arxiv: 2605.01544 · v1 · submitted 2026-05-02 · 💻 cs.RO

Recognition: unknown

An Efficient Metric for Data Quality Measurement in Imitation Learning

Noushad Sojib , Momotaz Begum

Authors on Pith no claims yet

Pith reviewed 2026-05-09 13:57 UTC · model grok-4.3

classification 💻 cs.RO

keywords imitation learningdata curationdemonstration qualitypower spectral densityrobot fine-tuningtrajectory analysisend-user datadaily living tasks

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The pith

A power spectral density metric ranks imitation learning demonstrations by smoothness to improve fine-tuned robot policies without rollouts.

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

The paper proposes using power spectral density to measure the smoothness of robot demonstration trajectories as a way to automatically curate data for imitation learning. This approach avoids the need for expensive policy rollouts or environment interactions that other methods require. Lower PSD values indicate smoother motions with fewer oscillations, which the authors show lead to better fine-tuned policies. They test it on benchmark datasets and a user study with older adults performing daily living tasks. If correct, this makes it practical to improve robot policies using real-world end-user data collected in the field.

Core claim

The central claim is that computing the power spectral density of raw demonstration trajectories provides an efficient, policy-free metric for ranking demonstration quality, where lower PSD corresponds to smoother and more effective demonstrations for training imitation learning policies. When used to select data for fine-tuning a pre-trained policy on a daily living task, the curated subset produces higher success rates and smoother robot executions than uncurated data or data ranked by other methods.

What carries the argument

The power spectral density (PSD) of demonstration trajectories, which quantifies the distribution of motion frequencies and serves as an indicator of erratic versus smooth behavior.

If this is right

Fine-tuned policies using low-PSD data achieve higher task success rates on daily living tasks.
Robot execution trajectories become smoother with PSD-curated data.
The metric performs well on both expert and lay-user demonstrations.
It outperforms uncurated baselines and two other data-ranking approaches.
No environment interaction or policy learning is needed for curation.

Where Pith is reading between the lines

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

This metric could enable scalable data collection from non-expert users in homes or care facilities.
It might be combined with other quality signals like task completion time for even better curation.
The approach could extend to other continuous control tasks in robotics beyond manipulation.
Testing on more diverse tasks would reveal if PSD correlates with quality across different robot platforms.

Load-bearing premise

That lower power spectral density reliably indicates higher quality demonstrations that translate to improved policy performance after fine-tuning, without needing to validate against the specific policy or task.

What would settle it

Running the fine-tuning experiments with PSD-selected data versus random selection and finding no significant difference in task success rates or trajectory smoothness would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.01544 by Momotaz Begum, Noushad Sojib.

**Figure 1.** Figure 1: Demonstration trajectories (left) and their power spectrum (middle). Poor demonstrations exhibit broader spectral support and higher total power view at source ↗

**Figure 2.** Figure 2: Example of an expert (Demo 0) and a non-expert (Demo view at source ↗

**Figure 4.** Figure 4: We asked the participants to give demonstrations view at source ↗

**Figure 3.** Figure 3: Remaining average demonstration quality is plotted against the number of demonstrations removed for Can and Square tasks under MH and view at source ↗

**Figure 4.** Figure 4: An older adult participant from our study teleoperating a Franka view at source ↗

read the original abstract

Imitation learning (IL) has seen remarkable progress, yet field deployment of IL-powered robots remains hindered by the challenge of out-of-distribution (OOD) scenarios. Fine-tuning pre-trained policies with end-user demonstrations collected in deployment environments is a promising strategy to address this challenge. However, end-user demonstrations are frequently of poor quality, characterized by excessive corrective motions, oscillations, and abrupt adjustments that degrade both learned and fine-tuned policy performance. Existing automated approaches for curating demonstration data require policy rollouts in the environment, making them computationally expensive and impractical for real-world deployment. In this paper, we propose a fast, efficient, and fully automated demonstration ranking metric based on the power spectral density (PSD) of demonstration trajectories. The PSD metric requires no policy learning, environment interaction, or expert labeling, making it well-suited for scalable, in-the-field data curation. Lower PSD values correspond to smoother, higher-quality demonstrations, while higher PSD values indicate erratic, artifact-laden trajectories. We evaluate the proposed metric on two benchmark imitation learning datasets comprising expert and lay-user demonstrations, and through a user study with older adults at a retirement facility, where collected demonstrations are used to fine-tune $\pi0.5$ \cite{intelligence2025pi_} for a daily living task. Results demonstrate that PSD-curated data yields policies with higher task success rates and smoother execution trajectories compared to uncurated baselines and two competitive data-ranking methods.

