Daniele Di Vito
Papers
12
Total Citations
204
H-Index
6
About
Daniele Di Vito’s research bridges assistive robotics, brain-computer interfaces (BCIs), and intelligent motion planning for redundant manipulators. His major contributions include developing control architectures that allow users with severe motion disabilities to operate robotic arms via P300-based BCIs, enabling daily-life tasks like drinking or object manipulation. His work on damped least squares algorithms for inverse kinematics critically evaluates handling of kinematic singularities and joint velocity limits, earning 48 citations. Di Vito also advanced task-priority frameworks by merging global and local planners for real-time replanning, addressing local minima issues in redundant robots. His dual-arm mobile robot system for assistive tasks, controlled via BCIs, further demonstrates practical impact. With over 200 total citations across his top papers, Di Vito’s research is notable for its focus on real-world applicability, from soil sampling robots (ROBILAUT project) to tree-based Q-learning for object relocation in clutter. His work consistently emphasizes user-centered design and robust control, making him a key figure in assistive and autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 2Assistive robot operated via P300-based brain computer interface47 citations · 2017
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