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PAKT, a framework for kinesthetic teaching in RL, adds a high-performance control stack that maps low-frequency RL actions to high-frequency torque commands and preserves the tracking performance of the impedance controller while improving contact handling and producing smoother policy actions.
By combining simulation-trained priors with real-world adaptation, SPARR achieves near-perfect success in robotic assembly without human supervision, outperforming both sim-to-real and real-world RL baselines.