Development releases#

Version 0.2.0#

2024, January 4th

First and foremost, this update moves motornet from tensorflow to pytorch. There has been systematic requests for a pytorch implementation of this package, and over time it is becoming clear that this will enable better integration with existing research efforts from the scientific community that this package is aiming to help. As a consequence, many API changes and change in the code structure were made, as the logical structure of pytorch is fundamentally different than that of tensorflow. These changes are further detailed below.

  • Renamed the motornet.plants package to effector and the motornet.plants.Plant class to Effector, as ‘plant’ is a specific engineering term and may be overly arcanic to a more general audience. Generally, the swap from “plant” to “effector” has been enacted consistently in the text and code.

  • Task object essentially perform computations pertaining to environment objects in typical simulation software for machine learning. Therefore, the motornet.tasks module has been renamed motornet.environment and the Task base class has been renamed to Environment. This is also now a subclass of gymnasium‘s gymnasium.Env class, and it shares its API convention. The motivation behind these changes is that gymnasium is a popular interfacing package for simulation environments in machine learning, and standardizing motornet‘s API according to gymnasium will enable wider cross-compatibility, as well as facilitate familiarization efforts from a lot of researchers already accustomed to gymnasium‘s API. Users are strongly encouraged to check the updated tutorial notebooks on motornet‘s GitHub repository and on the online documentation website for more detailed explanation of the new Environment API, if they are not alredy familiar with gymnasium. Generally, the swap from “task” to “environment” has been enacted consistently in the text and code.

  • Pytorch does not require the creation of end-to-end model objects as tensorflow does. Consequently, motornet pipelines only require setting up an Effector and wrapping it up in an Environment object, without having to create a Network object at all. Feedback delays and Gaussian noise are now handled directly by the Environment class.

  • Removed all sub-packages in motornet. The pytorch implementation allows users to create their own loss and network objects the way they typically would for any project beyond motornet, removing the need for a complex sub-packaging structure differentiating between set of modules falling under the nets or effectors category. Therefore, motornet now only contains modules. For instance, the motornet.effector.muscle.Muscle class is now directly accessible as motornet.muscle.Muscle.

  • The motornet.plotor.plot_pos_over_time() function now takes cartesian position as argument rather than full cartesian states that include positions and velocities. In practice, the velocities were always discarded by that function so we removed this step to allow for a more transparent and intuitive function syntax.

  • The muscle_type argument of the motornet.effector.Effector class has been renamed to muscle for conciseness.

  • The term excitation is now replaced by action to better match the terminology in place in continuous control machine learning. Note that action and activation are not the same variables.

  • Added a motornet.effector.muscles.MujocoHillMuscle class to the muscle module. This object instantiates MuJoCo’s Hill-type muscle as described in the MuJoCo documentation.

  • The file has been removed, as the means of streamlining model training pipelines usually boils down to personal preference.

  • Users can now seed their Environment and Effector classes. Seeding is an important aspect of reproducible programming, and is usually considered a “best practice”. Since the Environment and Effector classes are the only classes that make use of a random generator, these are the only classes that currently require seeding in motornet.

  • All motornet objects now inherit from the torch.nn.Module class. Amongst other things, this allows easy device assignment for model parameters, using pytorch‘s usual .to(device) method.

  • Renamed the muscles, skeletons, effectors, and environments modules to muscle, skeleton, effector, and environment for conciseness.

Version 0.1.5#

2023, February 19th

  • Fixed a typo for a parameter value in the mn.plants.muscles.RigidTendonHillMuscleThelen class, from 0.66 to 0.6. This parameter was epsilon_0^M in equation 3 of the main reference (Thelen, 2003).

  • Random noise is now correctly applied to gated recurrent units in the mn.nets.layers.GRUNetwork.forward_pass() method. Specifically, it is now applied before the non-linearity is applied rather than after.

Version 0.1.4#

2022, November 8th

  • Added an attribute alias object at mn.utils.Alias that allows users declare transparent aliases to object attributes.

  • Declared an alias state_names for output_names in the mn.nets.Network base class.

  • Fixed the first state_name of ReluMuscle class from excitation/activation to activation, as excitation and activation are actually distinct variables. See that class’ documentation for details.

Version 0.1.3#

2022, October 30th

  • Fixed a bug which would prevent some new custom models from compiling due to mismatched sequence duration.

  • Added a ClippedPositionLoss, which penalizes positional error unless the radial distance to the desired position is less than a user-defined radius (target size) around said desired position (see documentation in the mn.nets.losses module for more details).

  • The plot_pos_over_time function in the mn.utils.plotor module can now take colormaps as a keyword argument (see documentation for details.)

  • Removed a numpy.ndarray from CenterOutReach attributes to allow for JSON serialization when saving models.

  • Added a warning in Task base class to inform users when their task contains a numpy.ndarray as attribute. This is to make them aware that it might raise an error when saving models due to numpy.ndarray not being JSON serializable.

  • Fixed an error in mn.plants.Plant.get_muscle_cfg() which occured when the method is called and the add_muscle method was not called before.

Version 0.1.2#

2022, July 31st

  • Some optional arguments, and associated attributes for mn.plants.skeletons.TwoDofArm at initialization are now case-insensitive.

  • Removed a typo that resulted in printing of some state shapes in mn.plants.skeletons.TwoDofArm.path2cartesian() method.

Version 0.1.1#

2022, June 4th

  • Fixed to allow for solving the tensorflow dependency on M1-chip equipped Apple devices. Instead of asking for tensorflow as a requirement upon pip install calls, motornet will now ask for the M1-compatible version of TensorFlow on machines equipped with M1 chips, which is tensorflow-macos.

Version 0.1.0#

2022, June 3th

  • Initial release.