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arxiv: 2604.23000 · v1 · submitted 2026-04-24 · 💻 cs.RO

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Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning

Soham Kulkarni , Raayan Dhar , Yuchen Cui
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Pith reviewed 2026-05-08 11:31 UTC · model grok-4.3

classification 💻 cs.RO
keywords imitation learningbehavioral cloningdata qualitytrajectory smoothnessdemonstration filteringrobot manipulationmotor control
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The pith

Smoothness metrics can select higher-quality demonstrations for imitation learning, raising policy success while using far less data.

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

The paper establishes that behavioral cloning performance is constrained by uneven demonstration quality arising from differences in operator skill and collection artifacts. It shows that smoothness in trajectories can serve as a practical signal of skilled movement, allowing a lightweight scoring system to filter or weight data without requiring policy training loops or human labels. This curation reduces variance in the retained action distributions and improves results across filtering, retrieval, and reweighting tasks. The approach matters because real-world robot datasets are typically noisy and heterogeneous, so scalable ways to retain only the best examples can make imitation learning more efficient and reliable.

Core claim

RINSE ranks and indexes demonstrations by trajectory smoothness using two metrics grounded in motor control: Spectral Arc Length measures frequency-domain regularity while Trajectory-Envelope Distance measures spatial deviation with contact awareness. When applied as a filter, the method reduces conditional action variance in the data, which compounds favorably with techniques such as action chunking. On standard benchmarks the filtered data yields higher success rates from substantially smaller subsets; the same scores also improve retrieval re-ranking and align with learned domain reweightings.

What carries the argument

RINSE framework that scores full trajectories with SAL, a spectral regularity measure, and TED, a contact-aware spatial deviation measure, to produce quality rankings without policy feedback.

If this is right

  • SAL filtering on RoboMimic benchmarks produces 16 percent higher success rates while retaining only one-sixth of the original demonstrations.
  • TED filtering on real-world manipulation tasks yields 20 percent performance gains while using only half the data.
  • Using RINSE scores for retrieval re-ranking inside STRAP raises mean success by 5.6 percent on LIBERO-10.
  • When employed as soft weights in domain reweighting, the smoothness scores produce allocations that correlate strongly with those learned by the reweighting method itself.

Where Pith is reading between the lines

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

  • Smoothness filtering could be applied during data collection to discard low-quality trajectories in real time rather than after the fact.
  • The same metrics might generalize to non-robot sequential tasks where expert demonstrations are also expected to exhibit low-frequency regularity.
  • If smoothness correlates with reduced compounding error, the method may be especially useful for long-horizon tasks where small early deviations cause large later failures.
  • Combining SAL and TED into a single composite score could further tighten the quality signal without adding policy-dependent computation.

Load-bearing premise

Trajectory smoothness serves as a reliable proxy for demonstration quality that improves policy performance without introducing harmful selection bias.

What would settle it

Policies trained on the smoothest subset of a fixed demonstration pool show equal or lower success rates than policies trained on randomly selected subsets of identical size across the same tasks and environments.

Figures

Figures reproduced from arXiv: 2604.23000 by Raayan Dhar, Soham Kulkarni, Yuchen Cui.

Figure 1
Figure 1. Figure 1: Smoothness metrics applied to synthetic trajectories. Left to right: 3D trajectory, individual axes, SAL spectral analysis, and TED Bézier curve view at source ↗
Figure 2
Figure 2. Figure 2: SAL (higher = smoother) and TED (lower = smoother) for Open Drawer from [14]. Scores correlate with policy success across modalities. view at source ↗
Figure 3
Figure 3. Figure 3: Real-world setup: U-Factory xArm-7 with two RealSense cameras view at source ↗
Figure 4
Figure 4. Figure 4: SAL violin plots across five metric variants and three modalities for three tasks from [ view at source ↗
Figure 5
Figure 5. Figure 5: SAL distributions stratified by operator-reported difficulty for Push view at source ↗
read the original abstract

In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varying quality due to operator skill differences, teleoperation artifacts, and procedural inconsistencies, yet standard BC treats all demonstrations equally. Existing curation methods require costly policy training in the loop or manual annotation, limiting scalability. We propose RINSE (Ranking and INdexing Smooth Examples), a lightweight framework for scoring demonstrations based on trajectory smoothness that is policy-architecture-agnostic and operates on trajectory data alone, with TED additionally using a phase-boundary/contact signal. Grounded in motor control theory, which establishes smoothness as a hallmark of skilled movement, RINSE uses two complementary metrics: Spectral Arc Length (SAL), a spectral measure of frequency-domain regularity, and Trajectory-Envelope Distance (TED), a spatial measure of contact-aware geometric deviation. We show that smoothness filtering can reduce the conditional action variance of the retained data distribution, with downstream effects that can be amplified by action chunking and compounding error. On RoboMimic benchmarks, SAL filtering achieves 16% higher success using one-sixth of the data. On real-world manipulation, TED filtering achieves 20% improvement with half the data. As a retrieval-stage filter within STRAP on LIBERO-10, RINSE re-ranking improves mean success by 5.6%. As soft weights in Re-Mix domain reweighting, RINSE scores produce domain allocations highly correlated with the learned Re-Mix allocations (Spearman $\rho \geq 0.89$). These results support smoothness as a useful quality signal across filtering, retrieval, and reweighting settings, especially in noisy or heterogeneous data regimes.

