Optimization of Robot Configurations for Assistive Tasks

Robots can provide assistance with activities of daily living (ADLs) to humans with motor impairments. Specialized robots, such as desktop robotic feeding systems, have been successful for specific assistive tasks when placed in fixed and designated positions with respect to the user. General-purpose mobile manipulators could act as a more versatile form of assistive technology, able to perform many tasks, but selecting a configuration for the robots from which to perform a task can be challenging due to the high number of degrees of freedom of the robots and the complexity of the tasks. As with the specialized, fixed robots, once in a good configuration, another system or the user can provide the fine control to perform the details of the task. In this short paper, we present Task-centric Optimization of robot Configurations (TOC), a method for selecting configurations for a PR2 and a robotic bed to allow the PR2 to provide effective assistance with ADLs. TOC builds upon previous work, Taskcentric initial Configuration Selection (TCS), addressing some of the limitations of TCS. Notable alterations are selecting configurations from the continuous configuration space using a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization, introducing a joint-limit-weighted manipulability term, and changing the framework to move all optimization offline and using function approximation at run-time. To evaluate TOC, we created models of 13 activities of daily living (ADLs) and compared TOC’s and TCS’s performance with these 13 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TOC performed as well or better than TCS in most of our tests against state estimation error. We also implemented TOC on a real PR2 and a real robotic bed and found that from the TOC-selected configuration the PR2 could reach all task-relevant goals on a mannequin on the bed.