Pinned Repositories
awesome-formal-methods-robotics
carla
Open-source simulator for autonomous driving research.
conforming_example
A standalone package implementing the "conforming funnel" algorithm
drake
A planning, control, and analysis toolbox for nonlinear dynamical systems. More info at
drake-maliput
LTLMoP
A toolkit for designing and implementing LTL-based task specifications
reasyns
REActive SYnthesis for Nonlinear Systems: a Matlab toolbox
slugs
SmalL bUt Complete GROne Synthesizer
jadecastro's Repositories
jadecastro/drake-maliput
jadecastro/LTLMoP
A toolkit for designing and implementing LTL-based task specifications
jadecastro/reasyns
REActive SYnthesis for Nonlinear Systems: a Matlab toolbox
jadecastro/awesome-formal-methods-robotics
jadecastro/carla
Open-source simulator for autonomous driving research.
jadecastro/conforming_example
A standalone package implementing the "conforming funnel" algorithm
jadecastro/drake
A planning, control, and analysis toolbox for nonlinear dynamical systems. More info at
jadecastro/slugs
SmalL bUt Complete GROne Synthesizer
jadecastro/d4rl
A benchmark for offline reinforcement learning.
jadecastro/dreal3
New version of the dReal solver. More info at
jadecastro/drone-dataset-tools
The goal of this repo is to make the inD dataset as easy to use as possible. For this purpose we provide source code in Python, which allows the import and visualization of the dataset.
jadecastro/googletest
Google Test
jadecastro/imitation
Clean PyTorch implementations of imitation and reward learning algorithms
jadecastro/jadecastro.github.io
jadecastro/Sentence-VAE
PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349
jadecastro/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
jadecastro/stlcg
jadecastro/TimeOptimalPlanner
jadecastro/trajectory-transformer
Code for the paper "Offline Reinforcement Learning as One Big Sequence Modeling Problem"
jadecastro/Trajectron-plus-plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).