Diversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics Datasets

TL;DR

This paper introduces a fast, model-free diversity metric based on signature transform entropy for robotics datasets, and demonstrates its effectiveness in improving imitation learning success rates through diversity-aware data curation.

Contribution

The authors develop a novel, computationally efficient diversity metric for robot datasets and propose FAKTUAL, a data curation method that enhances imitation learning by maximizing dataset entropy.

Findings

Diversity-aware data curation improves success rates across multiple benchmarks.

FAKTUAL requires no access to policies or rollouts, adding minimal overhead.

The entropy metric effectively measures dataset diversity and correlates with generalization performance.

Abstract

Robotics datasets for imitation learning typically consist of long-horizon trajectories of different lengths over states, actions, and high-dimensional observations (e.g., RGB video), making it non-trivial to quantify diversity in a way that respects the underlying trajectory structure and geometry. We extend Shannon and von Neumann entropy to this setting by defining signature transform-based entropy on the Gram matrix of a signature kernel over demonstrations, yielding entropy and diversity metrics that operate directly on the demonstration dataset. Building on these metrics, we study how dataset diversity affects generalization performance in robot imitation learning and propose a simple, model-free way to curate diverse demonstrations. We introduce FAKTUAL (FAst trajectory Kernel enTropy cUration for imitation Learning), a data curation algorithm that selects a subset of demonstrations maximizing entropy given a subset-size budget. FAKTUAL is fully model-free, requires no access to the imitation policy or rollouts, and adds negligible overhead relative to policy training. We evaluate our approach on image and state-based RoboMimic and MetaWorld benchmarks, as well as four real-world manipulation tasks. Across tasks and architectures, diversity-aware curation with FAKTUAL consistently improves downstream success rates over random selection, while being substantially more computationally efficient compared to recent robot data curation methods. Our results suggest that the entropy of demonstration datasets is a practical tool for understanding and improving dataset diversity in robot imitation learning.