Papers
15
Total Citations
376
H-Index
10
About
Alessandro Palleschi is a robotics researcher whose work spans trajectory planning, human-robot collaboration, manipulation, and multi-robot coordination — areas that sit at the critical intersection of safety, efficiency, and autonomy in modern robotic systems. His most-cited contribution, "Fast and Safe Trajectory Planning" (2021, 76 citations), directly addresses the fundamental performance-safety trade-off in collaborative robotics, offering practical solutions for robots operating alongside humans in industrial environments. Complementing this, his time-optimal path tracking and trajectory planning methods — developed for both jerk-controlled and flexible joint robots — have advanced the field's ability to generate smooth, efficient motion profiles under realistic physical constraints. Palleschi has also made notable strides in robotic manipulation, with his two-part "Grasp It Like a Pro" series demonstrating how human expertise and data-driven decomposition can enable robots to handle unknown objects with remarkable reliability. His work on whole-body control of unstable wheeled humanoid robots and iterative learning control for compliant arms further demonstrates his versatility across robotic platforms. Rounding out his portfolio, research into multi-robot coordination and intralogistics applications underscores his commitment to deploying robust robotic solutions in real-world warehouse and factory settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2Dynamic Whole-Body Control of Unstable Wheeled Humanoid Robots51 citations · 2019
- 3
- 4Iterative Learning Control for Compliant Underactuated Arms39 citations · 2023
- 5Time-Optimal Path Tracking for Jerk Controlled Robots35 citations · 2019
- 6Time-Optimal Trajectory Planning for Flexible Joint Robots29 citations · 2020
- 7
- 8WRAPP-up: A Dual-Arm Robot for Intralogistics19 citations · 2020
- 9
- 10