Papers
14
Total Citations
132
H-Index
7
About
D. Gonzalez-Aguirre is a leading researcher in humanoid robotics, focusing on enabling robots to perceive, interact with, and navigate complex, unstructured environments. Their work spans whole-body affordance extraction, visual object categorization, and force-visual feedback integration, with a strong emphasis on model-based perception and self-localization. A key contribution is the development of frameworks that allow humanoid robots to robustly perform physical tasks—such as opening doors in real kitchens—by combining stereo vision and force feedback, as demonstrated in their 2009 paper (21 citations). Their research on extracting whole-body affordances from multimodal exploration (2014, 26 citations) has been pivotal for robots operating in cluttered disaster scenarios. Gonzalez-Aguirre has also advanced real-time 6D active visual localization using particle filtering (2014, 11 citations) and shape-based object categorization (2011, 21 citations), enabling robots to handle novel objects. More recently, they have explored robot-based LIDAR mapping for metaverse applications (2023, 7 citations) and intuitive human-robot collaboration via Bayesian inference (2022, 5 citations). With over 100 total citations, their work bridges perception, control, and interaction, making significant strides toward autonomous humanoid robots in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Extracting whole-body affordances from multimodal exploration26 citations · 2014
- 2Towards shape-based visual object categorization for humanoid robots21 citations · 2011
- 3
- 4Robust real-time 6D active visual localization for humanoid robots11 citations · 2014
- 5Model-based visual self-localization using geometry and graphs10 citations · 2008
- 6
- 7On Environmental Model-Based Visual Perception for Humanoids7 citations · 2009
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- 9
- 10Model-Based Visual Self-localization Using Gaussian Spheres4 citations · 2010