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

The paper introduces a rollout-free PSD metric to rank imitation learning demos by smoothness and reports gains in fine-tuned policy performance on benchmarks plus a user study.

read the letter

The main takeaway is that this work gives a cheap, automated way to score demonstration quality in imitation learning using the power spectral density of the raw trajectories. Lower PSD is treated as smoother and therefore better, letting you curate end-user data without any policy rollouts or environment runs. That setup directly targets the practical problem of noisy field data for fine-tuning pre-trained policies like pi0.5. They test the ranking on two standard IL benchmarks and in a user study with older adults collecting demonstrations for daily living tasks, claiming the curated subsets produce higher success rates and smoother execution than uncurated data or two other ranking baselines. The efficiency and lack of policy dependence are real advantages here, and the user study adds some grounding in actual deployment-like conditions. The central assumption still needs more checking. The metric penalizes higher-frequency content as artifacts, but some tasks may require quick adjustments or high-frequency elements that are task-intrinsic rather than noise. The paper does not appear to include cross-task tests or breakdowns showing whether the filtered motions were truly detrimental versus necessary. Results are described as positive, yet without reported effect sizes, error bars, or statistical details in the summary it is hard to gauge how reliable the improvements are across settings. This is aimed at people working on real-world robot adaptation and data curation for imitation learning. Readers who need lightweight, policy-agnostic tools for cleaning user demonstrations would get practical value from it. The idea is straightforward and addresses a clear deployment issue with some empirical backing, so it deserves a serious referee even if revisions are needed to strengthen the generalizability claims.

Referee Report

2 major / 2 minor

Summary. The paper proposes a policy-agnostic metric for ranking imitation learning demonstrations based on the power spectral density (PSD) of their trajectories. Lower aggregate PSD is interpreted as indicating smoother, higher-quality data free of oscillations and corrective artifacts. The metric requires no policy rollouts or environment interaction. On two benchmark IL datasets and a user study with older adults fine-tuning π0.5 for a daily-living task, PSD-curated subsets are reported to produce policies with higher task success rates and smoother execution than uncurated baselines or two competitive ranking methods.

Significance. If the central claim holds, the work offers a computationally cheap, fully automated curation tool that could scale end-user data collection for real-world IL deployment. The policy-free nature and inclusion of a retirement-facility user study are concrete strengths. However, the result's impact is limited by the untested modeling choice that frequency content alone reliably separates beneficial from detrimental motions across tasks.

major comments (2)

[Evaluation / Results] The central claim that PSD ranking improves downstream success rates rests on the assumption that lower frequency content is universally beneficial. No ablation or analysis isolates whether gains arise from removing artifacts versus discarding task-intrinsic high-frequency components, and no cross-task experiments test transfer to domains with different motion profiles (e.g., rapid vs. slow tasks).
[Abstract and Evaluation sections] The abstract and summary report positive outcomes on benchmarks and the user study yet supply no numerical success rates, error bars, statistical tests, or methodology details for the fine-tuning experiments. This prevents verification of whether the reported improvements are statistically reliable or practically meaningful.

minor comments (2)

[Abstract] The citation for π0.5 appears as an incomplete placeholder (intelligence2025pi_); a full reference should be supplied.
[Method] Notation for the PSD computation and aggregation across trajectories is not defined in the provided summary; explicit equations would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help strengthen the presentation of our work on the PSD-based demonstration ranking metric. We address each major comment point by point below, with revisions planned where they improve clarity or address valid limitations.

read point-by-point responses

Referee: [Evaluation / Results] The central claim that PSD ranking improves downstream success rates rests on the assumption that lower frequency content is universally beneficial. No ablation or analysis isolates whether gains arise from removing artifacts versus discarding task-intrinsic high-frequency components, and no cross-task experiments test transfer to domains with different motion profiles (e.g., rapid vs. slow tasks).

Authors: We appreciate this critique of the underlying assumption. Our approach is motivated by the prevalence of corrective oscillations in end-user demonstrations for daily-living tasks, where lower PSD correlates with smoother, higher-quality data as evidenced by improved policy success and execution in our benchmarks and retirement-facility user study. We acknowledge that the metric does not include explicit ablations to separate artifact removal from potential loss of task-intrinsic high-frequency components, nor dedicated cross-task tests on rapid-motion domains. In the revised manuscript, we will add a limitations section explicitly discussing the domain-specific nature of the frequency-quality assumption and proposing future adaptations such as task-dependent PSD thresholds. No new experiments are added, as the current evaluation focuses on validating rollout-free curation in the targeted practical settings with varied demonstration qualities. revision: partial
Referee: [Abstract and Evaluation sections] The abstract and summary report positive outcomes on benchmarks and the user study yet supply no numerical success rates, error bars, statistical tests, or methodology details for the fine-tuning experiments. This prevents verification of whether the reported improvements are statistically reliable or practically meaningful.

