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arxiv: 2504.14820 · v2 · pith:LTHXE24Vnew · submitted 2025-04-21 · 💻 cs.RO

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

Zichun Xu , Zhaomin Wang , Yuntao Li , Lei Zhuang , Zhiyuan Zhao , Guocai Yang , Jingdong Zhao This is my paper

Pith reviewed 2026-05-22 18:40 UTC · model grok-4.3

classification 💻 cs.RO
keywords peg-in-holereinforcement learningvisual RLassembly tasksprimitive policysample efficiencyrobot manipulation
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The pith

A separate primitive policy for visual RL lets agents master peg-in-hole tasks with fewer samples and higher success rates.

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

The paper draws from human binocular vision to split peg-in-hole assembly into a location primitive that positions the peg above the hole and an insertion primitive that completes the mating. It encodes this split as a Separate Primitive Policy (S2P) compatible with any model-free reinforcement learning algorithm. Ten polygon benchmarks in simulation show the split yields better sample efficiency and success even when force limits are active. Real-robot trials confirm the approach transfers without retraining from scratch. Ablation tests explore how the separation affects generalization across task variations.

Core claim

The central claim is that explicitly separating the policy into independent location and insertion primitives allows visual reinforcement learning agents to derive both action types simultaneously yet learn each phase more effectively than a single joint policy, producing measurable gains in sample efficiency and success rate across ten distinct polygon insertion tasks under force constraints.

What carries the argument

The Separate Primitive Policy (S2P), which decomposes the action space into a location primitive and an insertion primitive so that each can be learned while the other is also active.

Load-bearing premise

The assumption that splitting the policy into separate location and insertion primitives improves learning dynamics over a single joint policy.

What would settle it

Running the exact same ten polygon benchmarks and force-constrained settings with a single joint policy that matches or exceeds S2P's sample efficiency and success rate would falsify the claimed benefit of the separation.

Figures

Figures reproduced from arXiv: 2504.14820 by Guocai Yang, Jingdong Zhao, Lei Zhuang, Yuntao Li, Zhaomin Wang, Zhiyuan Zhao, Zichun Xu.

Figure 1
Figure 1. Figure 1: Overview of the proposed insertion strategy. The encoded visual [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Network architectures for the actor and critic of S2P-DrQ-v2. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Simulation setup and peg-in-hole suites with different shapes, where pegs are initialized with being grasped by the gripper [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training performance of S2P against the plain policy, where the solid line and the shaded area represent the mean and standard deviation across [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Benchmark results of S2P-DrQ-v2 and DrQ-v2 with force penalty. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Training procedure and communication network on the real [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Real-world platform setup and a completed insertion process with the trained model of S2P-DrQ-v2. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation analysis on the effect of action repeat on S2P-DrQ-v2. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
read the original abstract

For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This paper draws insights from this behavior to enable agents to learn efficient assembly strategies through visual reinforcement learning. Hence, we propose a Separate Primitive Policy (S2P) to learn how to derive location and insertion actions simultaneously. S2P is compatible with model-free reinforcement learning algorithms. Ten insertion tasks featuring different polygons are developed as benchmarks for evaluations. Simulation experiments show that S2P can boost the sample efficiency and success rate even with force constraints. Real-world experiments are also performed to verify the feasibility of S2P. Ablations are finally given to discuss the generalizability of S2P and some factors that affect its performance.

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 / 2 minor

Summary. The paper proposes a Separate Primitive Policy (S2P) for visual reinforcement learning in peg-in-hole tasks. Drawing from human binocular vision, S2P decomposes the policy into independent location and insertion primitives that are learned simultaneously. It is compatible with model-free RL algorithms and is evaluated on ten polygon insertion benchmarks in simulation (showing gains in sample efficiency and success rate under force constraints) plus real-world verification. Ablations discuss generalizability and performance factors.

Significance. If the separation mechanism is shown to be causal, the work could support modular policy designs for contact-rich assembly tasks and improve sample efficiency in constrained visual RL settings. The multi-benchmark simulation suite and real-world transfer provide a reasonable empirical foundation, though the absence of matched baselines limits the strength of the causal claim.

major comments (2)
  1. [Experiments / Ablations] Experiments and ablations sections: The central claim that primitive separation itself boosts sample efficiency and success rate (even with force constraints) is not supported by a direct head-to-head comparison against an otherwise identical monolithic joint policy. Ablations appear to vary secondary factors (network size, reward shaping, visual encoder) but do not report training a single-policy baseline on the same ten benchmarks with matched hyperparameters, architecture, and force handling. This leaves the operative mechanism unproven.
  2. [Methods] Methods or implementation details: The abstract and results claim improved performance 'even with force constraints,' yet the manuscript provides insufficient detail on how force limits are enforced during training (e.g., via reward penalties, action clipping, or external controllers) and whether the same constraints are applied identically to any baselines. This detail is load-bearing for the robustness claim.
minor comments (2)
  1. [Abstract] Abstract: No quantitative numbers, error bars, or baseline comparisons are reported, which weakens the ability to assess the magnitude of the claimed gains.
  2. [Figures / Notation] Notation and figures: Clarify whether the two primitives share any parameters or visual features, and ensure all figures include clear legends distinguishing S2P from any comparison methods.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of our results. We address each major comment below and indicate the revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Experiments / Ablations] Experiments and ablations sections: The central claim that primitive separation itself boosts sample efficiency and success rate (even with force constraints) is not supported by a direct head-to-head comparison against an otherwise identical monolithic joint policy. Ablations appear to vary secondary factors (network size, reward shaping, visual encoder) but do not report training a single-policy baseline on the same ten benchmarks with matched hyperparameters, architecture, and force handling. This leaves the operative mechanism unproven.

    Authors: We agree that a direct head-to-head comparison against a monolithic joint policy with matched hyperparameters, architecture, and force handling on the same ten benchmarks would provide stronger evidence for the causal benefit of primitive separation. The current ablations examine factors internal to S2P (such as network size and reward shaping) and compare against methods from the literature, but do not include this specific baseline. In the revised manuscript we will add this experiment and report the corresponding sample-efficiency and success-rate results. revision: yes

  2. Referee: [Methods] Methods or implementation details: The abstract and results claim improved performance 'even with force constraints,' yet the manuscript provides insufficient detail on how force limits are enforced during training (e.g., via reward penalties, action clipping, or external controllers) and whether the same constraints are applied identically to any baselines. This detail is load-bearing for the robustness claim.

    Authors: We acknowledge that the manuscript currently lacks explicit implementation details on force-limit enforcement. In the revised version we will expand the Methods section to describe that force limits are enforced via a combination of reward penalties for exceeding predefined force thresholds and action clipping inside the simulator. The same enforcement mechanism is applied uniformly to S2P and all baselines to maintain comparability. revision: yes

Circularity Check

0 steps flagged

Empirical RL method proposal with no derivation chain

full rationale

The paper proposes S2P as an algorithmic design choice (separate location and insertion primitives) inspired by human behavior, then validates it via simulation benchmarks on ten polygon tasks and real-world tests. No equations, fitted parameters, or self-citations are used to derive the core claim; results are reported directly from training runs. This is self-contained empirical work with independent experimental evidence, so no circularity is present.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper relies on standard RL assumptions such as Markov decision process formulation and reward design for insertion success, but no explicit free parameters, axioms, or invented entities are detailed in the abstract.

pith-pipeline@v0.9.0 · 5676 in / 1106 out tokens · 25861 ms · 2026-05-22T18:40:19.780481+00:00 · methodology

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Reference graph

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