pick_ik Kinematics Solver

pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2, developed by PickNik Robotics. It is designed to provide robust and customizable IK solutions, offering a wide range of features.

The solver in pick_ik is a reimplementation of bio_ik, integrating a global optimizer and a local optimizer. The global optimizer utilizes evolutionary algorithms to explore alternative solutions within the solution space and identify global optima. Building upon the results obtained by the global optimizer, the local optimizer applies gradient descent for iterative refinement of the solution. It takes a potential optimum solution provided by the global optimizer as input and aims to improve its accuracy and ultimately convergence to the most optimum solution.

Getting Started

Before proceeding, please ensure that you have completed the steps outlined in the Getting Started Guide.

Additionally, it is required to have a MoveIt configuration package specifically tailored to your robot. This package can be created using the MoveIt Setup Assistant.


From binaries

Make sure your ROS 2 installation is sourced and run the following command

sudo apt install ros-$ROS_DISTRO-pick-ik

From source

Create a colcon workspace.

export COLCON_WS=~/ws_moveit2/
mkdir -p $COLCON_WS/src

Clone this repository in the src directory of your workspace.

cd $COLCON_WS/src
git clone -b main https://github.com/PickNikRobotics/pick_ik.git

Set up colcon mixins.

sudo apt install python3-colcon-common-extensions
sudo apt install python3-colcon-mixin
colcon mixin add default https://raw.githubusercontent.com/colcon/colcon-mixin-repository/master/index.yaml
colcon mixin update default

Build the workspace.

cd /path/to/your/workspace
colcon build --mixin release


Using pick_ik as a Kinematics Plugin

You can use MoveIt Setup Assistant to create the configuration files for your robot to use it with MoveIt, or edit the kinematics.yaml file in your robot’s config directory to use pick_ik as the IK solver.

    kinematics_solver: pick_ik/PickIkPlugin
    kinematics_solver_timeout: 0.05
    kinematics_solver_attempts: 3
    mode: global
    position_scale: 1.0
    rotation_scale: 0.5
    position_threshold: 0.001
    orientation_threshold: 0.01
    cost_threshold: 0.001
    minimal_displacement_weight: 0.0
    gd_step_size: 0.0001


You can launch a preconfigured demo with pick_ik using the following command:

ros2 launch moveit2_tutorials demo_pick_ik.launch.py

The command starts a demo similar to the one in MoveIt Quickstart in RViz tutorial. However, this demo specifically utilizes the robot kinematics configuration file kinematics_pick_ik.yaml located at the moveit2_tutorials/doc/pick_ik/config directory here.

Parameter Description

For an exhaustive list of parameters, refer to the parameters YAML file.

Some key parameters you may want to start with are:

  • mode: If you choose local, this solver will only do local gradient descent; if you choose global, it will also enable the evolutionary algorithm. Using the global solver will be less performant, but if you’re having trouble getting out of local minima, this could help you. We recommend using local for things like relative motion / Cartesian interpolation / endpoint jogging, and global if you need to solve for goals with a far-away initial condition.

  • memetic_<property>: All the properties that only kick in if you use the global solver. The key one is memetic_num_threads, as we have enabled the evolutionary algorithm to solve on multiple threads.

  • position_threshold / orientation_threshold: Optimization succeeds only if the pose difference is less than these thresholds in meters and radians respectively. A position_threshold of 0.001 would mean a 1 mm accuracy and an orientation_threshold of 0.01 would mean a 0.01 radian accuracy.

  • cost_threshold: This solver works by setting up cost functions based on how far away your pose is, how much your joints move relative to the initial guess, and custom cost functions you can add. Optimization succeeds only if the cost is less than cost_threshold. Note that if you’re adding custom cost functions, you may want to set this threshold fairly high and rely on position_threshold and orientation_threshold to be your deciding factors, whereas this is more of a guideline.

  • approximate_solution_position_threshold / approximate_solution_orientation_threshold: When using approximate IK solutions for applications such as endpoint servoing, pick_ik may sometimes return solutions that are significantly far from the goal frame. To prevent issues with such jumps in solutions, these parameters define maximum translational and rotation displacement. We recommend setting this to values around a few centimeters and a few degrees for most applications.

  • position_scale: If you want rotation-only IK, set this to 0.0. If you want to solve for a custom IKCostFn (which you provide in your setFromIK() call), set both position_scale and rotation_scale to 0.0. You can also use any other value to weight the position goal; it’s part of the cost function. Note that any checks using position_threshold will be ignored if you use position_scale = 0.0.

  • rotation_scale: If you want position-only IK, set this to 0.0. If you want to treat position and orientation equally, set this to 1.0. You can also use any value in between; it’s part of the cost function. Note that any checks using orientation_threshold will be ignored if you use rotation_scale = 0.0.

  • minimal_displacement_weight: This is one of the standard cost functions that checks for the joint angle difference between the initial guess and the solution. If you’re solving for far-away goals, leave it to zero or it will hike up your cost function for no reason. Have this to a small non-zero value (e.g., 0.001) if you’re doing things like Cartesian interpolation along a path or endpoint jogging for servoing.

You can test out this solver live in RViz, as this plugin uses the generate_parameter_library package to respond to parameter changes at every solve. This means that you can change values on the fly using the ROS 2 command-line interface, e.g.,

ros2 param set /rviz2 robot_description_kinematics.panda_arm.mode global

ros2 param set /rviz2 robot_description_kinematics.panda_arm.minimal_displacement_weight 0.001