Action Representations in Robotics: A Taxonomy and Systematic Classification
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Abstract (arXiv)
Understanding and defining the meaning of "action" is substantial for robotics research. This becomes utterly evident when aiming at equipping autonomous robots with robust manipulation skills for action execution. Unfortunately, to this day we still lack both a clear understanding of the concept of an action and a set of established criteria that ultimately characterize an action. In this survey we thus first review existing ideas and theories on the notion and meaning of action. Subsequently we discuss the role of action in robotics and attempt to give a seminal definition of action in accordance with its use in robotics research. Given this definition we then introduce a taxonomy for categorizing action representations in robotics along various dimensions. Finally, we provide a systematic literature survey on action representations in robotics where we categorize relevant literature along our taxonomy. After discussing the current state of the art we conclude with an outlook towards promising research directions.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 4.
9 papers.
- 2025Action Tokenizer Matters in In-Context Imitation Learningcited by 12
- 2026Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Modelscited by 8
- 2023A Glimpse in ChatGPT Capabilities and its impact for AI researchcited by 6
- 2021Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchycited by 4
- 2026SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Modelscited by 4
- 2022Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learningcited by 3
- 2024Unsupervised Learning of Effective Actions in Roboticscited by 0
- 2026A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Roboticscited by 0
- 2026Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentationcited by 0
- 2026Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation13 Jul 2026
- 2026Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models13 May 2026
- 2026A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics16 Apr 2026
- 2026SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models20 Jan 2026
- 2025Action Tokenizer Matters in In-Context Imitation Learning3 Mar 2025
- 2024Unsupervised Learning of Effective Actions in Robotics3 Apr 2024
- 2023A Glimpse in ChatGPT Capabilities and its impact for AI research10 May 2023
- 2022Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning11 Feb 2022
- 2021Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy19 Feb 2021