Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning
RINSE evaluates demonstration quality via trajectory smoothness, achieving 16% higher success on RoboMimic with one-sixth data.
Key Findings
Methodology
RINSE framework evaluates demonstration quality via trajectory smoothness using two complementary metrics: Spectral Arc Length (SAL) and Trajectory-Envelope Distance (TED). SAL measures frequency-domain regularity, while TED accounts for contact-aware geometric deviation. This method is policy-architecture-agnostic and requires no policy training.
Key Results
- On RoboMimic benchmarks, SAL filtering achieves 16% higher success using one-sixth of the data.
- In real-world manipulation, TED filtering achieves 20% improvement with half the data.
- On LIBERO-10, RINSE re-ranking improves mean success by 5.6%.
Significance
RINSE provides a lightweight, architecture-agnostic method for evaluating demonstration quality, addressing the issue of uneven data quality in behavioral cloning. By filtering based on smoothness, it reduces the conditional action variance of the retained data distribution, enhancing policy performance.
Technical Contribution
RINSE achieves rapid evaluation of demonstration quality through smoothness metrics, avoiding costly policy training. Theoretical analysis shows that smoothness filtering effectively reduces conditional action variance, improving the learning signal in behavioral cloning.
Novelty
RINSE is the first to use smoothness as a core metric for demonstration quality, combining spectral and geometric analysis to provide a quality evaluation method without policy training.
Limitations
- RINSE may be less effective in handling extremely noisy data, as smoothness metrics might not fully capture complex action variations.
- TED may be affected by contact state segmentation in contact-intensive tasks.
Future Work
Future research could explore RINSE's applicability to more tasks and datasets, particularly in multimodal data and complex tasks.
AI Executive Summary
Behavioral cloning (BC) is widely used in robot learning, but its performance is limited by the quality of demonstration data. Traditional methods often treat all demonstrations equally, ignoring the impact of operator skill differences and data noise. The RINSE framework evaluates demonstration quality via trajectory smoothness using Spectral Arc Length (SAL) and Trajectory-Envelope Distance (TED) metrics, providing a lightweight, architecture-agnostic solution.
The core of RINSE lies in its theoretical foundation: smoothness is a hallmark of skilled movement. By filtering out unsmooth demonstrations, it reduces the conditional action variance of the retained data. On RoboMimic benchmarks, SAL filtering achieves 16% higher success using one-sixth of the data; in real-world manipulation, TED filtering achieves 20% improvement with half the data. These results indicate that smoothness is an effective quality signal, especially in noisy or heterogeneous data environments.
However, RINSE has its limitations. It may be less effective in handling extremely noisy data, as smoothness metrics might not fully capture complex action variations. Additionally, TED may be affected by contact state segmentation in contact-intensive tasks. Future research could explore RINSE's applicability to more tasks and datasets, particularly in multimodal data and complex tasks.
Deep Analysis
Background
Behavioral cloning (BC) is a method of training policies to mimic expert demonstrations through supervised learning, widely used in learning robotic manipulation skills. However, the effectiveness of BC is constrained by the quality of training data. Real-world demonstration datasets are inherently heterogeneous due to operator skill, fatigue, and strategy differences, as well as device-specific artifacts introduced by teleoperation interfaces. Standard BC treats all demonstrations equally, ignoring these differences.
Core Problem
The core problem in BC is the uneven quality of demonstration data, which inflates the noise floor of the training objective. Existing curation methods require costly policy training or manual annotation, limiting scalability. Therefore, a lightweight, architecture-agnostic method for evaluating demonstration quality is needed.
Innovation
The RINSE framework evaluates demonstration quality via trajectory smoothness using two metrics: Spectral Arc Length (SAL) and Trajectory-Envelope Distance (TED). SAL measures frequency-domain regularity, while TED accounts for contact-aware geometric deviation. This method is policy-architecture-agnostic and requires no policy training.
Methodology
- �� Use Spectral Arc Length (SAL) to assess frequency-domain regularity, suitable for detecting high-frequency jitter in free-space motions.
- �� Use Trajectory-Envelope Distance (TED) to assess contact-aware geometric deviation, suitable for detecting spatial artifacts in contact-rich tasks.
- �� Theoretical analysis shows that smoothness filtering effectively reduces conditional action variance, improving the learning signal in behavioral cloning.
Experiments
Experiments were conducted on RoboMimic benchmarks and real-world manipulation tasks, using Diffusion Policy to train policies. Performance of subsets filtered by TED or SAL was compared against full datasets. On LIBERO-10, RINSE was combined with STRAP for sub-trajectory retrieval, evaluating the impact of re-ranking on success rates.
Results
On RoboMimic benchmarks, SAL filtering achieves 16% higher success using one-sixth of the data. In real-world manipulation, TED filtering achieves 20% improvement with half the data. On LIBERO-10, RINSE re-ranking improves mean success by 5.6%. These results indicate that smoothness is an effective quality signal.
Applications
RINSE can be used for demonstration data filtering in robot learning, improving the efficiency and effectiveness of policy learning. It is applicable to various policy architectures, particularly effective in noisy or heterogeneous data environments.
Limitations & Outlook
RINSE may be less effective in handling extremely noisy data, as smoothness metrics might not fully capture complex action variations. Additionally, TED may be affected by contact state segmentation in contact-intensive tasks.
Plain Language Accessible to non-experts
Imagine you're in a kitchen preparing a meal. You have a bunch of ingredients, but some aren't fresh or are poorly chopped. You need to select the best ingredients to make a delicious dish. RINSE acts like a smart chef, selecting the best ingredients by checking their smoothness (like checking color and smell). This way, you can make a tastier dish with fewer ingredients. RINSE uses this method to enhance the efficiency and effectiveness of robot learning.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to mimic a pro player's moves to win. There are many videos of pros, but some are blurry or not smooth. RINSE is like a smart assistant that helps you pick the smoothest videos, making it easier to learn the pro's techniques. This way, you can improve your game skills with less time and effort. Isn't that cool?
Glossary
Behavioral Cloning
A machine learning method for training policies by mimicking expert demonstrations.
Used for training robotic manipulation skills.
Spectral Arc Length (SAL)
A metric that measures the regularity of a trajectory in the frequency domain.
Used to assess trajectory smoothness.
Trajectory-Envelope Distance (TED)
A metric that measures geometric deviation in a contact-aware manner.
Used to assess trajectory smoothness.
RoboMimic
A benchmark dataset for evaluating robot learning algorithms.
Used to test the effectiveness of RINSE.
STRAP
A framework for sub-trajectory retrieval.
Combined with RINSE for experiments on LIBERO-10.
Open Questions Unanswered questions from this research
- 1 How can RINSE be effectively applied to extremely noisy data? Current methods may not fully capture complex action variations, requiring new metrics or methods.
- 2 How can TED's performance be optimized in contact-intensive tasks? Contact state segmentation may affect its effectiveness.
Applications
Immediate Applications
Robot Demonstration Data Filtering
RINSE can be used to filter high-quality robot demonstration data, improving the efficiency and effectiveness of policy learning.
Long-term Vision
Multimodal Data Processing
RINSE's smoothness metrics can be extended to multimodal data, enhancing data quality evaluation in complex tasks.
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 $ρ\geq 0.89$). These results support smoothness as a useful quality signal across filtering, retrieval, and reweighting settings, especially in noisy or heterogeneous data regimes.