Felix Ruppert

Max Planck Institute for Intelligent Systems

Papers

Total Citations

H-Index

About

Felix Ruppert is a roboticist at the forefront of bio-inspired locomotion, where he bridges the gap between animal neuromechanics and agile robot control. His research focuses on how robots can learn to exploit the passive, elastic intelligence embedded in their own mechanical bodies—a concept known as morphological computation. Ruppert’s major contributions include demonstrating that robots can learn to tune their leg dynamics in real-time, using closed-loop central pattern generators (CPGs) to achieve energy-efficient hopping and running without explicit models. His 2022 paper on "plastic matching" in CPGs (37 citations) shows how a robot can adapt its neural control to resonate with its leg’s series elastic actuators, reducing control effort. Earlier, his 2019 work on biarticular muscle-tendon structures (35 citations) provided a principled design for robotic legs that mimic the serial elasticity of biological limbs. Ruppert’s 2017 paper on "shaping in practice" (9 citations) pioneered a hardware-friendly learning approach using training wheels to safely explore unstable gaits. His work has been published in top venues like *Nature Communications* and *IEEE Transactions on Robotics*, and he is recognized for advancing the frontier of learning directly in hardware—a critical step toward truly autonomous, resilient robots.