# ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation

arXiv: https://arxiv.org/abs/2604.26637 · HTML: https://mlsanity.com/paper/2604.26637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning](https://mlsanity.com/paper/2111.02767/) (2021)
- [OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation](https://mlsanity.com/paper/2309.06276/) (2023)
- [ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly](https://mlsanity.com/paper/2409.16451/) (2024)
- [ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction](https://mlsanity.com/paper/2501.07051/) (2025)
- [M2R2: MultiModal Robotic Representation for Temporal Action Segmentation](https://mlsanity.com/paper/2504.18662/) (2025)

## Abstract (arXiv)

Annotating long-horizon robotic demonstrations with precise temporal action boundaries is crucial for training and evaluating action segmentation and manipulation policy learning methods. Existing annotation tools, however, are often limited: they are designed primarily for vision-only data, do not natively support synchronized visualization of robot-specific time-series signals (e.g., gripper state or force/torque), or require substantial effort to adapt to different dataset formats. In this paper, we introduce ATLAS, an annotation tool tailored for long-horizon robotic action segmentation. ATLAS provides time-synchronized visualization of multi-modal robotic data, including multi-view video and proprioceptive signals, and supports annotation of action boundaries, action labels, and task outcomes. The tool natively handles widely used robotics dataset formats such as ROS bags and the Reinforcement Learning Dataset (RLDS) format, and provides direct support for specific datasets such as REASSEMBLE. ATLAS can be easily extended to new formats via a modular dataset abstraction layer. Its keyboard-centric interface minimizes annotation effort and improves efficiency. In experiments on a contact-rich assembly task, ATLAS reduced the average per-action annotation time by at least 6% compared to ELAN, while the inclusion of time-series data improved temporal alignment with expert annotations by more than 2.8% and decreased boundary error fivefold compared to vision-only annotation tools.