Daniele Meli
Papers
15
Total Citations
287
H-Index
9
About
Daniele Meli is a robotics researcher whose work bridges autonomous task planning, knowledge representation, and robot motion control, with particular emphasis on surgical robotics and human-robot interaction. He has made significant contributions to two interconnected research threads: the development of Dynamic Movement Primitives (DMPs) for intelligent robot trajectory learning and obstacle avoidance, and the application of logic-based frameworks to autonomous surgical task planning. His DMP work introduced volumetric obstacle avoidance using superquadric potential functions, later extended to incorporate dynamic environments — earning 76 and 48 citations respectively, establishing him as a key voice in learned motion generation. Parallel to this, Meli has pioneered knowledge-based and logic programming approaches to surgical autonomy, enabling robots to reason over complex procedural tasks with interpretable, safety-conscious decision-making. His 2020 paper on autonomous task planning in robotic surgery (46 citations) exemplifies this vision, while subsequent work on inductive learning of Answer Set Programs and natural language-to-temporal-logic mapping demonstrates a drive toward truly adaptive surgical systems. Collectively, his research addresses one of robotics' most consequential frontiers: making autonomous robots not merely capable, but reliably intelligent in high-stakes clinical environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Dynamic Movement Primitives: Volumetric Obstacle Avoidance48 citations · 2019
- 3Autonomous task planning and situation awareness in robotic surgery46 citations · 2020
- 4A knowledge-based framework for task automation in surgery20 citations · 2019
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- 8Logic programming for deliberative robotic task planning13 citations · 2023
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- 10