ArXiv

Scalable Behavior Cloning with Open Data, Training, and Evaluation

2026-06-25 · 17:59 UTC ·Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh... ·1 min read
Authors
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh...
Categories
cs.RO
arXiv
https://arxiv.org/abs/2606.27375v1
PDF
https://arxiv.org/pdf/2606.27375v1

Brief

ABC presents a fully open-source behavior-cloning stack centered on ABC-130K (3,500 hours, ~130K episodes, 195 tasks), plus hardware, training, and simulation releases. They add 400 hours of sim-teleop data and a co-training recipe that correlates sim and real evaluations, compare DiT and VLA architectures, and show policies succeeding on dexterous manipulation, enabling reproducible research.

Why it matters

ABC-130K: released the largest open-source teleoperation dataset to date with 3,500 hours across ~130K episodes covering 195 manipulation tasks (paper posted 2026-06-25).

Key details

  • Open-source stack: published hardware designs, training infrastructure, a simulation pipeline, plus 400 hours of sim-teleop data and a co-training recipe that yields correlated simulation↔real evaluations for low-cost ablation.
  • Empirical study: compared Diffusion Transformers (DiT) and Vision-Language-Action (VLA) models, validated on real-world dexterous tasks (e.g., box folding and extracting credit cards from wallets) and released a reproducible toolkit.
Source evidence

Abstract

We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500 hours of data spanning over 130K episodes across 195 diverse tasks. Furthermore, we open-source our accessible hardware setup, training infrastructure, and simulation pipeline. We also release 400 hours of sim-teleop data and provide a co-training recipe that produces correlated simulation and real-world evaluation, offering a reliable proxy for ablating model-design and training decisions before costly real-world evaluation. We explore various training recipes and compare common architectural choices for Diffusion Transformers (DiT) and Vision-Language-Action (VLA) models, grounding our findings in real-world evaluations. The resulting policies successfully execute dexterous tasks such as box folding and extracting credit cards from wallets. By providing a reproducible toolkit, we aim to place researchers on an equal footing, establishing the necessary foundation to learn the ABCs of Behavior Cloning together as a community.

Comment: 30 pages. Project page: https://abc.bot