# REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

arXiv: https://arxiv.org/abs/2502.05086 · HTML: https://mlsanity.com/paper/2502.05086/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 14 of 14)

- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [Vision Language Action Models in Robotic Manipulation: A Systematic Review](https://mlsanity.com/paper/2507.10672/) (2025)
- [Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective](https://mlsanity.com/paper/2509.09154/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter](https://mlsanity.com/paper/2602.05513/) (2026)
- [SELF-VLA: A Skill Enhanced Agentic Vision-Language-Action Framework for Contact-Rich Disassembly](https://mlsanity.com/paper/2603.11080/) (2026)
- [AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly](https://mlsanity.com/paper/2604.08983/) (2026)
- [Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation](https://mlsanity.com/paper/2609.01596/) (2026)
- [M2R2: MultiModal Robotic Representation for Temporal Action Segmentation](https://mlsanity.com/paper/2504.18662/) (2025)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact](https://mlsanity.com/paper/2604.12905/) (2026)
- [A Comprehensive Review of Generative Physical Artificial Intelligence](https://mlsanity.com/paper/2609.18111/) (2026)
- [VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation](https://mlsanity.com/paper/2609.22606/) (2026)
- [ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation](https://mlsanity.com/paper/2609.34893/) (2026)

## Abstract (arXiv)

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data, including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and task inversion learning. The REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios. The dataset is publicly available on our project website: https://tuwien-asl.github.io/REASSEMBLE_page/.