@fabiangruss B is clearer than A to me. Still I find B too subtil for first time user to directly see (had to look for some seconds even tho I knew roughly what to look for). What about slightly different color coding either for the icon or entire posts even?!
@roboticseabass@MarkusRyll Geometries and meshes could be (online) transformed into signed distance functions and similarly leveraged for collision avoidance. Not as flashy as an example tho ๐
In our Real-time Neural MPC paper, we leverage network capacities 4000x larger in optimizations. We now release L4CasADi, which enables easy integration of PyTorch models in optimizations on CPU and GPU, supporting fast C code generation and seamless integration in Acados.
@aetheru_@MarkusRyll@davsca1@drmapavone@kaufmann_elia@jonarriza96 Yes, correct! Any (differentiable and jit traceable) PyTorch model is supported (convolutions, transformers โฆ). The framework is generic and does not assume a specific robotic platform or even a robotics application: Any control system that can profit from data-driven models.
@PPuchaud@MarkusRyll@davsca1@drmapavone@kaufmann_elia@jonarriza96 Prominent examples include learning the system dynamics function as in Real-Time Neural MPC, but learning the objective or the constraints is similarly supported. Generally, a data-driven L4CasADi model can replace every symbolic formulation (MX) in CasADi.
@aks1812@MarkusRyll@davsca1@kaufmann_elia@jonarriza96@drmapavone For the gradients: while there are generally no guarantees, the worst case would be in OOD scenarios. There is lots of good work going on in OOD for NN:
https://t.co/6tIqDYMMZQ https://t.co/V2X7fHMioe
@aks1812@MarkusRyll@davsca1@kaufmann_elia@jonarriza96@drmapavone Both questions are very valid! While this paper is targeting the computational and feasibility aspect with empirical results, theoretical guarantees are an interesting research avenue. If you know prior work targeting your 2. remark (with or without DL) I would be interested.