RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning

Menglin Zou; Xinhai Sun; Yaojie Tu; Zhuang Li; Zijian Zhu

arxiv: 2605.30957 · v1 · pith:CNGKLEJOnew · submitted 2026-05-29 · 💻 cs.RO

RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning

Zijian Zhu , Menglin Zou , Zhuang Li , Yaojie Tu , Xinhai Sun This is my paper

Pith reviewed 2026-06-28 22:25 UTC · model grok-4.3

classification 💻 cs.RO

keywords robot demonstration generationreinforcement learningsim-to-real transfervision-language-action modelstrajectory smoothnesspick-and-place taskhuman teleoperation comparison

0 comments

The pith

Reinforcement learning policies transferred from simulation generate higher-quality robot demonstration trajectories than human teleoperation for training vision-language-action models.

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

The paper aims to establish that a sim-to-real reinforcement learning framework can replace human teleoperation as the source of demonstration data for vision-language-action models. It trains an RL policy in simulation, transfers it to the real robot, and harvests the successful rollouts as trajectories. These trajectories are claimed to be smoother and to produce better-performing VLA models on tasks such as pick-and-place. If correct, this would reduce dependence on labor-intensive human data collection while providing a scalable way to generate consistent training signals. The system pairs the RL policy with a VLM task parser and an object localizer to enable the process.

Core claim

The paper claims that an RL policy trained in simulation and transferred to the real robot can serve as a structured trajectory generator whose successful rollouts supply cleaner, smoother demonstrations than human teleoperation, resulting in superior downstream VLA performance on a pick-and-place task while simulation supplies additional trajectories at low marginal cost.

What carries the argument

The sim-to-real transferred RL policy acting as a demonstration generator, supported by a VLM-based task parser and Grounding DINO-based object localizer, to produce and harvest successful real-world rollouts.

If this is right

The transferred RL policy achieves a high success rate on real-world pick-and-place tasks.
RDGen trajectories are significantly smoother than human teleoperation trajectories.
VLA models trained on RDGen demonstrations outperform those trained on human teleoperation data.
Simulation supplies additional trajectories at little marginal cost for VLA training.

Where Pith is reading between the lines

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

The approach might reduce the overall cost and variability of collecting robot demonstration data if the RL transfer succeeds across tasks.
Iterative retraining of the RL policy on its own harvested trajectories could further improve demonstration quality over time.
Similar sim-to-real generators could be tested on manipulation tasks beyond pick-and-place to check generality.

Load-bearing premise

The transferred RL policy will reliably produce successful real-world rollouts whose trajectories are both smoother and more useful for VLA training than human teleoperation trajectories without post-hoc filtering.

What would settle it

A controlled experiment in which VLA models trained on RDGen trajectories show equal or lower task performance than VLA models trained on human teleoperation trajectories for the same pick-and-place task.

Figures

Figures reproduced from arXiv: 2605.30957 by Menglin Zou, Xinhai Sun, Yaojie Tu, Zhuang Li, Zijian Zhu.

**Figure 2.** Figure 2: Example of language-guided object localization in a real scene. Grounding DINO detects [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Task execution of grasping a gray cube and placing it into a paper box. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative result of grasping a bottle and placing it into a paper box. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

read the original abstract

Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control. However, their performance remains fundamentally constrained by the availability of high-quality robot trajectory data. In current robot learning practice, such data are primarily collected through human teleoperation, which is labor-intensive, costly, and difficult to scale. In this paper, we propose RDGen, a sim-to-real reinforcement learning framework for generating high-quality robot demonstrations. Rather than employing reinforcement learning solely as the final control policy, RDGen leverages trained RL policies as a structured trajectory generator. The system consists of a VLM-based task parser that identifies task-relevant objects, a Grounding DINO-based object localizer, and an RL policy transferred from simulation to the real robot. Successful rollouts are then harvested as clean, high-quality demonstrations for downstream VLA training, while the simulation stage further provides a scalable source of additional trajectories at little marginal cost. Experiments on a pick-and-place task demonstrate that the transferred RL policy achieves a high task success rate. Compared with human teleoperation, RDGen produces significantly smoother trajectories and yields superior downstream VLA performance. These results indicate that RL-generated demonstrations can serve as more reliable and consistent supervisory signals for robot policy learning.

