Papers
16
Total Citations
166
H-Index
7
About
Davide Tateo is a robotics researcher whose work sits at the intersection of machine learning, motion planning, and control theory, with a particular focus on enabling robots to operate safely and efficiently in dynamic, real-world environments. His most influential contributions include the development of Regularized Deep Signed Distance Fields for real-time reactive motion generation (32 citations), and novel approaches to learning stable vector fields on Lie groups (20 citations), advancing how robots can fluidly adapt their motion in operational space. Tateo has made significant strides in safe reinforcement learning for high-dimensional robotic tasks (17 citations) and kinodynamic planning on constraint manifolds using deep neural networks (18 citations), addressing the critical challenge of fast, constraint-aware robot control. His work on learning-based design for parallel-elastic quadrupedal robots (31 citations) demonstrates a compelling blend of hardware co-design and data-driven control. Beyond individual platforms, he has pushed toward generalizable locomotion policies across multiple robot morphologies and pioneered frameworks integrating large language models with embodied AI. Collectively, his research advances the autonomy, adaptability, and safety of next-generation robotic systems across manipulation, locomotion, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Regularized Deep Signed Distance Fields for Reactive Motion Generation32 citations · 2022
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- 3Learning Stable Vector Fields on Lie Groups20 citations · 2022
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- 6Efficient and Reactive Planning for High Speed Robot Air Hockey14 citations · 2021
- 7ROS-LLM: A Framework for Embodied AI7 citations · 2025
- 8Robot Reinforcement Learning on the Constraint Manifold6 citations · 2021
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