S2P learns separate location and insertion primitives simultaneously via visual RL for peg-in-hole tasks, improving sample efficiency and success rates across polygon benchmarks in simulation and real-world tests.
Reinforcement Learning of Impedance Policies for Peg-in-Hole Tasks: Role of Asymmetric Matrices,
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A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks
S2P learns separate location and insertion primitives simultaneously via visual RL for peg-in-hole tasks, improving sample efficiency and success rates across polygon benchmarks in simulation and real-world tests.