Papers
6
Total Citations
74
H-Index
2
About
Domingo Esteban is a leading researcher in legged robotics and robot learning, whose work bridges the gap between perception, control, and autonomous adaptation. His research centers on vision-based locomotion planning, foothold adaptation, and the development of transferable control policies for high-degree-of-freedom robots. Esteban’s most influential contribution is the ViTAL framework (33 citations), which separates locomotion planning into foothold selection and pose adaptation, enabling legged robots to navigate complex terrain with unprecedented stability. He has also pioneered transfer learning methods that allow robots with similar kinematic structures to share latent skill representations, dramatically reducing the data required for learning manipulation tasks. His work on body velocity’s role in foothold adaptation via CNNs has refined dynamic locomotion strategies, while his anticipative postural control systems for humanoid robots have advanced balance during locomotion. Esteban has further explored hierarchical reinforcement learning to discover composable policies, and deep robot controllers that learn from both successful and failed executions. With over 70 citations across his key publications, Esteban’s research is shaping the future of autonomous, terrain-aware locomotion and efficient robot skill acquisition.
Research Focus
Key Achievements
Top Papers
- 1ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots33 citations · 2022
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- 4Anticipative humanoid postural control system for locomotive tasks2 citations · 2014
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