Daniele De Simone
Papers
6
Total Citations
108
H-Index
6
About
Daniele De Simone is a robotics researcher whose work bridges multi-robot coordination and humanoid locomotion. His key research areas include decentralized consensus dynamics, model predictive control (MPC) for bipedal walking, and safe human-robot interaction. De Simone made a major contribution to the study of consensus in multi-robot networks, demonstrating how the naming game model—traditionally used in semiotics—can be empirically validated for decentralized multiagent systems, a paper that has garnered 45 citations. In humanoid robotics, he advanced gait generation by developing single-stage MPC frameworks that allow humanoid robots to walk to arbitrary Cartesian goals in real-time, overcoming the limitations of simplified models like the Linear Inverted Pendulum. His work on closed-loop MPC integrated with dense visual SLAM achieved stability through reactive stepping, earning 19 citations. Notably, De Simone developed a behavior-based safety framework for deploying humanoids in human environments, classifying safety behaviors into override, temporary override, and proactive categories. He also pioneered real-time planning for evasive motions and pursuit-evasion scenarios between humanoids, enabling robots to react dynamically to moving obstacles. With over 100 total citations, De Simone’s research is foundational for creating responsive, safe, and coordinated robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Closed-loop MPC with Dense Visual SLAM - Stability through Reactive Stepping19 citations · 2019
- 3Humanoid gait generation for walk-to locomotion using single-stage MPC12 citations · 2017
- 4A behavior-based framework for safe deployment of humanoid robots12 citations · 2021
- 5Real-time planning and execution of evasive motions for a humanoid robot10 citations · 2016
- 6Real-time pursuit-evasion with humanoid robots10 citations · 2017