Robot-driven Trajectory Improvement for Feeding Tasks

Travers Rhodes and Manuela M. Veloso
Conference Paper, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2991-2996, October, 2018

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Kinesthetic learning is a type of learning from demonstration in which the teacher manually moves the robot through the demonstrated trajectory. It shows great promise in the area of assistive robotics since it enables a caretaker who is not an expert in computer programming to communicate a novel task to an assistive robot. However, the trajectory the caretaker demonstrates to solve the task may be a high-cost trajectory for the robot. The demonstrated trajectory could be high-cost because the teacher does not know what trajectories are easy or hard for the robot to perform, which would be due to a limitation of the teacher’s knowledge, or because the teacher has difficulty moving all the robotic joints precisely along the desired trajectories, which would be due to a limitation of the teacher’s coordination. We propose the Parameterized Similar Path Search (PSPS) algorithm to extend kinesthetic learning so that a robot can improve the learned trajectory over a known cost function. This algorithm is based on active learning from the robot through collaboration between the robot’s knowledge of the cost function and the caretaker’s knowledge of the constraints of the assigned task.

author = {Travers Rhodes and Manuela M. Veloso},
title = {Robot-driven Trajectory Improvement for Feeding Tasks},
booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2018},
month = {October},
pages = {2991-2996},
publisher = {IEEE},
keywords = {Trajectory , Task analysis , Robot kinematics , Cost function , Training , Manipulators},
} 2019-02-06T13:32:26-04:00