Papers
14
Total Citations
157
H-Index
7
About
Claudio Zito is a robotics researcher whose work spans robot manipulation, tactile sensing, dexterous grasping, and human-robot interaction. He has made significant contributions to the challenge of robotic manipulation under uncertainty — a fundamental problem in autonomous robotics. His early work introduced a two-level RRT planning algorithm for robotic push manipulation (50 citations), establishing an elegant framework for sequencing pushes to achieve desired object poses. Building on this foundation, Zito pioneered tactile-informed planning strategies, demonstrating how sensory feedback during failed grasp attempts can refine object-pose estimates and enable intelligent trajectory re-planning (20 citations). His GPAtlasRRT planner (22 citations) extended this vision to local tactile exploration, allowing robots to reconstruct unknown object shapes through guided fingertip contact. More recently, Zito has broadened his research into physical human-robot interaction, developing frameworks for movement intention prediction that outperform manual control (9 citations) and adaptive impedance control for upper-limb prostheses (8 citations). His 2023 review of tactile sensing, grasping, and social robotics reflects a growing interest in biologically inspired "living machines." Across more than a decade of research, Zito has consistently pushed the boundaries of intelligent, sensor-driven robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Two-level RRT planning for robotic push manipulation50 citations · 2012
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- 4Hypothesis-based Belief Planning for Dexterous Grasping13 citations · 2019
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- 8Exploratory reach-to-grasp trajectories for uncertain object poses6 citations · 2012
- 9Multisensory Learning Framework for Robot Drumming4 citations · 2019
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