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

Problem
This paper addresses the limitations of existing annotation tools for long-horizon robotic action segmentation, particularly in the context of multi-modal data integration. Current tools primarily focus on vision-only data and lack support for synchronized visualization of robot-specific time-series signals, such as gripper states or force/torque measurements. Additionally, they often require significant adaptation efforts for different dataset formats. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce ATLAS, an annotation tool specifically designed for long-horizon robotic action segmentation. ATLAS features a time-synchronized visualization interface that integrates multi-modal robotic data, including multi-view video and proprioceptive signals. The tool supports the annotation of action boundaries, action labels, and task outcomes. It natively accommodates popular robotics dataset formats, such as ROS bags and the Reinforcement Learning Dataset (RLDS) format, and includes direct support for specific datasets like REASSEMBLE. A modular dataset abstraction layer allows for easy extension to new formats. The interface is keyboard-centric, aimed at minimizing annotation effort and enhancing efficiency.

Results
In empirical evaluations conducted on a contact-rich assembly task, ATLAS demonstrated a reduction in average per-action annotation time by at least 6% compared to the baseline tool ELAN. Furthermore, the integration of time-series data facilitated a more accurate temporal alignment with expert annotations, improving alignment by over 2.8%. The boundary error was significantly reduced, achieving a fivefold decrease compared to traditional vision-only annotation tools. These results indicate that ATLAS not only streamlines the annotation process but also enhances the quality of the annotations produced.

Limitations
The authors acknowledge that while ATLAS improves efficiency and accuracy, it may still be limited by the inherent challenges of annotating complex robotic actions, which can vary significantly across different tasks and environments. Additionally, the tool’s performance has not been evaluated across a wide range of robotic tasks beyond the specific assembly task used in the experiments. The modularity of the dataset abstraction layer, while a strength, may also introduce complexity for users unfamiliar with software development. The paper does not address potential scalability issues when applied to larger datasets or the integration of ATLAS with existing machine learning pipelines.

Why it matters
The development of ATLAS has significant implications for the field of robotic action segmentation and manipulation policy learning. By providing a robust tool for annotating long-horizon actions with precise temporal boundaries, ATLAS can facilitate the training of more accurate models for robotic manipulation tasks. The ability to integrate multi-modal data enhances the richness of the annotations, potentially leading to improved learning outcomes in downstream applications. This tool could serve as a foundation for future research in action recognition and policy learning, ultimately contributing to advancements in autonomous robotic systems.

Authors: Sergej Stanovcic, Daniel Sliwowski, Dongheui Lee
Source: arXiv:2604.26637
URL: https://arxiv.org/abs/2604.26637v1