RT-HCP combines a physics-informed model, multi-step planning, and an actor-critic policy to learn a swing-up controller on a real Furuta pendulum under strict time and sample limits.
pytorch implementation of PETS,
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RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms
RT-HCP combines a physics-informed model, multi-step planning, and an actor-critic policy to learn a swing-up controller on a real Furuta pendulum under strict time and sample limits.