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arxiv: 2605.15480 · v1 · pith:5UEZA2UHnew · submitted 2026-05-14 · 💻 cs.RO · cs.AI

Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays

Kaize Deng , Zewen Yang This is my paper

Pith reviewed 2026-05-19 14:26 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords robot teleoperationstochastic delaysreinforcement learningLSTM state estimationresidual policyhybrid controlFranka Panda
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The pith

An LSTM state estimator paired with residual RL produces stable robot teleoperation under stochastic delays.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Stochastic communication delays create discontinuous signals that make conventional reinforcement learning produce unstable high-frequency chattering during robot teleoperation. The paper introduces a hybrid framework that feeds delayed observations into an LSTM network to produce smooth continuous state estimates. These estimates then support a residual RL policy that learns torque corrections balancing position tracking accuracy against velocity smoothness. Experiments on Franka Panda robots show the method outperforms existing baselines and remains stable even when delay variance is high. If the claim holds, remote robot control becomes feasible in environments with unpredictable network timing without sacrificing performance.

Core claim

The paper establishes that a delay-resilient RL framework formed by integrating an LSTM state estimator with a residual RL policy maintains control stability and performance for teleoperated robots subject to stochastic delays. The LSTM converts delayed and discontinuous observations into continuous state trajectories so the RL agent can optimize a residual torque command that trades off precise tracking against motion smoothness.

What carries the argument

The delay-resilient RL framework that combines an LSTM state estimator reconstructing continuous states from delayed observations with a residual reinforcement learning policy computing compensatory torques.

If this is right

  • The hybrid system prevents the high-frequency chattering that standard RL exhibits with delayed observations.
  • Teleoperation stays robust and stable when delay variance increases.
  • The approach achieves better results than current state-of-the-art methods on physical robot hardware.
  • Tracking accuracy and velocity smoothness remain balanced through the learned residual torque policy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same LSTM-plus-residual structure could be tested on other robots or tasks where observations arrive with variable timing.
  • If the state estimator works reliably, the residual policy might be swapped in for existing controllers without complete retraining.
  • Extending the experiments to different delay statistics would show how far the resilience generalizes.

Load-bearing premise

The LSTM can reconstruct smooth continuous states from delayed and discontinuous observations without introducing errors large enough to destabilize the residual RL policy.

What would settle it

Running the same Franka Panda teleoperation experiments under high-variance stochastic delays and finding that the proposed method produces no performance gain over baselines or exhibits instability would disprove the central claim.

Figures

Figures reproduced from arXiv: 2605.15480 by Kaize Deng, Zewen Yang.

Figure 1
Figure 1. Figure 1: Teleoperation architecture with stochastic commu [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Tracking error ∥e∥ comparison against other meth￾ods under different network delay conditions. 4.2 Quantitative Comparison in Simulation We evaluate the tracking performance by the norm of the Cartesian tracking error ∥e(t)∥ between the leader and follower end-effectors, computed over the first 50 s of each trial 3 . In [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of spatial trajectories using DR-RL in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a hybrid delay-resilient RL framework for robot teleoperation under stochastic communication delays. It integrates an LSTM-based state estimator to reconstruct smooth continuous states from delayed and discontinuous observations with a residual RL policy that learns torque compensations to balance tracking accuracy and velocity smoothness. The central claim is that this approach significantly outperforms state-of-the-art baselines and ensures robust stable teleoperation on Franka Panda robots even under high-variance stochastic delays.

Significance. If the empirical results hold with proper quantitative support, the work addresses a practical issue in networked robotics and could improve stability in teleoperation tasks subject to variable network delays. The combination of LSTM reconstruction and residual policy builds on standard components without evident circularity or parameter fitting that reduces the result to a tautology.

major comments (2)
  1. [Abstract / Experimental validation] Abstract and experimental validation section: the claim that the approach 'significantly outperforms the state-of-the-art baselines' supplies no quantitative metrics (e.g., tracking RMSE, velocity smoothness, success rates), no description of the delay model or variance levels, no baseline implementations, and no statistical tests or ablations. This leaves the central empirical claim unevaluable and under-supported.
  2. [Method / LSTM State Estimator] LSTM state estimator description: the framework assumes the LSTM produces sufficiently accurate smooth estimates so that the residual torque policy remains stable, yet the manuscript provides no reconstruction error bounds, RMSE analysis, worst-case delay ablation, or sensitivity study demonstrating that errors stay below the threshold that would induce chattering or instability in the RL policy when delay variance exceeds the training distribution.
minor comments (1)
  1. [Method] Notation for the residual policy and LSTM input/output dimensions should be defined explicitly with a diagram or equations to clarify how delayed observations are fed into the estimator.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback, which highlights important areas for strengthening the empirical support and robustness analysis in our work. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract / Experimental validation] Abstract and experimental validation section: the claim that the approach 'significantly outperforms the state-of-the-art baselines' supplies no quantitative metrics (e.g., tracking RMSE, velocity smoothness, success rates), no description of the delay model or variance levels, no baseline implementations, and no statistical tests or ablations. This leaves the central empirical claim unevaluable and under-supported.

    Authors: We agree that the presentation of results would be strengthened by explicit quantitative metrics and supporting details. In the revised manuscript, we will expand the experimental validation section to report specific values for tracking RMSE, velocity smoothness metrics, and success rates. We will also include a clear description of the stochastic delay model and variance levels tested, details on baseline implementations, and results from statistical tests along with ablations. These additions will make the performance claims more transparent and directly evaluable. revision: yes

  2. Referee: [Method / LSTM State Estimator] LSTM state estimator description: the framework assumes the LSTM produces sufficiently accurate smooth estimates so that the residual torque policy remains stable, yet the manuscript provides no reconstruction error bounds, RMSE analysis, worst-case delay ablation, or sensitivity study demonstrating that errors stay below the threshold that would induce chattering or instability in the RL policy when delay variance exceeds the training distribution.

    Authors: We acknowledge the value of a more detailed robustness analysis for the LSTM estimator. We will add RMSE metrics for state reconstruction, worst-case delay ablations, and sensitivity studies examining performance when delay variance exceeds the training distribution, along with discussion of observed error levels and their relation to policy stability. Theoretical error bounds are not provided in the current empirical framework and would require substantial additional theoretical development. revision: partial

standing simulated objections not resolved
  • Deriving theoretical reconstruction error bounds for the LSTM state estimator under arbitrary stochastic delay distributions.

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The manuscript proposes a hybrid framework that combines a standard LSTM state estimator with a residual RL policy to handle stochastic delays in teleoperation. No equations, fitted parameters, or self-citations are presented in the provided text that reduce the central claims or performance results to definitions or inputs by construction. The approach is described as building on established LSTM and RL components, with experimental validation on Franka Panda robots serving as independent evidence rather than a tautological restatement of the method itself. The derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that LSTM networks can produce usable continuous state estimates from delayed observations and that residual RL can learn stable compensatory policies; no free parameters or new entities are introduced in the abstract.

axioms (1)
  • domain assumption LSTM networks can reconstruct smooth continuous states from delayed and discontinuous observations sufficiently well for downstream control.
    This premise underpins the state-estimator component and is required for the residual policy to receive usable inputs.

pith-pipeline@v0.9.0 · 5654 in / 1324 out tokens · 52003 ms · 2026-05-19T14:26:07.129188+00:00 · methodology

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Reference graph

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