# A Dataset of Daily Interactive Manipulation

arXiv: https://arxiv.org/abs/1807.00858 · HTML: https://mlsanity.com/paper/1807.00858/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2303.05344/) (2023)
- [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024)
- [Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp](https://mlsanity.com/paper/2112.01270/) (2021)
- [Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction](https://mlsanity.com/paper/2503.05231/) (2025)
- [Pouring Dynamics Estimation Using Gated Recurrent Units](https://mlsanity.com/paper/2105.12828/) (2021)

## Abstract (arXiv)

Robots that succeed in factories stumble to complete the simplest daily task humans take for granted, for the change of environment makes the task exceedingly difficult. Aiming to teach robot perform daily interactive manipulation in a changing environment using human demonstrations, we collected our own data of interactive manipulation. The dataset focuses on position, orientation, force, and torque of objects manipulated in daily tasks. The dataset includes 1,593 trials of 32 types of daily motions and 1,596 trials of pouring alone, as well as helper code. We present our dataset to facilitate the research on task-oriented interactive manipulation.