Papers
23
Total Citations
516
H-Index
14
About
Felix Reinhart is a leading figure in the intersection of robotics, machine learning, and control theory, with a core focus on developing hybrid analytical and data-driven models for robot control. His major contributions lie in creating neural network architectures that learn and generate stable, goal-directed movements for both rigid and soft robotic systems. Reinhart’s work on recurrent neural networks for learning inverse kinematics and vector fields has been foundational, enabling robots to autonomously master complex tasks like reaching and manipulation. His research on hybrid modeling, which combines mechanical principles with data-driven learning, has been particularly impactful for soft robotics, where traditional models often fail. With over 370 citations across his top ten papers, his 2017 work on hybrid feed-forward control has garnered 81 citations alone. Notably, Reinhart has pioneered control strategies for the Bionic Handling Assistant, a large pneumatic soft robot, and developed skill babbling approaches for autonomous motor skill exploration. His innovative use of reservoir computing and multi-stable attractor dynamics has advanced the field of compliant and safe human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †81 citations · 2017
- 2Neural learning of vector fields for encoding stable dynamical systems45 citations · 2014
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- 8An active compliant control mode for interaction with a pneumatic soft robot23 citations · 2014
- 9Autonomous exploration of motor skills by skill babbling23 citations · 2016
- 10A multi-level control architecture for the bionic handling assistant23 citations · 2015