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 manuscript proposes RINSE, a lightweight framework for scoring imitation learning demonstrations based on trajectory smoothness using Spectral Arc Length (SAL) and Trajectory-Envelope Distance (TED) metrics, grounded in motor control theory. It demonstrates that filtering data with these metrics can lead to higher success rates in behavioral cloning, specifically 16% higher on RoboMimic with 1/6 data via SAL, 20% on real-world with half data via TED, plus improvements in retrieval and reweighting applications.

Significance. Should the smoothness metrics prove to be a robust proxy for demonstration quality independent of variance reduction effects, the work would offer a scalable, training-free approach to data curation that could significantly benefit imitation learning in robotics, particularly for heterogeneous real-world datasets. The evaluation across filtering, retrieval, and reweighting strengthens the case for its utility.

major comments (2)
  1. [§4.1] §4.1 (RoboMimic Experiments): The reported 16% higher success rate using one-sixth of the data with SAL filtering is central to the claim; however, the experiments must include a control where trajectories are selected based on low variance alone (e.g., by action magnitude or frequency content without the full SAL metric) to confirm that the motor-control-grounded metric provides benefits beyond simple variance reduction.
  2. [§4.3] §4.3 (Real-world Manipulation): The 20% improvement with TED filtering using half the data requires evidence that the phase-boundary/contact signal does not inadvertently filter out valid exploratory or corrective trajectories that are high-quality but less smooth, as this would undermine the proxy relationship asserted in the introduction.
minor comments (2)
  1. The abstract states that RINSE is 'policy-architecture-agnostic and operates on trajectory data alone'; this should be explicitly verified in the method section with pseudocode or implementation details.
  2. [§3] Provide the mathematical definitions of SAL and TED as equations to allow readers to reproduce the metrics without ambiguity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help us improve the manuscript. We provide point-by-point responses to the major comments below.

read point-by-point responses

  1. Referee: [§4.1] §4.1 (RoboMimic Experiments): The reported 16% higher success rate using one-sixth of the data with SAL filtering is central to the claim; however, the experiments must include a control where trajectories are selected based on low variance alone (e.g., by action magnitude or frequency content without the full SAL metric) to confirm that the motor-control-grounded metric provides benefits beyond simple variance reduction.

    Authors: We agree that it is important to demonstrate that the improvements from SAL filtering are not solely due to variance reduction. SAL specifically quantifies smoothness through spectral arc length, capturing frequency-domain properties that simple variance measures (like action magnitude) do not fully encompass. Nevertheless, to strengthen this distinction, we will add a new control experiment in the revised version of §4.1. This will involve comparing performance when selecting low-variance trajectories based on action magnitude or frequency content alone versus using the full SAL metric. We expect this to show additional benefits from the motor control grounding, and the results will be reported accordingly. revision: yes

  2. Referee: [§4.3] §4.3 (Real-world Manipulation): The 20% improvement with TED filtering using half the data requires evidence that the phase-boundary/contact signal does not inadvertently filter out valid exploratory or corrective trajectories that are high-quality but less smooth, as this would undermine the proxy relationship asserted in the introduction.

    Authors: This is a valid concern, as overly aggressive filtering could exclude useful variability. However, our TED metric is designed to measure geometric deviation within phase boundaries defined by contact signals, aiming to retain trajectories that are consistent with skilled execution rather than erratic movements. In the real-world experiments, the selected data led to improved policy performance, suggesting that the retained trajectories are of higher quality for imitation. To provide the requested evidence, we will include in the revision an analysis of the discarded trajectories, examining if they contain exploratory or corrective elements that might be valuable, and discuss the implications for the smoothness-quality proxy. This may involve qualitative examples or quantitative metrics on the filtered set. revision: yes

Circularity Check

0 steps flagged

No circularity: metrics defined independently from motor-control theory and trajectory properties; downstream gains are measured outcomes, not definitional inputs.

full rationale

The paper defines SAL and TED directly from spectral and geometric properties of trajectories, citing external motor-control literature for the smoothness-quality link rather than deriving the metrics from policy performance or the same experimental outcomes. No equations reduce the quality score to a fit on the reported success rates; filtering is applied first, then BC performance is measured separately on RoboMimic and real-world tasks. Self-citations, if any, are not load-bearing for the core proxy claim, and the reported improvements (16% success with 1/6 data, 20% with half data) are falsifiable empirical results rather than tautological re-statements of the input metrics. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; full paper likely contains additional details on metric definitions and experimental protocols.

axioms (1)
  • domain assumption Smoothness is a hallmark of skilled movement according to motor control theory
    Invoked to justify using smoothness as a quality signal for demonstrations.

pith-pipeline@v0.9.0 · 5615 in / 1156 out tokens · 48657 ms · 2026-05-08T11:31:40.269034+00:00 · methodology

discussion (0)

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