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arxiv: 2604.10165 · v1 · submitted 2026-04-11 · 💻 cs.RO

Recognition: unknown

MoRI: Mixture of RL and IL Experts for Long-Horizon Manipulation Tasks

Gewei Zuo, Han Ding, Lianjie Ma, Lijun Zhu, Wentao Zhang, Yaohang Xu

Pith reviewed 2026-05-10 15:56 UTC · model grok-4.3

classification 💻 cs.RO
keywords robotic manipulationreinforcement learningimitation learninghybrid learninglong-horizon tasksaction variancepolicy fine-tuning
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0 comments X

The pith

Dynamically switching between imitation and reinforcement learning experts by action variance achieves 97.5 percent success on complex robotic manipulation tasks with far less human intervention.

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

The paper seeks to combine the strengths of imitation learning for quick policy learning and reinforcement learning for autonomous improvement in robotic manipulation. It does this by introducing a system that switches between the two based on how much their action suggestions differ, using imitation for broad movements and reinforcement for precise adjustments. The approach includes offline pre-training of both experts and then online fine-tuning with the imitation expert keeping the reinforcement one safe through regularization. If this works, it means robots can learn long sequences of actions more efficiently and with less human supervision than either method alone.

Core claim

MoRI pre-trains an imitation expert and a reinforcement learning expert offline, then in online fine-tuning switches between them according to the variance in their action outputs to manage coarse and fine movements, while regularizing the RL policy with the IL one to maintain safe exploration, leading to high success rates on real-world tasks.

What carries the argument

The variance between actions from the IL and RL experts, which determines dynamic switching between the experts during task execution.

Load-bearing premise

The difference in action variance between the two experts is a dependable way to choose between imitation for rough motions and reinforcement for detailed ones, and the four tasks cover the typical challenges in long-horizon manipulation.

What would settle it

Evaluating the system on a fifth long-horizon task where switching based on variance does not improve success rates or reduce interventions compared to using only RL or only IL.

Figures

Figures reproduced from arXiv: 2604.10165 by Gewei Zuo, Han Ding, Lianjie Ma, Lijun Zhu, Wentao Zhang, Yaohang Xu.

Figure 1
Figure 1. Figure 1: Overview of the MoRI framework. The architecture integrates IL and RL within a MoE system. During offline pre-training, both BC and RL [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of real-world experimental tasks, including 1) place block in drawer, 2) put towel in lidded box, 3) insert two sockets, and 4) double-fold [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves during online training. Each panel compares the performance of ConRFT [12] and MoRI in terms of success rate, intervention [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The evolution of Q-values and variance for RL and BC experts within the MoRI framework. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Evolution of the RL expert selection ratio over training steps in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Action fluctuations during expert switching within the MoRI [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Reinforcement Learning (RL) and Imitation Learning (IL) are the standard frameworks for policy acquisition in manipulation. While IL offers efficient policy derivation, it suffers from compounding errors and distribution shift. Conversely, RL facilitates autonomous exploration but is frequently hindered by low sample efficiency and the high cost of trial and error. Since existing hybrid methods often struggle with complex tasks, we introduce Mixture of RL and IL Experts (MoRI). This system dynamically switches between IL and RL experts based on the variance of expert actions to handle coarse movements and fine-grained manipulations. MoRI employs an offline pre-training stage followed by online fine-tuning to accelerate convergence. To maintain exploration safety and minimize human intervention, the system applies IL-based regularization to the RL component. Evaluation across four complex real-world tasks shows that MoRI achieves an average success rate of 97.5% within 2 to 5 hours of fine-tuning. Compared to baseline RL algorithms, MoRI reduces human intervention by 85.8% and shortens convergence time by 21%, demonstrating its capability in robotic manipulation.

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

3 major / 2 minor

Summary. The manuscript introduces MoRI, a hybrid Mixture of RL and IL Experts system for long-horizon robotic manipulation tasks. It dynamically switches between IL and RL experts using the variance of their actions to manage coarse movements versus fine-grained control, incorporates offline pre-training followed by online fine-tuning, and applies IL-based regularization to the RL component for exploration safety. Evaluations on four complex real-world tasks report an average 97.5% success rate within 2-5 hours of fine-tuning, an 85.8% reduction in human intervention, and 21% shorter convergence time relative to baseline RL algorithms.