Authors: We agree that the abstract would be more informative with specific quantitative details. The full manuscript's evaluation section already reports task success rates, standard errors, and fine-tuning methodology details for the π0.5 experiments, including the user study protocol. In the revision, we will update the abstract to include key numerical results (e.g., success rate improvements with associated statistical significance) and ensure all evaluation details are prominently referenced. This enhances verifiability without changing the reported outcomes. revision: yes

Circularity Check

0 steps flagged

No circularity: PSD metric is a direct computation on input trajectories with empirical validation only

full rationale

The paper's core contribution is a demonstration ranking metric defined directly as the power spectral density (PSD) computed on the raw trajectory data. No equations, self-citations, or ansatzes reduce this metric to a fitted parameter, downstream policy performance, or prior author result by construction. The assumption that lower PSD corresponds to higher quality is stated explicitly and tested via separate empirical evaluations on two IL benchmarks and a user study with π0.5 fine-tuning; these tests do not feed back into the metric definition itself. No load-bearing derivation step collapses to tautology or self-reference. This is a standard non-circular empirical proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on an unproven empirical correlation between trajectory smoothness (low PSD) and downstream policy quality; no free parameters or new entities are introduced, but the domain assumption that PSD captures the relevant quality dimensions is load-bearing.

axioms (1)

domain assumption Lower power spectral density values correspond to smoother, higher-quality demonstrations that improve fine-tuned policy performance
Explicitly stated in the abstract as the basis for using PSD as the ranking metric.

pith-pipeline@v0.9.0 · 5555 in / 1134 out tokens · 26440 ms · 2026-05-09T13:57:58.996819+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 16 canonical work pages · 7 internal anchors

[1]

$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

P. Intelligence, K. Black, Brownet al., “pi0.5: a vision-language- action model with open-world generalization,”arXiv preprint arXiv:2504.16054, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

O. X.-E. Collaboration, A. O’Neill, A. Rehmanet al., “Open X- Embodiment: Robotic learning datasets and RT-X models,” https: //arxiv.org/abs/2310.08864, 2023

work page internal anchor Pith review arXiv 2023
[3]

Octo: An Open-Source Generalist Robot Policy

O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xuet al., “Octo: An open-source generalist robot policy,”arXiv preprint arXiv:2405.12213, 2024

work page internal anchor Pith review arXiv 2024
[4]

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

J. Bjorcket al., “Gr00t n1: An open foundation model for generalist humanoid robots,”arXiv preprint arXiv:2503.14734, 2025

work page internal anchor Pith review arXiv 2025
[5]

One demo is worth a thousand trajectories: Action- view augmentation for visuomotor policies,

C. Pan, L. Liang, D. Bauer, E. Cousineau, B. Burchfiel, S. Feng, and S. Song, “One demo is worth a thousand trajectories: Action- view augmentation for visuomotor policies,” inConference on Robot Learning (CoRL), 2025

2025
[6]

Novel demonstration generation with gaussian splatting enables robust one-shot manipulation,

S. Yang, W. Yu, J. Zeng, J. Lv, K. Ren, C. Lu, D. Lin, and J. Pang, “Novel demonstration generation with gaussian splatting enables ro- bust one-shot manipulation,”arXiv preprint arXiv:2504.13175, 2025

work page arXiv 2025
[7]

Diffusion policy: Visuomotor policy learning via action diffusion,

C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proceedings of Robotics: Science and Systems (RSS), 2023

2023
[8]

Affordance-based robot manipulation with flow matching,

F. Zhang and M. Gienger, “Affordance-based robot manipulation with flow matching,”arXiv preprint arXiv:2409.01083, 2024

work page arXiv 2024
[9]

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

T. Z. Zhao, V . Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,”arXiv preprint arXiv:2304.13705, 2023

work page internal anchor Pith review arXiv 2023
[10]

Self supervised detection of incorrect human demonstrations: A path toward safe imitation learning by robots in the wild,

