what is regrind fr?

REGRIND is a research project for teaching robot hands to manipulate objects using reinforcement learning, a training method where an AI system improves by trial and error rather than being explicitly programmed. It is the code that goes along with a paper called A Minimalist Retargeting Guided Reinforcement Learning Recipe for Dexterous Manipulation, and it focuses specifically on dexterous manipulation, meaning fine, hand-like movements such as picking up and using tools like scissors or a screwdriver. The project works in two main stages. First, human hand motions are retargeted, meaning translated, into movements a specific robot hand can perform, using recorded human demonstrations of using an object. Retargeted example movements are already included in the repository, so someone can skip straight to training if they want. Second, those retargeted movements guide a reinforcement learning process that trains a simulated robot hand, either a LeapHand or a WujiHand model, to perform the task on its own in a physics simulator. Everything runs inside NVIDIA's IsaacSim and IsaacLab robotics simulation software, which needs to be installed separately before this project will work. Training happens with thousands of simulated environments running in parallel to speed up learning, and results can optionally be tracked using a tool called wandb. The retargeting step also depends on an optimization library called Drake, which by default uses a solver called Mosek that requires a free academic or trial license, though a license-free alternative solver is also supported.