jiminy
jiminy copied to clipboard
Jiminy: a fast and portable Python/C++ simulator of poly-articulated robots with OpenAI Gym interface for reinforcement learning
 I suppose the text documents an overload to the function, without being sure. Also, is the `[ ]` nesting to indicate optionality correct ? This one indicates that `load_visual_meshes`...
Currently, there is no toolbox with various pre-implemented toolboxes for termination conditions and reward components. It would be nice to provide some highly optimized yet modular implementations of the most...
The data holders (`SensorSharedDataHolder_t` ...) should be refactored to be actual classes managing set of sensors, and as such, being friend of the corresponding abstract class (`AbstractSensorBase` ...). It should...
- Add option to disable extra computations - Add option to update less frequently the formulation of the constrained problem (the pose of the contact points, the set of active...
Beware all deps must be pinned to a specific version. Note that boost version requirement is incompatible between the binaries provided by `apt` `robotpkg` repository and the one deployed by...
The point is to avoid copy when wrapping temporaries, which typically happens the case when parsing log files [here](https://github.com/duburcqa/jiminy/blob/04511dd1bf8bea91f3af054db3f78d2775a0470c/python/jiminy_pywrap/src/engine.cc#L710) and wrapping some methods. Consider using `boost::python::numpy` instead raw C-Python API...
In python/gym_jiminy/rllib/gym_jiminy/rllib/callbacks.py, line 45: ` episode.hist_data.setdefault(key, []).append(value) ` if `value` is not of native type (i.e. for instance if it is numpy.float or numpy.int), it can create significant bottlenecks resulting...
MurmurHash is great but x10 performance improvement can be expected by replacing it by [xxHash](https://github.com/Cyan4973/xxHash) instead.
We should get rid of `use_theoretical_model` everywhere to make everything more simple. The user would be responsible for converting the state for flexible to rigid or the other way around...
https://github.com/humanoid-path-planner/hpp-fcl/issues/376