airo-planner 0.0.6

Motion planning for single and dual robot arms.

airo-planner

Python package for single and dual robot arm motion planning.

Key motivation:

• 🔗 Provide unified interfaces for different motion planners and collision checkers, such as OMPL's powerful (but robot-agnostic) sampling-based planners and Drake's collision checking for robots.
• 🦾 Standardize and add other features taylored to robotic arm motion planning such as joint limits and planning to TCP poses.

Overview 🧾

Features: this packages provides two main things:

• 🤝 Interfaces: specify interfaces for robot arm motion planning
  • SingleArmPlanner
  • DualArmPlanner
• 🔌 Implementations: reliable and well-tested implementations of these interfaces.
  • OMPL for single and dual arm planning to joint configurations or TCP poses
  • cuRobo for single arm planning to joint configurations or TCP poses

Design goals:

• ⚓ Robustness and stability: provide an off-the-shelf motion planner that supports research by reliably covering most (not all) use cases at our labs, prioritizing dependability over niche, cutting-edge features.

• 🧩 Modularity and flexibility in the core components:

  • 🧭 Motion planning algorithms
  • 💥 Collision checker
  • 🔙 Inverse kinematics

• 🐛 Debuggability and transparency: many things can go wrong in motion planning, so we log generously and store debugging information (IK solutions, alternative paths) to troubleshoot issues.

• 🧪 Enable experimentation: Facilitate the benchmarking and exploration of experimental planning algorithms.

Planned features:

• Drake optimization-based planning

Getting started 🚀

See the getting started notebooks, where we set up:

• 🎲 OMPL for sampling-based motion planning
• 🐉 Drake for collision checking
• 🧮 ur-analytic-ik for inverse kinematics of a UR5e
• 🟢 cuRobo for GPU-accelerated motion planning

Which planner should I use?

If you have mostly static scenes, use OMPL. It’s well tested, fast, and runs on your CPU. If you have dynamic scenes that change often and have access to a CUDA-supporting GPU, use cuRobo.

Installation 🔧

Dependencies

If you want to use cuRobo with airo-planner, you first need to install it according to these instructions. Note that you will need a CUDA-enabled GPU.

Installing airo-planner

airo-planner is available on PyPI and installable with pip:

pip install airo-planner

For visualization of cuRobo worlds (see notebooks/06_curobo.ipynb), you can use the optional rerun dependency.

pip install airo-planner[rerun]

Custom robots with cuRobo

You can use the official cuRobo instructions to configure a new robot, but this requires Isaac Sim. An easier method is to fill in a YAML file with the output of bubblify.

pip install bubblify
bubblify --urdf_path /path/to/urdf --show_collision

Copy the collision_spheres section to the YAML file of your robot, and you are ready to use your own robot!

Developer guide 🛠️

See the airo-mono developer guide. A very similar process and tools are used for this package.

Releasing 🏷️

See airo-models, releasing airo-planner works the same way.