Telekinesis - RLbotics
Telekinesis RLbotics is a lightweight, GPU-accelerated PyTorch library for Reinforcement Learning. It supports multi-environment training across Gymnasium, mjlab, and Isaac Lab, common learning algorithms, ONNX export, and deployment with NumPy alone.
Open source under Apache 2.0.
Full documentation: Telekinesis Agentic OS: RLbotics.
Requirements
- Python 3.10–3.12
- PyTorch. To match a specific CUDA toolkit, install it first:
pip install torch --index-url https://download.pytorch.org/whl/cu128
Quickstart
Gymnasium - (requirements: any OS, no GPU)
pip install "telekinesis-rlbotics[gym]"
python examples/training_example.py configs/gymnasium/Humanoid-v5.yaml
mjlab - (requirements: Linux/Windows, NVIDIA GPU)
pip install "telekinesis-rlbotics[mjlab]" "mjlab[cu128]"
python examples/training_example.py configs/mjlab/Mjlab-Velocity-Flat-Unitree-G1.yaml
Isaac Lab - (requirements: Linux/Windows, NVIDIA GPU, Python 3.11)
pip install "telekinesis-rlbotics[isaaclab]" --extra-index-url https://pypi.nvidia.com
python examples/training_example.py configs/isaaclab/Isaac-Velocity-Flat-Anymal-C-v0.yaml
tensorboard --logdir logs to watch any of them learn. More tasks in configs/<framework>/.
Options
| Option | What it does |
|---|---|
-n, --num-envs |
Parallel environments |
-d, --device |
auto, cpu, mps, or cuda |
-i, --num-learning-iterations |
Iterations to train for |
--log-dir |
Where logs, checkpoints and videos go |
--resume [WHICH] |
Continue from last, best, or a checkpoint path |
python examples/training_example.py --help for details.
Deployment
Training exports the best checkpoint to one self-contained policy.onnx. Run it with just numpy and
onnxruntime:
from telekinesis.rlbotics.policy import Policy
policy = Policy("logs/gymnasium_ppo/2026-08-06_18-08-47/policy.onnx")
action = policy.get_action(observation) # (obs_dim,) -> (num_actions,)
Documentation
Find the documentation for RLBotics at: Telekinesis Agentic OS: RLbotics
Citation
@software{telekinesis_rlbotics,
author = {Telekinesis GmbH},
title = {Telekinesis-Rlbotics: Reinforcement Learning for Robotics},
year = {2026},
url = {https://github.com/telekinesis-ai/telekinesis-rlbotics},
note = {Apache-2.0}
}
1 maintainer
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