Abstract:Training image-based reinforcement learning (RL) agents are sample-inefficient, limiting their effectiveness in real-world manipulation tasks. Sim2Real, which involves training in simulations and transferring to the real world, effectively reduces the dependence on real data. However, the performance of the transferred agent degrades due to the visual difference between the two environments. This research presents a low-cost segmentation-driven unsupervised RL framework (Seg-CURL) to solve the Sim2Real problem… Show more
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