Diego Bianchi
Papers
6
Total Citations
78
H-Index
5
About
Diego Bianchi is at the forefront of soft robotics, pioneering learning-based control strategies that enable soft manipulators to perform dynamic, high-speed tasks. His research centers on the intersection of soft material mechanics and deep reinforcement learning, addressing the fundamental challenge of controlling highly nonlinear, compliant systems. Bianchi’s major contributions include developing the first soft robot control system capable of throwing life-size objects, demonstrated in his work on SofToss (20 citations), and extending this to circular run-ups with SoftSling (10 citations). He has also advanced adaptive control through error-driven schemes like the Online Regressing Network (13 citations) and explored the impact of modeling on continuum soft manipulators (13 citations). His closed-loop deep reinforcement learning approach for pushing tasks (20 citations) further showcases his ability to solve complex interaction problems. With over 78 total citations across his most-cited works, Bianchi’s innovative controllers are pushing soft robotics beyond simple grasping into agile, dynamic manipulation—a leap that promises transformative applications in safe human-robot interaction and unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Pushing with Soft Robotic Arms via Deep Reinforcement Learning20 citations · 2024
- 2SofToss: Learning to Throw Objects With a Soft Robot20 citations · 2023
- 3Policy Adaptation using an Online Regressing Network in a Soft Robotic Arm13 citations · 2023
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