mlsanityData to 14 Oct 2026

Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays

arXiv 2605.15480 ↗First posted 14 May 2026cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 16 references that are in the map. First posted on arXiv 14 May 2026.

Builds on

5 of 16 references.

References · 5

  1. 2018Residual Reinforcement Learning for Robot Controlcited by 274
  2. 2018At Human Speed: Deep Reinforcement Learning with Action Delaycited by 26
  3. 2021Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delayscited by 21
  4. 2023Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delayscited by 1
  5. 2025Contact-Safe Reinforcement Learning with ProMP Reparameterization and Energy Awarenesscited by 1

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  1. 2026Deep Reinforcement Learning for Adaptive Gain Tuning in Control of Teleoperation Manipulators with Joint Flexibility and Time-Varying Delays
  2. 2026Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey
  3. 2026Diffusion-Guided Uncertainty-Aware Delayed Policy Optimization
  4. 2025Model-Based Reinforcement Learning under Random Observation Delays
  5. 2025MEMBOT: Memory-Based Robot in Intermittent POMDP
  6. 2026Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation
  7. 2025Accelerating Residual Reinforcement Learning with Uncertainty Estimation
  8. 2026Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency