# Hoi! - A Multimodal Dataset for Force-Grounded, Cross-View Articulated Manipulation

arXiv: https://arxiv.org/abs/2512.04884 · HTML: https://mlsanity.com/paper/2512.04884/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2025.

## Built on by (most cited first, 10 of 10)

- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control](https://mlsanity.com/paper/2606.24208/) (2026)
- [EgoFun3D: Modeling Interactive Objects from Egocentric Videos using Function Templates](https://mlsanity.com/paper/2604.11038/) (2026)
- [FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations](https://mlsanity.com/paper/2609.20817/) (2026)
- [ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction](https://mlsanity.com/paper/2609.21751/) (2026)
- [ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning](https://mlsanity.com/paper/2609.39665/) (2026)
- [ROMA: LLM System for Real-World Object-Centric Multi-Sensory Active Perception](https://mlsanity.com/paper/2610.06955/) (2026)
- [Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmark](https://mlsanity.com/paper/2610.13947/) (2026)

## Abstract (arXiv)

We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during real human interaction. The dataset contains 3048 sequences across 381 articulated objects in 38 environments. Each object is operated in four embodiments - (i) human hand, (ii) human hand with a wrist-mounted camera, (iii) handheld UMI gripper, and (iv) a custom Hoi! gripper, where the tool embodiment provides end-effector forces and tactile sensing. Our dataset offers a holistic view of interaction understanding from video, enabling researchers to evaluate how well methods transfer between human and robotic viewpoints, but also investigate underexplored modalities such as interaction forces. The Project Website can be found at https://timengelbracht.github.io/Hoi-Dataset-Website/.