Title: Cost Functions for Robot Motion Style,
Abstract: We focus on autonomously generating robot motion for day to day physical
tasks that is expressive of a certain style or emotion. Because we seek
generalization across task instances and task types, we propose to capture
style via cost functions that the robot can use to augment its nominal task
cost and task constraints in a trajectory optimization process. We compare two
approaches to representing such cost functions: a weighted linear combination
of hand-designed features, and a neural network parameterization operating on
raw trajectory input. For each cost type, we learn weights for each style from
user feedback. We contrast these approaches to a nominal motion across
different tasks and for different styles in a user study, and find that they
both perform on par with each other, and significantly outperform the baseline.
Each approach has its advantages: featurized costs require learning fewer
parameters and can perform better on some styles, but neural network
representations do not require expert knowledge to design features and could
even learn more complex, nuanced costs than an expert can easily design.