Papers
29
Total Citations
476
H-Index
14
About
Marco Faroni is a robotics researcher whose work sits at the intersection of motion planning, human-robot collaboration, and optimization-based control. His research spans redundant manipulator kinematics, model predictive control (MPC), and task-and-motion planning, with a consistent focus on making robots safer, faster, and more adaptable in dynamic environments shared with humans. Among his most influential contributions is a predictive inverse kinematics framework for redundant manipulators that enforces kinematic constraints while preserving geometric task integrity through task-scaling — a method that has garnered 75 citations and become a reference in real-time robot control. His MPC-based framework for human-robot collaborative planning further extends this work by enabling robots to dynamically adjust trajectories in response to human behavior, balancing efficiency and safety in shared workspaces. Faroni has also made notable strides in uncertainty-aware planning, proposing time-optimal motion strategies that account for uncertain human state estimates, and in multiagent task-and-motion planning for dynamic environments. His work on anytime replanning strategies addresses the practical challenge of rapidly changing surroundings without halting robot operation. With over 300 cumulative citations and contributions spanning manufacturing, Industry 4.0, and collaborative robotics, Faroni's research is shaping how intelligent robots operate safely and efficiently alongside humans.
Research Focus
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Top Papers
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- 9Anytime Informed Multi-Path Replanning Strategy for Complex Environments22 citations · 2023
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