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About

Carlo Alessi is an emerging researcher specializing in soft robotics, continuum robot modeling, and learning-based control systems. His work addresses one of the field's most pressing challenges: developing effective controllers for soft robotic arms whose nonlinear material properties make traditional modeling approaches inadequate. Alessi's most significant contributions center on combining physics-based mechanical models with deep reinforcement learning to achieve reliable control of pneumatic soft robotic arms. His development of Cosserat rod-based dynamic models, capable of capturing complex behaviors like combined stretching and bending in 3D-printed systems, has provided a rigorous foundation for learning-based control pipelines. His most-cited work on pushing tasks via deep reinforcement learning (20 citations) demonstrates how soft robots can adaptively interact with unstructured environments—a critical capability for real-world deployment. Beyond manipulation, Alessi has contributed broadly to the community through comprehensive reviews of rod models in continuum robot control and critical analyses of modeling choices for learning controllers. His recent SoftTex project, integrating textile-based proprioception into soft arms, signals an expanding interest in embodied sensing. With over 60 cumulative citations across publications spanning just two years, Alessi is establishing himself as a notable voice in intelligent soft robotics research.