# Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays

arXiv: https://arxiv.org/abs/2605.15480 · HTML: https://mlsanity.com/paper/2605.15480/

## 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.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 5 of 5)

- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [At Human Speed: Deep Reinforcement Learning with Action Delay](https://mlsanity.com/paper/1810.07286/) (2018)
- [Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays](https://mlsanity.com/paper/2108.07555/) (2021)
- [Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays](https://mlsanity.com/paper/2305.16979/) (2023)
- [Contact-Safe Reinforcement Learning with ProMP Reparameterization and Energy Awareness](https://mlsanity.com/paper/2511.13459/) (2025)

## 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.