Repository Summary
| Description | Inverse Kinematics solver for MoveIt |
| Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-01-14 |
| Dev Status | DEVELOPED |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| pick_ik | 1.1.1 |
README
pick_ik
pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik, which combines:
- A local optimizer which solves inverse kinematics via gradient descent
- A global optimizer based on evolutionary algorithms
Critically, pick_ik allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik, the functionality in this package includes:
- Reimplementation of the memetic solver (equivalent to
bio1andbio2_memeticsolvers) - Reimplementation of the numeric gradient descent solvers (equivalent to
gd,gd_r, andgd_csolvers) - Fully configurable number of threads if using the global solver
- Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik, refer to the following README files:
CONTRIBUTING
Repository Summary
| Description | Inverse Kinematics solver for MoveIt |
| Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-01-14 |
| Dev Status | DEVELOPED |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| pick_ik | 1.1.1 |
README
pick_ik
pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik, which combines:
- A local optimizer which solves inverse kinematics via gradient descent
- A global optimizer based on evolutionary algorithms
Critically, pick_ik allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik, the functionality in this package includes:
- Reimplementation of the memetic solver (equivalent to
bio1andbio2_memeticsolvers) - Reimplementation of the numeric gradient descent solvers (equivalent to
gd,gd_r, andgd_csolvers) - Fully configurable number of threads if using the global solver
- Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik, refer to the following README files:
CONTRIBUTING
Repository Summary
| Description | Inverse Kinematics solver for MoveIt |
| Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-01-14 |
| Dev Status | DEVELOPED |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| pick_ik | 1.1.1 |
README
pick_ik
pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik, which combines:
- A local optimizer which solves inverse kinematics via gradient descent
- A global optimizer based on evolutionary algorithms
Critically, pick_ik allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik, the functionality in this package includes:
- Reimplementation of the memetic solver (equivalent to
bio1andbio2_memeticsolvers) - Reimplementation of the numeric gradient descent solvers (equivalent to
gd,gd_r, andgd_csolvers) - Fully configurable number of threads if using the global solver
- Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik, refer to the following README files:
CONTRIBUTING
Repository Summary
| Description | Inverse Kinematics solver for MoveIt |
| Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-01-14 |
| Dev Status | DEVELOPED |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| pick_ik | 1.1.1 |
README
pick_ik
pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik, which combines:
- A local optimizer which solves inverse kinematics via gradient descent
- A global optimizer based on evolutionary algorithms
Critically, pick_ik allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik, the functionality in this package includes:
- Reimplementation of the memetic solver (equivalent to
bio1andbio2_memeticsolvers) - Reimplementation of the numeric gradient descent solvers (equivalent to
gd,gd_r, andgd_csolvers) - Fully configurable number of threads if using the global solver
- Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik, refer to the following README files:
CONTRIBUTING
Repository Summary
| Description | Inverse Kinematics solver for MoveIt |
| Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-01-14 |
| Dev Status | DEVELOPED |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| pick_ik | 1.1.1 |
README
pick_ik
pick_ik is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik, which combines:
- A local optimizer which solves inverse kinematics via gradient descent
- A global optimizer based on evolutionary algorithms
Critically, pick_ik allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik, the functionality in this package includes:
- Reimplementation of the memetic solver (equivalent to
bio1andbio2_memeticsolvers) - Reimplementation of the numeric gradient descent solvers (equivalent to
gd,gd_r, andgd_csolvers) - Fully configurable number of threads if using the global solver
- Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik, refer to the following README files: