Carlo Alessi- Master of Science
- PhD Student at Sant'Anna School of Advanced Studies
Carlo Alessi
- Master of Science
- PhD Student at Sant'Anna School of Advanced Studies
Machine Learning for Soft Robot control
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6
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Publications (6)
Soft robots are promising in biomedical applications thanks to their inherent structural compliance and distributed large deformations. However, integrating a sensory system that maintains the robot's dexterity while offering accurate state estimation remains an open challenge for their widespread adoption. This letter presents SoftTex, a small-sca...
Soft manipulators, renowned for their compliance and adaptability, hold great promise in their ability to engage safely and effectively with intricate environments and delicate objects. Nonetheless, controlling these soft systems presents distinctive hurdles owing to their nonlinear behavior and complicated dynamics. Learning‐based controllers for...
Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manip...
Continuum and soft robots can positively impact diverse sectors, from biomedical applications to marine and space exploration, thanks to their potential to adaptively interact with unstructured environments. However, the complex mechanics exhibited by these robots pose diverse challenges in modeling and control. Reduced order continuum mechanical m...
Recently, learning-based controllers that leverage mechanical models of soft robots have shown promising results. This paper presents a closed-loop controller for dynamic trajectory tracking with a pneumatic soft robotic arm learned via Deep Reinforcement Learning using Proximal Policy Optimization. The control policy was trained in simulation leve...
Ongoing advancements in the design and fabrication of soft robots are creating new challenges in modeling and control. This paper presents a dynamic Cosserat rod model for a single-section 3D-printed pneumatic soft robotic arm capable of combined stretching and bending. The model captures the manufacturing variability of the actuators by tuning the...
