Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning
Ali Marjaninejad, Jie Tan, Francisco J. Valero-Cuevas

TL;DR
This study shows that adding elastic elements to tendon-driven robotic limbs can improve autonomous learning and performance, suggesting co-development of robot bodies and controllers can leverage elasticity benefits.
Contribution
It demonstrates that increased tendon stiffness enhances learning accuracy and robustness in autonomous control of tendon-driven limbs, revealing benefits of elastic elements in robotic systems.
Findings
Higher tendon stiffness requires more learning iterations but improves task accuracy.
The system adapts quickly to changes in muscle stiffness within five attempts.
Elastic elements contribute to improved locomotion performance.
Abstract
Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which makes them more challenging to model and control. The impact of this added complexity to autonomous learning has not been thoroughly explored. This is especially relevant to tendon-driven limbs whose cables and tendons are inevitably elastic. Here, we explored the efficacy of autonomous learning and control on a simulated bio-plausible tendon-driven leg across different tendon stiffness values. We demonstrate that increasing stiffness of the simulated muscles can require more iterations for the inverse map to converge but can then perform more accurately, especially in discrete tasks. Moreover, the system is robust to subsequent changes in muscle stiffnesses and can adapt on-the-go within 5 attempts. Lastly, we…
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