N. Sojib and M. Begum, “Self supervised detection of incorrect human demonstrations: A path toward safe imitation learning by robots in the wild,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 2862–2869

2024
[11]

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

M. Ahn, A. Brohanet al., “Do as i can, not as i say: Grounding language in robotic affordances,”arXiv preprint arXiv:2204.01691, 2022

work page internal anchor Pith review arXiv 2022
[13]

Droid: A large-scale in-the-wild robot manipulation dataset,

A. Khazatsky, K. Pertschet al., “Droid: A large-scale in-the-wild robot manipulation dataset,” 2024

2024
[14]

Cupid: Curating data your robot loves with influence functions,

C. Agiaet al., “Cupid: Curating data your robot loves with influence functions,”arXiv preprint arXiv:2506.19121, 2025

work page arXiv 2025
[15]

Curating demon- strations using online experience,

A. S. Chen, A. M. Lessing, Y . Liu, and C. Finn, “Curating demon- strations using online experience,”arXiv preprint arXiv:2503.03707, 2025

work page arXiv 2025
[16]

Robot data curation with mutual information estimators.arXiv preprint arXiv:2502.08623, 2025

J. Hejnaet al., “Robot data curation with mutual information estima- tors,”arXiv preprint arXiv:2502.08623, 2025

work page arXiv 2025
[17]

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

A. Mandlekar, Xuet al., “What matters in learning from of- fline human demonstrations for robot manipulation,”arXiv preprint arXiv:2108.03298, 2021

work page internal anchor Pith review arXiv 2021
[18]

Data quality in imitation learning,

S. Belkhale, Y . Cui, and D. Sadigh, “Data quality in imitation learning,”Advances in neural information processing systems, vol. 36, pp. 80 375–80 395, 2023

2023
[19]

Consistency matters: Defining demonstration data quality metrics in robot learning from demonstration,

M. Sakr, J. Zhang, H. M. V . d. Loos, D. Kuli ´c, and E. Croft, “Consistency matters: Defining demonstration data quality metrics in robot learning from demonstration,”ACM Transactions on Human- Robot Interaction, vol. 15, no. 2, pp. 1–31, 2025

2025
[20]

Behavioral cloning from noisy demon- strations,

F. Sasaki and R. Yamashina, “Behavioral cloning from noisy demon- strations,” inInternational Conference on Learning Representations, 2020

2020
[21]

Imitation learning by estimating expertise of demonstrators,

M. Beliaev, A. Shih, S. Ermon, D. Sadigh, and R. Pedarsani, “Imitation learning by estimating expertise of demonstrators,” inInternational Conference on Machine Learning. PMLR, 2022, pp. 1732–1748

2022
[22]

Imitation learn- ing from purified demonstrations,

Y . Wang, M. Dong, Y . Zhao, B. Du, and C. Xu, “Imitation learn- ing from purified demonstrations,”arXiv preprint arXiv:2310.07143, 2023

work page arXiv 2023
[23]

Scizor: A self-supervised approach to data curation for large-scale imitation learning,

Y . Zhanget al., “Scizor: A self-supervised approach to data curation for large-scale imitation learning,”arXiv preprint arXiv:2505.22626, 2025

work page arXiv 2025
[24]

Datamil: Selecting data for robot imitation learning with datamodels,

S. Dass, A. Khaddaj, L. Engstrom, A. Madry, A. Ilyas, and R. Mart ´ın- Mart´ın, “Datamil: Selecting data for robot imitation learning with datamodels,”arXiv preprint arXiv:2505.09603, 2025

work page arXiv 2025
[25]

Towards effective uti- lization of mixed-quality demonstrations in robotic manipulation via segment-level selection and optimization,

J. Chen, H. Fang, H.-S. Fang, and C. Lu, “Towards effective uti- lization of mixed-quality demonstrations in robotic manipulation via segment-level selection and optimization,” in2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025, pp. 16 884–16 891

2025
[26]

A. V . Oppenheim,Discrete-time signal processing. Pearson Education India, 1999

1999
[27]

Using non-expert data to robustify imitation learning via offline reinforcement learning,

K. Huanget al., “Using non-expert data to robustify imitation learning via offline reinforcement learning,”arXiv preprint arXiv:2510.19495, 2025

work page arXiv 2025
[28]

Lora: Low-rank adaptation of large language models

E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.” Iclr, vol. 1, no. 2, p. 3, 2022. APPENDIX Training details: To account for differences in demonstra- tion length between datasets, we adjust the rollout horizon for Layman v2 while keeping all other settings fixed. Sp...

2022