Control for and interaction with humanoid robots is often restrictive due to limitations of the robot platform and the high dimensionality of controlling systems with many degrees of freedom. We focus on the problem of providing a "skill-level interface" for a humanoid robot. Such an interface serves as (i) a modular foundation for structuring task-oriented control, (ii) a parsimonious abstraction of motor-level control (e.g. PD-servo control), and (iii) a means for grounding interactions between humans and robots through common skill vocabularies. Our approach to constructing skill-level interfaces is two-fold. First, we propose a representation for a skill-level interface as a "behavior vocabulary," a repertoire of modular exemplar-based memory models expressing kinematic motion. A module in such a vocabulary encodes a flow field (or gradient field) in joint angle space that describes the "flow" of kinematic motion for a particular skill-level behavior, enabling prediction from a given kinematic configuration. Second, we propose a data-driven method for deriving behavior vocabularies from time-series data of human motion using spatio-temporal dimension reduction and clustering. Results from evaluating an implementation of our methodology are presented along with the application of derived behavior vocabularies as predictors towards on-line humanoid trajectory formation and off-line motion synthesis.
Evolutionary motion design for humanoid robots