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.

Desk Editor's Note private letter to a colleague

RDGen applies a standard sim-to-real RL pipeline to harvest trajectories for VLA training, but the abstract gives no numbers, baselines, or transfer details to support the claims of smoother paths and better downstream performance.

read the letter

The core idea is to train an RL policy in simulation, move it to the real robot, run it on a pick-and-place task, and collect the successful rollouts as training data for vision-language-action models. The pipeline adds a VLM parser and Grounding DINO localizer to set up the scene, then harvests the RL trajectories instead of using human teleoperation. The simulation stage is also used to generate extra data cheaply.

This is a straightforward combination of existing pieces: sim-to-real transfer, object detection, and RL for data generation. It does address the practical bottleneck of collecting consistent robot data at scale, and the framing of RL as a demo generator rather than only a final controller is a reasonable practical step.

The problem is that the abstract states the transferred policy reaches a high success rate, produces significantly smoother trajectories, and leads to superior VLA performance, yet supplies none of the supporting numbers. There are no success rates, no smoothness metric such as jerk or curvature, no VLA accuracy curves or final scores, no count of trajectories, and no description of the sim-to-real method or any sim-real gap. The stress-test note correctly flags this absence of quantitative support and controls.

The paper is aimed at groups working on scaling demonstration data for imitation and VLA models. A reader looking for a concrete pipeline sketch might get an idea from the component list, but without results tables or failure analysis the superiority claim cannot be evaluated.

I would not bring this to a reading group in its current state. I would not cite it. It does not yet deserve peer review because the central empirical claims rest on assertions rather than reported evidence.

Referee Report

3 major / 0 minor

Summary. The paper proposes RDGen, a sim-to-real RL framework that trains an RL policy in simulation, transfers it to a real robot via a VLM-based task parser and Grounding DINO localizer, and harvests successful real-world rollouts as demonstration trajectories for downstream VLA training. On a pick-and-place task the abstract claims the transferred policy achieves a high success rate, generates significantly smoother trajectories than human teleoperation, and yields superior VLA performance, while simulation supplies additional scalable data.

Significance. If the quantitative claims hold, RDGen would provide a scalable, lower-cost source of consistent robot trajectories that could improve VLA training over labor-intensive human teleoperation. The approach of using RL policies explicitly as demonstration generators rather than end controllers is a potentially useful distinction, but its value depends on verifiable sim-to-real reliability and measurable trajectory quality gains.

major comments (3)

[Abstract] Abstract: the central empirical claims ('high task success rate', 'significantly smoother trajectories', 'superior downstream VLA performance') are stated without any numerical values, standard deviations, number of trials, baseline comparisons, or statistical tests, rendering the superiority assertion unevaluable from the provided text.
[Abstract] Abstract: no definition or metric is supplied for trajectory smoothness (e.g., jerk, curvature variance, or velocity profile statistics), nor is any post-hoc filtering or task-specific tuning described, so the comparison to human teleoperation cannot be assessed.
[Abstract] Abstract: the sim-to-real transfer method is outlined at the component level (VLM parser, Grounding DINO, RL policy) but supplies no domain-randomization parameters, sim-to-real success-rate gap, failure-mode analysis, or real-world rollout count, leaving the reliability of the harvested demonstrations unsupported.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We agree that the abstract requires more quantitative detail to make the claims evaluable. We will revise the abstract accordingly and address each of the major comments below.

read point-by-point responses

Referee: [Abstract] Abstract: the central empirical claims ('high task success rate', 'significantly smoother trajectories', 'superior downstream VLA performance') are stated without any numerical values, standard deviations, number of trials, baseline comparisons, or statistical tests, rendering the superiority assertion unevaluable from the provided text.

