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Recent advancements in GPU-accelerated simulation have made it possible to train reinforcement learning policies for robotic control using large-scale parallel data collection. This thesis explores the pipeline from task description to deployment of control policies on the robot platform Spot. The focus is on two important skills needed for future loco-manipulation tasks: robust standing and comma
