uv pip install rfx-sdk

Three commands. Any robot. Any policy.

rfx record --robot so101 --repo-id my-org/demos --episodes 10
rfx deploy runs/my-policy --robot so101
rfx deploy hf://user/my-policy --robot go2 --duration 60

ROS was built for message passing between components. We're in a different era -- the workflow is collect demos, train a policy, deploy, iterate. rfx is built from scratch for that loop.

• Three commands -- rfx record, rfx deploy, rfx doctor -- that's the whole CLI
• Three-method robot interface -- observe(), act(), reset() -- same API for sim and real
• Self-describing models -- save once, load anywhere, deploy with zero config
• HuggingFace Hub native -- push and pull policies like you push datasets
• Rust core for real-time control, Python SDK for fast research
• Zenoh transport underneath -- invisible plumbing, there when you need it

uv pip install rfx-sdk

Or with pip:

pip install rfx-sdk

From source:

git clone https://github.com/quantbagel/rfx.git && cd rfx
bash scripts/setup-from-source.sh

All CLI commands can also be run directly with uv:

uv run rfx deploy runs/my-policy --robot so101
uv run rfx record --robot so101 --repo-id demos --episodes 10

Collect teleoperation demonstrations into a LeRobot dataset:

rfx record --robot so101 --repo-id my-org/demos --episodes 10

# Or from Python
rfx.collection.collect("so101", "my-org/demos", episodes=10)

Load a trained policy and run it on hardware. One command:

# From a saved checkpoint
rfx deploy runs/my-policy --robot so101

# From HuggingFace Hub
rfx deploy hf://rfx-community/go2-walk-v1 --robot go2

# From a Python file with @rfx.policy
rfx deploy my_policy.py --robot so101

# Test without hardware
rfx deploy runs/my-policy --robot so101 --mock

# Or from Python
rfx.deploy("runs/my-policy", robot="so101")
rfx.deploy("hf://user/policy", robot="go2", duration=30)

Deploy handles everything: load weights, resolve robot config, connect hardware, run the control loop with rate control and jitter tracking, clean shutdown on Ctrl+C.

Every robot -- simulated or real -- implements three methods:

obs = robot.observe()    # {"state": Tensor(1, 64), "images": ...}
robot.act(action)        # Tensor(1, 64)
robot.reset()

A policy is any callable Dict[str, Tensor] -> Tensor. Use @rfx.policy to make it deployable from the CLI:

# my_policy.py
import torch
import rfx

@rfx.policy
def hold_position(obs):
    return torch.zeros(1, 64)  # hold still

rfx deploy my_policy.py --robot so101

For named joint control instead of raw tensor indices:

@rfx.policy
def grasp(obs):
    return rfx.MotorCommands(
        {"gripper": 0.8, "wrist_pitch": -0.2},
        config=rfx.SO101_CONFIG,
    ).to_tensor()

Every saved model is a self-describing directory:

policy.save("runs/go2-walk-v1",
    robot_config=config,
    normalizer=normalizer,
    training_info={"total_steps": 50000})

runs/go2-walk-v1/
  rfx_config.json       # architecture + robot + training metadata
  model.safetensors     # weights
  normalizer.json       # observation normalizer state

Push to Hub, load anywhere:

rfx.push_policy("runs/go2-walk-v1", "rfx-community/go2-walk-v1")
loaded = rfx.load_policy("hf://rfx-community/go2-walk-v1")

Custom robots: implement observe() / act() / reset() or write a YAML config.

robot = rfx.SimRobot.from_config("so101.yaml", backend="genesis", viewer=True)
robot = rfx.SimRobot.from_config("go2.yaml", backend="mjx", num_envs=4096)
robot = rfx.MockRobot(state_dim=12, action_dim=6)  # zero deps, for testing

• Full documentation
• SO-101 quickstart
• Simulation guide
• Python SDK reference

• Issues
• Discussions
• Discord

MIT