Code for simulation to reality (Sim2Real) transfer research for autonomous driving
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Updated
Apr 20, 2021 - Python
Code for simulation to reality (Sim2Real) transfer research for autonomous driving
Source code for simulating inflatable particles in voxcraft
Development of an image-based autonomous driving system for an e-FSAE.
Code to run and train a model able to autonomously drive a RC car. Using Reinforcement Learning the model is trainable on simulator. The code to drive the real car is also available
Code for the Continual Domain Randomization paper
Deep reinforcement learning for simultaneous robotic manipulation and locomotion
Sim2Real for joint robotic locomotion and manipulation with RCAN
Source code for "Evolution of Adaptive Force Chains in Reconfigurable Granular Metamaterials"
Kuka Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
Code for learning a road segmentation network. Duckietown Simulator for data generation with domain randomization: https://github.com/niksaz/randomized-duckietown
Automatic Domain Randomization (ADR) proposed in "Solving Rubik's Cube with a Robot Hand"
A panoptic segmentation deep learning architecture for sim2real autonomous driving scene understanding
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Sim2Real transfer of trained deep neural networks for OpenCat robots.
Supervisor for controlling data flow in the BenchBot software stack: https://github.com/qcr/benchbot
Manager for add-ons in the BenchBot software stack: https://github.com/qcr/benchbot
Evaluation tools for Semantic Scene Understanding with the BenchBot software stack: https://github.com/qcr/benchbot
IT's MOre than a roBOTICS SIMulator - The framework for simulating open loop kinematic robots in the best traditions of sim2real concept.
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