English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To overcome the obstructions imposed by high-dimensional bipedal models, weembed a stable walking motion in an attractive low-dimensional surface of thesystem's state space. The process begins with trajectory optimization to designan open-loop periodic walking motion of the high-dimensional model and thenadding to this solution, a carefully selected set of additional open-looptrajectories of the model that steer toward the nominal motion. A drawback oftrajectories is that they provide little information on how to respond to adisturbance. To address this shortcoming, Supervised Machine Learning is usedto extract a low-dimensional state-variable realization of the open-looptrajectories. The periodic orbit is now an attractor of the low-dimensionalstate-variable model but is not attractive in the full-order system. We thenuse the special structure of mechanical models associated with bipedal robotsto embed the low-dimensional model in the original model in such a manner thatthe desired walking motions are locally exponentially stable. The designprocedure is first developed for ordinary differential equations andillustrated on a simple model. The methods are subsequently extended to a classof hybrid models and then realized experimentally on an Atrias-series 3Dbipedal robot.

Combining Trajectory Optimization, Supervised Machine Learning, and  Model Structure for Mitigating the Curse of Dimensionality in the Control of  Bipedal Robots