Upload SO-ARM101 reaching policy (999 iterations)

README.md

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+---
+language: en
+license: mit
+tags:
+- reinforcement-learning
+- robotics
+- isaac-lab
+- manipulation
+- so-arm101
+- inverse-kinematics
+library_name: rsl-rl
+---
+
+# SO-ARM101 Reaching Policy
+
+This model is a reinforcement learning policy trained for the **SO-ARM101** robot arm to perform end-effector reaching tasks in Isaac Lab.
+
+## Model Description
+
+- **Task**: Move the end-effector to randomly sampled target poses in 3D space
+- **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-Reach-v0`
+- **Training**: 999 iterations with 4096 parallel environments
+
+## Model Overview
+
+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
+
+- [Isaac Lab Documentation](https://isaac-sim.github.io/IsaacLab/)
+- [SO-ARM101 Isaac Lab Integration](https://github.com/MuammerBay/isaac_so_arm101)
+- [RSL-RL Library](https://github.com/leggedrobotics/rsl_rl)
+
+## License
+
+MIT License