A Dataset of Daily Interactive Manipulation
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
6 papers.
- 2022Factory: Fast Contact for Robotic Assemblycited by 49
- 2023Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspectivecited by 10
- 2024Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliabilitycited by 6
- 2021Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Graspcited by 5
- 2025Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interactioncited by 4
- 2021Pouring Dynamics Estimation Using Gated Recurrent Unitscited by 1
- 2025Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction7 Mar 2025
- 2024Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability15 Jan 2024
- 2023Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective9 Mar 2023
- 2022Factory: Fast Contact for Robotic Assembly7 May 2022
- 2021Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp30 Nov 2021
- 2021Pouring Dynamics Estimation Using Gated Recurrent Units8 May 2021