Papers
10
Total Citations
238
H-Index
7
About
Luca Arleo is a robotics researcher specializing in soft robotics, with particular expertise in control strategies, fabrication techniques, and variable stiffness technologies. His work addresses some of the field's most persistent challenges: bridging the gap between the mechanical promise of soft robotic systems and their practical deployment in real-world applications. Arleo's most influential contribution, "Closed-Loop Dynamic Control of a Soft Manipulator Using Deep Reinforcement Learning" (2022, 83 citations), tackled the notoriously difficult problem of controlling highly nonlinear soft systems by leveraging deep reinforcement learning — a landmark step toward autonomous soft robot operation. His parallel work on additive manufacturing (59 citations) demonstrated that airtight, monolithic pneumatic actuators could be reliably 3D-printed, significantly lowering barriers to soft robot fabrication. A recurring theme across his research is variable stiffness — the ability of soft robots to switch between compliant and rigid states — which he has explored through fiber jamming, layer jamming, and bioinspired seashell structures, accumulating dozens of additional citations. His contributions span medical robotics, assistive technology, and proprioceptive sensing, reflecting a remarkably broad yet coherent research vision. With over 230 total citations and an active publication record through 2025, Arleo represents an emerging voice shaping the future of intelligent, adaptive soft robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Variable Stiffness Linear Actuator Based on Differential Drive Fiber Jamming31 citations · 2023
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- 8Variable stiffness structure inspired by seashells3 citations · 2024
- 9SoftTex: Soft Robotic Arm With Learning-Based Textile Proprioception3 citations · 2025
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