~2 hours on NVIDIA RTX 4080 Super (16GB VRAM)
• Framework: Isaac Lab with RSL-RL runner
• Simulator: Isaac Sim 5.1.0

Hardware Used

• GPU: NVIDIA RTX 4080 Super (16GB VRAM)
• OS: Ubuntu 24.04 LTS
• CUDA: 13.0

Usage

Prerequisites

# Install Isaac Lab (with Docker)
# See: https://isaac-sim.github.io/IsaacLab/

# Clone SO-ARM101 external project
git clone https://github.com/MuammerBay/isaac_so_arm101.git
cd isaac_so_arm101

Evaluation

# Inside Isaac Lab container
cd /workspace/isaaclab

# Run the trained policy
./isaaclab.sh -p /workspace/isaac_so_arm101/src/isaac_so_arm101/scripts/rsl_rl/play.py \
    --task Isaac-SO-ARM101-Lift-Cube-Play-v0 \
    --checkpoint /path/to/model_1950.pt

Training From Scratch

# Train the policy
./isaaclab.sh -p /workspace/isaac_so_arm101/src/isaac_so_arm101/scripts/rsl_rl/train.py \
    --task Isaac-SO-ARM101-Lift-Cube-v0 \
    --num_envs 4096 \
    --headless

Performance

The trained policy successfully lifts and moves cubes to target positions with high success rate in simulation.

Citation

If you use this model, please cite:

@misc{so-arm101-lift-isaaclab,
  title={SO-ARM101 Cube Lifting Policy trained with Isaac Lab},
  author={PathOn AI},
  year={2026},
  howpublished={\url{https://huggingface.co/}},
}

@software{isaaclab,
  author = {Mittal, Mayank and others},
  title = {Isaac Lab: A Unified Framework for Robot Learning},
  url = {https://isaac-sim.github.io/IsaacLab/},
  year = {2024},
}

Related Resources

• Isaac Lab Documentation
• SO-ARM101 Isaac Lab Integration
• RSL-RL Library

License

MIT License