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Applies Deep Reinforcement Learning with domain randomization and Bayesian optimization to enable sim-to-real transfer for robotic arm manipulation in radiochemical environments.
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The project addresses a highly specialized niche (radiochemical workflows) using established sim-to-real techniques. However, with zero stars, forks, or activity, it currently functions as a personal experiment or research artifact rather than a maintained tool. It lacks the community traction or technical moats required for a higher defensibility score.
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