FractalSerl — Fractal Symmetries for Sample-Efficient Robotic Learning

Short description

FractalSERL implements Branched Euclidean Group Fractal Symmetries — a trajectory-level augmentation framework that accelerates policy learning by iteratively applying affine and Euclidean-group transformations to episodic trajectories. Treating an episodic MDP as a tree of state–action pairs, self-similar branching produces fractal symmetry expansions that populate replay buffers with diverse, consistent experiences. We demonstrate improvements on simulated and real Franka manipulation tasks, achieving robust policies in as little as 14 minutes (avg. ~20 minutes) of wall-clock training.

Contributions in this repo include:

• SymmGrid Framework: A preliminary research implementation of fractal symmetry for deep reinforcement learning, demonstrating how branched symmetries accelerate DRL policy learning in physical robots.
• Data Augmentation via Super-Scaling: Efficient robot data generation through trajectory-level augmentation that significantly speeds up policy learning while improving performance and consistency on physical hardware.
• Fractal Symmetry Replay Buffer: An Optimized Datastore and Replay Buffer implementation designed to support parallelized computations and image handling without excessive memory overhead, enabling faster training iterations.
• nAUC Performance Metric: Using normalized Area under the Curve (nAUC) as a trajectory-wide performance metric to capture combined contributions of sample efficiency and policy performance throughout training.

Navigation

The docs/ folder contains additional Markdown files with step-by-step guides. Quick links are provided below:

• Overview of code structure
• Installation guide
• Run in simulation
• Run on the real robot

Quick start (very short)

1. Install dependencies: see docs/installation.md.
2. Run a demo in sim: see docs/run_sim.md for instructions to launch franka_sim
3. For real hardware, follow the instructions in docs/run_realrobot.md and configure the files related to serl_robot_infra/.

Citation

If you use FractalSERL in your research, please cite our paper:

@misc{vanderstelt2026SymmGrid,
      title={Towards Accelerating Deep Reinforcement Learning via Branched Symmetries},
      author={Ryan Vanderstelt, Cleiver Ruiz Martinez, Caeden Rosen, Blake Hull, and Juan Rojas},
      year={2026},
      eprint={____},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

@misc{luo2024serl,
      title={SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning},
      author={Jianlan Luo and Zheyuan Hu and Charles Xu and You Liang Tan and Jacob Berg and Archit Sharma and Stefan Schaal and Chelsea Finn and Abhishek Gupta and Sergey Levine},
      year={2024},
      eprint={2401.16013},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}