An LSTM state estimator paired with a residual RL policy enables robust robot teleoperation under stochastic delays by reconstructing continuous states and learning compensatory torques, outperforming baselines on Franka Panda robots.
(1994).Teleoperation and Robotics in Space (In- genieria Mecanica y Maquinaria)
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Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays
An LSTM state estimator paired with a residual RL policy enables robust robot teleoperation under stochastic delays by reconstructing continuous states and learning compensatory torques, outperforming baselines on Franka Panda robots.