# Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics

arXiv: https://arxiv.org/abs/2201.10392 · HTML: https://mlsanity.com/paper/2201.10392/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [A Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2406.01832/) (2024)
- [Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition](https://mlsanity.com/paper/2203.11007/) (2022)
- [Co-manipulation of soft-materials estimating deformation from depth images](https://mlsanity.com/paper/2301.05609/) (2023)
- [Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport](https://mlsanity.com/paper/2603.03768/) (2026)
- [Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport](https://mlsanity.com/paper/2609.25351/) (2026)

## Abstract (arXiv)

In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and highly deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners.