Personalized Robot-assisted Dressing
Robots have the potential to provide tremendous support to disabled and elderly people in their everyday tasks, such as dressing. We address this problem by introducing an approach that allows the robot to automatically adapt its motion according to the force applied on the robot's gripper caused by user movements. There are two main contributions introduced in our research: 1) the use of a hierarchical multi-task control strategy during the dressing process to automatically adapt therobot motion and minimize the force applied between the user and the robot caused by user movements; 2) the online update of the dressing trajectory based on the user movement limitations modeled with the Gaussian Process Latent Variable Model in a latent space, and the density information extracted from such latent space. The combination of these two contributions leads to a personalized dressing assistance that can cope with unpredicted user movements during the dressing while constantly minimizing the force that the robot may apply on the user. The experimental results demonstrate that the proposed method allows the Baxter humanoid robot to provide personalized dressing assistance for human users with simulated upper-body impairments.
For more details, please refer to the following publications.
- Zhang F, Cully A, Demiris Y (2017). Personalized Robot-assisted Dressing using User Modeling in Latent Spaces. International Conference on Intelligent Robots and Systems (IROS 2017).
- Gao Y, Chang HJ, Demiris Y, (2016). Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)
Gao Y, Chang HJ, Demiris Y (2015), User Modelling for Personalised Dressing Assistance by Humanoid Robots, IEEE International Conference on Intelligent Robots and Systems (IROS 2015), pp:1840-1845