Authors: We concur that the abstract would benefit from explicit numerical support for these claims. The full manuscript reports these results in detail (e.g., task success rates, trajectory quality metrics with standard deviations, trial counts, and statistical comparisons in the experiments section). We will update the abstract to include the key quantitative findings and baseline comparisons. revision: yes
Referee: [Abstract] Abstract: no definition or metric is supplied for trajectory smoothness (e.g., jerk, curvature variance, or velocity profile statistics), nor is any post-hoc filtering or task-specific tuning described, so the comparison to human teleoperation cannot be assessed.

Authors: We will revise the abstract to specify the smoothness metrics employed (jerk and curvature variance, as defined and computed in the methods) and clarify that the trajectories are used as generated by the RL policy without additional filtering. revision: yes
Referee: [Abstract] Abstract: the sim-to-real transfer method is outlined at the component level (VLM parser, Grounding DINO, RL policy) but supplies no domain-randomization parameters, sim-to-real success-rate gap, failure-mode analysis, or real-world rollout count, leaving the reliability of the harvested demonstrations unsupported.

Authors: The experimental section of the manuscript details the domain randomization parameters, reports the sim-to-real performance gap, analyzes failure modes, and specifies the number of real-world rollouts used to harvest demonstrations. We will add concise references to these elements in the revised abstract. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method description with no derivation chain or self-referential equations

full rationale

The paper presents a sim-to-real RL framework for generating robot demonstrations, consisting of a VLM parser, Grounding DINO localizer, and transferred RL policy whose successful rollouts are harvested for VLA training. No equations, fitted parameters, or mathematical derivations appear in the provided text. Claims rest on experimental comparison (smoother trajectories, superior VLA performance) rather than any prediction that reduces to its own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central assertions are therefore independent empirical statements, not self-definitional or fitted-input loops.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The central claim implicitly assumes reliable sim-to-real transfer and that RL success implies higher-quality demonstrations than human data.

pith-pipeline@v0.9.1-grok · 5760 in / 1117 out tokens · 17434 ms · 2026-06-28T22:25:08.344157+00:00 · methodology

discussion (0)

Reference graph

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RT-1: Robotics Transformer for Real-World Control at Scale

Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al.: Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817 (2022)

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Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al.: Ego4d: Around the world in 3,000 hours of egocentric video. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18995–19012 (2022)

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Kareer, D

Kareer, S., Patel, D., Punamiya, R., Mathur, P., Cheng, S., Wang, C., Hoffman, J., Xu, D.: EgoMimic: Scaling imitation learning via egocentric video. arXiv preprint arXiv:2410.24221 (2024) 11

work page arXiv 2024

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DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Khazatsky, A., Pertsch, K., Nair, S., Balakrishna, A., Dasari, S., Karamcheti, S., Nasiriany, S., Srirama, M.K., Chen, L.Y., Ellis, K., etal.: Droid: Alarge-scalein-the-wildrobotmanipulation dataset. arXiv preprint arXiv:2403.12945 (2024)

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OpenVLA: An Open-Source Vision-Language-Action Model

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In: 2023 IEEE International conference on robotics and automation (ICRA)

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., Zeng, A.: Code as policies: Language model programs for embodied control. In: 2023 IEEE International conference on robotics and automation (ICRA). pp. 9493–9500. IEEE (2023)

2023

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Continuous control with deep reinforcement learning

Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)

work page internal anchor Pith review Pith/arXiv arXiv 2015

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In: Conference on Robot Learning (2021)

Mandlekar, A., Xu, D., Wong, J., Nasiriany, S., Wang, C., Kulkarni, R., Fei-Fei, L., Savarese, S., Zhu, Y., Martín-Martín, R.: robomimic: A benchmark for robot learning from demonstration. In: Conference on Robot Learning (2021)

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Proximal Policy Optimization Algorithms

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work page internal anchor Pith review Pith/arXiv arXiv 2017

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work page arXiv 2021

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arXiv preprint arXiv:2603.08124 (2026)

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work page arXiv 2026

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