# Characterizing Input Methods for Human-to-robot Demonstrations

arXiv: https://arxiv.org/abs/1902.00084 · HTML: https://mlsanity.com/paper/1902.00084/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning](https://mlsanity.com/paper/2509.18757/) (2025)
- [Data-efficient learning of object-centric grasp preferences](https://mlsanity.com/paper/2203.00384/) (2022)

## Abstract (arXiv)

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.