Learning Robot Policies from Sparse Success Signals via STL-Guided Stein Variational Policy Gradient

Learning robot policies for tasks with sparse success signals is challenging when completion depends on coordinated actions, precise contact outcomes, or satisfying several conditions together. Intricate physical interactions with the world further complicate these requirements. Prior work using con