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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Passive elastic elements can contribute to stability, energetic efficiency,and impact absorption in both biological and robotic systems. They also adddynamical complexity which makes them more challenging to model and control.The impact of this added complexity to autonomous learning has not beenthoroughly explored. This is especially relevant to tendon-driven limbs whosecables and tendons are inevitably elastic. Here, we explored the efficacy ofautonomous learning and control on a simulated bio-plausible tendon-driven legacross different tendon stiffness values. We demonstrate that increasingstiffness of the simulated muscles can require more iterations for the inversemap to converge but can then perform more accurately, especially in discretetasks. Moreover, the system is robust to subsequent changes in musclestiffnesses and can adapt on-the-go within 5 attempts. Lastly, we test thesystem for the functional task of locomotion, and found similar effects ofmuscle stiffness to learning and performance. Given that a range of stiffnessvalues led to improved learning and maximized performance, we conclude therobot bodies and autonomous controllers---at least for tendon-drivensystems---can be co-developed to take advantage of elastic elements.Importantly, this opens also the door to development efforts that recapitulatethe beneficial aspects of the co-evolution of brains and bodies in vertebrates.

Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning