A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
Robocraft: Learning to see, simulate, and shape elasto-plastic objects with graph networks.arXiv preprint arXiv:2205.02909, 2022
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Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.