Significance. If the empirical results hold under detailed scrutiny and the variance-based switching mechanism generalizes beyond the four evaluated tasks, MoRI could offer a practical advance in hybrid RL-IL methods by improving sample efficiency, reducing reliance on human oversight, and enabling safer autonomous exploration in complex manipulation. The reported reductions in intervention time and faster convergence would be notable strengths for real-world robotics applications.

major comments (3)
  1. [Abstract] Abstract: The central performance claims (97.5% average success rate, 85.8% reduction in human intervention, 21% shorter convergence) are stated without any description of the four tasks, the specific baseline RL algorithms, number of trials, variance across runs, or statistical tests. This absence prevents verification of whether the gains are attributable to the proposed method or to unstated factors such as task selection or expert pre-training quality.
  2. [Method] Method section (variance-based switching): The dynamic expert selection relies on action variance between the RL and IL experts as the signal for favoring coarse IL versus fine RL. No ablation is described that isolates this signal against fixed-ratio mixing, random switching, or variance-agnostic baselines, leaving open the possibility that reported gains stem primarily from pre-training and regularization rather than the variance mechanism itself.
  3. [Evaluation] Evaluation: The claim that MoRI reduces human intervention by 85.8% and converges 21% faster is load-bearing for the paper's contribution, yet the text supplies no implementation details, hyperparameter settings, or analysis showing that the variance signal correlates reliably with task requirements across long-horizon scenarios rather than being an artifact of the specific expert training.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'baseline RL algorithms' is used without naming the specific methods (e.g., SAC, TD3, or PPO), which is needed for reproducibility and fair comparison.
  2. [Method] The manuscript would benefit from a clear pseudocode or diagram illustrating the exact variance computation and switching threshold logic.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each of the major comments below and have prepared revisions to strengthen the paper accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claims (97.5% average success rate, 85.8% reduction in human intervention, 21% shorter convergence) are stated without any description of the four tasks, the specific baseline RL algorithms, number of trials, variance across runs, or statistical tests. This absence prevents verification of whether the gains are attributable to the proposed method or to unstated factors such as task selection or expert pre-training quality.

    Authors: We agree with the referee that the abstract would be more informative with additional context. In the revised manuscript, we will expand the abstract to include brief descriptions of the four tasks, the specific baseline RL algorithms used, the number of trials conducted, the variance across runs, and any statistical tests performed. This will allow readers to better evaluate the claims. revision: yes

  2. Referee: [Method] Method section (variance-based switching): The dynamic expert selection relies on action variance between the RL and IL experts as the signal for favoring coarse IL versus fine RL. No ablation is described that isolates this signal against fixed-ratio mixing, random switching, or variance-agnostic baselines, leaving open the possibility that reported gains stem primarily from pre-training and regularization rather than the variance mechanism itself.

    Authors: The variance-based switching mechanism is central to MoRI's ability to handle long-horizon tasks by adapting to the uncertainty in expert actions. While the original submission focused on the overall system performance, we recognize the value of isolating this component. We will add an ablation study in the revised paper comparing the variance-based approach to fixed-ratio mixing, random switching, and variance-agnostic baselines. This will demonstrate the contribution of the dynamic variance signal. revision: yes

  3. Referee: [Evaluation] Evaluation: The claim that MoRI reduces human intervention by 85.8% and converges 21% faster is load-bearing for the paper's contribution, yet the text supplies no implementation details, hyperparameter settings, or analysis showing that the variance signal correlates reliably with task requirements across long-horizon scenarios rather than being an artifact of the specific expert training.

    Authors: We appreciate this point and will enhance the evaluation section in the revision. We will provide detailed implementation specifics on how human interventions are measured and counted, the hyperparameter settings for the variance threshold, and additional analysis demonstrating the correlation of the variance signal with task requirements in long-horizon scenarios. This will help confirm the reliability of the mechanism. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper describes an empirical system (MoRI) with a design choice for dynamic switching based on action variance, followed by offline pre-training and online fine-tuning. No equations, derivations, or mathematical reductions are present. Results are reported as experimental outcomes on four tasks rather than self-referential fits or predictions forced by construction. No self-citation load-bearing steps or uniqueness theorems are invoked in the provided text. The central claims rest on empirical evaluation, which is independent of the method description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities beyond the standard RL and IL frameworks.

pith-pipeline@v0.9.0 · 5499 in / 962 out tokens · 46005 ms · 2026-05-10T15:56:12.857132+00:00 · methodology

discussion (0)

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