[2605.15480] Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays
[Submitted on 14 May 2026]
Title:Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays
View a PDF of the paper titled Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays, by Kaize Deng and 1 other authors
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Abstract:Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Consequently, the conventional reinforcement learning (RL) methods struggle with the delayed observations due to the delay-induced observations, leading to high-frequency chattering. To address this, we propose a hybrid control framework, delay-resilient RL, integrating a state estimator utilizing Long Short-Term Memory (LSTM) with a residual RL policy, which is resilient to stochastic delays. The LSTM reconstructs smooth, continuous state estimates from delayed observations, enabling the RL agent to learn a residual torque compensation policy that balances tracking accuracy with velocity smoothness. Experimental validation on Franka Panda robots demonstrates that our approach significantly outperforms the state-of-the-art baselines, ensuring robust and stable teleoperation even under high-variance stochastic delays.
Comments: Accepted at 23rd IFAC World Congress 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.15480 [cs.RO]
(or arXiv:2605.15480v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.15480
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zewen Yang [view email] [v1] Thu, 14 May 2026 23:45:59 UTC (1,064 KB)
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