Anticipating daily human actions: comparing pipelines for long-term skeleton-based prediction in real-world scenarios
Abstract
Human action anticipation remains a key challenge to achieve efficient human-robot interaction due to the difficulties to learn the higher level of abstraction. This work explores three action anticipation pipelines as a guideline for future work. Specifically, two pipelines adopt a top-down approach: they recognize current actions and then anticipate future actions using either traditional machine learning models or Large Language Models (LLMs). The third pipeline follows a bottom-up strategy by first forecasting future motions and then inferring actions. Our results show that top-down pipelines achieve higher accuracy and robustness, demonstrating the advantage of abstract reasoning over direct motion-based inference.
Fields of Study & Subjects
Human Pose and Action Recognition
Human Motion and Animation
Hand Gesture Recognition Systems
Publication details
- Journal / source
- Advanced Robotics
- Volume
- 40
- Issue
- 10
- Pages
- 551–560
- Publication date
- 24 February 2026
- Language
- EN