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

2026-04-29 · Sergej Stanovcic, Daniel Sliwowski, Dongheui Lee 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.

📄 PDF Abstract BibTeX arXiv:2604.26637

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning Action Segmentation

AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents

2026-05-18 · Pan Wang, Yihao Hu, Xiujin Liu, Jingchu Yang 외 arxiv

Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most existing frameworks store memory as text and depend on proprietary t…

Reinforcement Learning Decision Making

Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents

2026-06-22 · Haoran Zhang, Yifu Lu, Boyang Wang, Xuhui Kang 외 arxiv

Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored. Detecting failures in long-horizon robotic tasks is particularly challenging because failure on…

AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models

2026-08-07 · Guiyu Zhao, Longteng Guo, Yanghong Mei, Zilin Zhu 외 hf

While Vision-Language-Action (VLA) models have advanced embodied AI, their fundamentally reactive paradigm severely limits performance in partially observable and long-horizon tasks. When restricted to a single wrist-mou…

Spatial Reasoning

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

2016-04-29 · Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner 외

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for tra…

Cardiac Segmentation Medical Image Analysis Segmentation

Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces

2026-03-05 · Karan Gupta, Pranav Vajreshwari, Yash Pandya, Raghav Magazine 외 arxiv

Agentic systems operating over large tool ecosystems must plan and execute long-horizon workflows under weak or non-verifiable supervision. While frontier models mitigate these challenges through scale and large context …