This policy learns to control the SO-ARM101 robot arm's joint positions to reach target end-effector poses. The model effectively learns inverse kinematics behavior through reinforcement learning, enabling the robot to accurately position its end-effector at desired 3D locations.

Training Details

Environment Configuration

Observation Space: Joint positions, velocities, and target pose relative to end-effector
Action Space: Joint position commands (6 DOF)
Reward Function: Negative distance between end-effector and target pose
Episode Length: Variable (resets on success or timeout)

Training Parameters

Parallel Environments: 4096
Total Iterations: 999
Training Time: ~1.5 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

bash

# 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

bash

# 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-Reach-Play-v0 \
    --checkpoint /path/to/model_999.pt

Training From Scratch

bash

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

Performance

The trained policy demonstrates accurate reaching behavior with the SO-ARM101 robot, successfully moving the end-effector to target positions across the reachable workspace with high precision.

Use Cases

This reaching policy serves as a foundation for:

Inverse Kinematics: Learned IK controller for end-effector positioning
Manipulation Tasks: Base controller for pick-and-place, assembly, etc.
Trajectory Following: Can be extended for path planning applications
Sim-to-Real Transfer: Ready for deployment on real SO-ARM101 hardware

Citation

If you use this model, please cite:

bibtex

@misc{so-arm101-reach-isaaclab,
  title={SO-ARM101 Reaching 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

License

MIT License

PathOn-AI/so-arm101-reach-isaaclab

SO-ARM101 Reaching Policy

Model Description

Model Overview

Training Details

Environment Configuration

Training Parameters

Hardware Used

Usage

Prerequisites

Evaluation

Training From Scratch

Performance

Use Cases

Citation

Related Resources

License

PathOn-AI/
so-arm101-reach-isaaclab