• What is dora-rs?
• • Performance
  • Dataflow Paradigm
  • Communication
  • Message Format
  • Opentelemetry
  • Hot-Reloading
  • Self-Coding Robot: Code RAG (WIP)
  • Installation
  • Getting Started
  • ROS2 Bridge
  • Hardwares
  • Documentation
  • Discussions
  • Support Matrix
  • • Unstable functionality
  • License

Website | Python API - Rust API | Guide | Discord

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What is dora-rs?

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple.

Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to:

• make integration of hardware and software easy by supporting Python, C, C++, and also ROS2.
• make communication low latency by using zero-copy Arrow messages.

dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

Performance

dora-rs can show impressive performance, up to 17x faster compared to current status quo ROS2 in Python! This is the result of using our own shared memory server and Apache Arrow to achieve zero copy data passing.

See: https://github.com/dora-rs/dora-benchmark/tree/main for reproduction.

Dataflow Paradigm

dora-rs implements a declarative dataflow paradigm where tasks are split between nodes isolated as individual processes.

Each node defines its inputs and outputs to connect with other nodes.

nodes:
  - id: webcam
    custom:
      source: webcam.py
      inputs:
        tick: dora/timer/millis/50
      outputs:
        - image

  - id: object_detection
    custom:
      source: object_detection.py
      inputs:
        image: webcam/image
      outputs:
        - bbox

  - id: plot
    custom:
      source: plot.py
      inputs:
        image: webcam/image
        bbox: object_detection/bbox

Nodes can either be:

• custom nodes were dora-rs is embedded as a native libraries.
• runtime nodes were dora-rs takes care of the main loop and run user-defined operators. This make dora-rs featureful as we can run features like hot-reloading.

The dataflow paradigm has the advantage of creating an abstraction layer that makes robotic applications modular and easily configurable.

Communication

Communication between nodes is handled with shared memory on a same machine and TCP on distributed machines. Our shared memory implementation tracks messages across processes and discards them when obsolete. Shared memory slots are cached to avoid new memory allocation.

Message Format

Nodes communicate with Apache Arrow Data Format.

Apache Arrow is a universal memory format for flat and hierarchical data. The Arrow memory format supports zero-copy reads for lightning-fast data access without serialization overhead. It defines a C data interface without any build-time or link-time dependency requirement, that means that dora-rs has no compilation step beyond the native compiler of your favourite language.

Opentelemetry

dora-rs uses Opentelemetry to record all your logs, metrics and traces. This means that the data and telemetry can be linked using a shared abstraction.

Opentelemetry is an open source observability standard that makes dora-rs telemetry collectable by most backend such as elasticseach, prometheus, Datadog..

Opentelemetry is language independent, backend agnostic, and easily collect distributed data, making it perfect for dora-rs applications.

Hot-Reloading

dora-rs implements Hot-Reloading for python which means you can change code at runtime in Python while keeping your state intact.

Using the feature flag: --attach --hot-reload, dora-rs watch for code change and reload nodes that has been modified.
You can check fail-safe mecanism at: https://github.com/dora-rs/dora/pull/239

Self-Coding Robot: Code RAG (WIP)

You can easily create a self-coding robot, by combining Hot-reloading with a Retrieval Augmented Generation (RAG) that is going to generate code modification from your prompt.
See:examples/python-operator-dataflow

Self-Coding Robot is just the tip of the iceberg of robotics combined with llm, that we hope to power. There is so much more that we haven't explored yet like:

• self-debugging
• memory
• function calling
• ...

Installation

Quickest way:

cargo install dora-cli --locked
pip install dora-rs # For Python API

dora --help

For more info on installation, check out our guide.

