semvec/pypi-packages huggingface dataset:
dm-robotics-transformations 0.10.0
A library for Rigid Body Transformations
License: Apache 2.0

# DeepMind Robotics Transformations

Transformations is a pure python library for rigid-body transformations
including velocities and forces.

The objectives for this library are **simplicity** and **comprehensiveness**
across all canonical representations (euler, axis-angle, quaternion,
homogeneous matrices).


## Supported conversions:
* Quaternion to Rotation matrix, Axis-angle and Euler-angle
* Axis-angle to Quaternion, Rotation matrix and Euler-angle
* Rotation matrix to Quaternion, Axis-angle and Euler-angle
* Euler-angle to Quaternion, Rotation matrix and Axis-angle

## Quaternions:
Quaternions are represented with the scalar part (w) first, e.g.

```python
identity_quaternion = np.asarray([1, 0, 0, 0])  # w, i, j, k
```

Supported quaternion operations:

* Difference
* Distance
* Multiplication
* Inverse
* Conjugate
* Logarithm and Exponent
* Slerp (spherical linear interpolation)
* Rotation of a vector by a quaternion.

## Euler-angles
All 24 from-euler orderings are supported.
7 of 24 to-euler orderings are supported.

## Transforms
This library supports force and velocity transforms.

## Usage Example

```python
from dm_robotics.transformations import transformations as tr

# Convert a pose, euler angle into a homogeneous matrix (a 4x4 matrix):
hmat = tr.poseuler_to_hmat(
        np.array([x, y, z, rot_x, rot_y, rot_z]), 'XYZ')

# Convert the homogeneous matrix to a twist (a 6 vector):
twist = tr.hmat_to_twist(hmat)
```
dm-robotics-manipulation 0.10.0
Parametrically defined mesh objects
License: Apache 2.0
Requires Python: <3.13,>=3.9

# Objects for robotic manipulation

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This folder provides various physical objects and software utilities used to
build simulated environments for robotic manipulation.

#### Cell for robotic manipulation
Contains assets and code to create a robotic cell in simulated environments.

#### Parametric objects documentation
Infrastructure to design families of parametric objects.

#### [RGB-objects &#128721;&#129001;&#128311; documentation](props/rgb_objects/README.md)
Contains assets and supported code that allows using the RGB-objects in
simulated environments. The RGB-objects is a set of parametric objects crafted
for [RGB-stacking][rgb_stacking] manipulation tasks.

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[rgb_stacking]: https://github.com/deepmind/rgb_stacking
dm-robotics-agentflow 0.10.0
Tools for single-embodiment, multiple-task, Reinforcement Learning
License: Apache 2.0
Requires Python: <3.13,>=3.9

# AgentFlow: A Modular Toolkit for Scalable RL Research

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## Overview

`AgentFlow` is a library for composing Reinforcement-Learning agents. The core
features that AgentFlow provides are:

1.  tools for slicing, transforming, and composing *specs*
2.  tools for encapsulating and composing RL-tasks.

Unlike the standard RL setup, which assumes a single environment and an agent,
`AgentFlow` is designed for the single-embodiment, multiple-task regime. This
was motivated by the robotics use-case, which frequently requires training RL
modules for various skills, and then composing them (possibly with non-learned
controllers too).

Instead of having to implement a separate RL environment for each skill and
combine them ad hoc, with `AgentFlow` you can define one or more `SubTasks`
which *modify* a timestep from a single top-level environment, e.g. adding
observations and defining rewards, or isolating a particular sub-system of the
environment, such as a robot arm.

You then *compose* SubTasks with regular RL-agents to form modules, and use a
set of graph-building operators to define the flow of these modules over time
(hence the name `AgentFlow`).

The graph-building step is entirely optional, and is intended only for use-cases
that require something like a (possibly learnable, possibly stochastic)
state-machine.

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### [Components](docs/components.md)
### [Control Flow](docs/control_flow.md)
### [Examples](docs/examples.md)
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