1. [Publications](/publications)
2. Model Predictive Control for Fluid Human-to-Robot Handovers
 
 # Model Predictive Control for Fluid Human-to-Robot Handovers

  ![](/sites/default/files/publications/cover_banner.gif) 

 Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handovers of unknown objects by using learning-based grasp generators. However, how to responsively generate smooth motions to take an object from a human is still an open question. Specifically, planning motions that take human comfort into account is not a part of the human-robot handover process in most prior works. In this paper, we propose to generate smooth motions via an efficient model-predictive control (MPC) framework that integrates perception and complex domain-specific constraints into the optimization problem. We introduce a learning-based grasp reachability model to select candidate grasps which maximize the robot’s manipulability, giving it more freedom to satisfy these constraints. Finally, we integrate a neural net force/torque classifier that detects contact events from noisy data. We conducted human-to-robot handover experiments on a diverse set of objects with several users (N=4) and performed a systematic evaluation of each module. The study shows that the users preferred our MPC approach over the baseline system by a large margin.



 ## Authors



[Wei Yang](/person/wei-yang)

[Balakumar Sundaralingam](/person/balakumar-sundaralingam)

Chris Paxton (Meta)

[Iretiayo Akinola](/person/iretiayo-akinola)

[Yu-Wei Chao](/person/yu-wei-chao)

Maya Cakmak (University of Washington)

Dieter Fox (NVIDIA)

 

 

 ## Publication Date



Thursday, April 7, 2022

 

 ## Published in



[IEEE International Conference on Robotics and Automation (ICRA) 2022](https://www.icra2022.org/)

 

 ## Research Area



[Human Computer Interaction](/research-area/human-computer-interaction)

[Robotics](/research-area/robotics)

 

 

 ## External Links



[arXiv](https://arxiv.org/abs/2204.00134)

[Project](https://sites.google.com/nvidia.com/mpc-for-handover)