Getting Started

1. Install the example python dependencies:

pip install -r https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/requirements.txt

2. Get some example operators:

wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/webcam.py
wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/plot.py
wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/utils.py
wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/object_detection.py
wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.3/examples/python-operator-dataflow/dataflow.yml

3. Start the dataflow

dora up
dora start dataflow.yml --attach --hot-reload

Make sure to have a webcam

To stop your dataflow, you can use ctrl+c

To go further, you can add a yolov8 operator, check out our getting started here: https://dora.carsmos.ai/docs/guides/getting-started/yolov8/

ROS2 Bridge

• Compilation Free Message passing to ROS 2
• Automatic conversion ROS 2 Message <-> Arrow Array

import random
import pyarrow as pa

# Configuration Boilerplate...
turtle_twist_writer = ...

## Arrow Based ROS2 Twist Message
## which does not requires ROS2 import
message = pa.array([{
            "linear": {
                "x": 1,
            },
            "angular": {
                "z": 1
            },
        }])

turtle_twist_writer.publish(message)

You might want to use ChatGPT to write the Arrow Formatting: https://chat.openai.com/share/4eec1c6d-dbd2-46dc-b6cd-310d2895ba15

Hardwares

Cool hardware that we think might be good fit to try out dora-rs 🙋 We are not sponsored by manufacturers:

	Price	Open Source	Github	type	Dora Project
DJI Robomaster S1	550$	SDK	https://github.com/dji-sdk/RoboMaster-SDK	Rover	https://huggingface.co/datasets/dora-rs/dora-robomaster
DJI Robomaster EP Core	950$	SDK	https://github.com/dji-sdk/RoboMaster-SDK	Rover, Arm
DJI Tello	100$			Drone
BitCraze Crazyflies	225$	Firmware, Lib, SDK	https://github.com/bitcraze	Drone
AlexanderKoch-Koch/low_cost_robot	250$	Everything	https://github.com/AlexanderKoch-Koch/low_cost_robot	Arm
xArm 1S	200$			Arm
Wavego	250$			Quadruplet
AINex	800$			Humanoid

For more: https://docs.google.com/spreadsheets/d/1YYeW2jfOIWDVgdEgqnMvltonHquQ7K8OZCrnJRELL6o/edit#gid=0

Documentation

The full documentation is available on our website

Discussions

Our main communication channels are:

• Our Discord server
• Our Github Project Discussion

Feel free to reach out on any topic, issues or ideas.

We also have a contributing guide.

Support Matrix

	dora-rs	Hoped for
Tier 1 Support	Python, Rust	C, C++, ROS 2
Tier 2 Support	C, C++, ROS2
Hot-reloading	Python	Rust (https://github.com/orgs/dora-rs/discussions/360)
Message Format	Arrow	Native
Local Communication	Shared Memory	Custom Middleware, zero-copy GPU IPC, intra-process tokio::channel communication
Remote Communication	TCP (See: https://github.com/dora-rs/dora/issues/459)	Custom Middleware, Zenoh
Metrics, Tracing, and Logging	Opentelemetry	Native logging libraries into Opentelemetry
Data archives	Parquet (dora-record)
Visualization and annotation	OpenCV	rerun.io
Supported Platforms (x86)	Windows, macOS, Linux
Supported Platforms (ARM)	macOS, Linux
Configuration	YAML

Unstable functionality

Some parts of the public API are marked as unstable.
You can recognize this functionality from the warning in the API reference, or from the warning issued when the configuration option warn_unstable is active.
There are a number of reasons functionality may be marked as unstable:

• We are unsure about the exact API. The name, function signature, or implementation are likely to change in the future.
• The functionality is not tested extensively yet. Bugs may pop up when used in real-world scenarios.
• The functionality does not integrate well with the full Polars API. You may find it works in one context but not in another.

Releasing functionality as unstable allows us to gather important feedback from users that use Polars in real-world scenarios.
This helps us fine-tune things before giving it the final stamp of approval.
Users are only interested in solid, well-tested functionality can avoid this part of the API.

Functionality marked as unstable may change at any point without it being considered a breaking change.

License

This project is licensed under Apache-2.0. Check out NOTICE.md for more information.