Papers
14
Total Citations
160
H-Index
8
About
Olac Fuentes is a robotics and machine learning researcher whose work has made significant contributions to two interconnected domains: autonomous robot navigation and dextrous robotic manipulation. His research pioneered the application of predictive sparse distributed memory — a self-organizing neural network architecture — to enable mobile robots to autonomously acquire perception-based navigational behaviors, work that has accumulated over 50 citations across multiple publications. Equally influential is his sustained investigation into dextrous manipulation with multifingered robot hands, where he developed innovative approaches that circumvent the need for prior object models, instead deriving all necessary information directly from onboard sensors. His concept of "virtual tools" offered an elegant solution to the computational challenges inherent in controlling high-degree-of-freedom robotic systems, drawing inspiration from biological manipulators such as the human hand. Fuentes also applied evolutionary strategies to enable robots to autonomously learn complex manipulation primitives, using human hand heuristics to make the learning tractable. Spanning nearly a decade of foundational work from the mid-1990s into the early 2000s, his research laid important groundwork for modern autonomous robotics, particularly in bridging sensory perception with intelligent motor control.
Research Focus
Key Achievements
Top Papers
- 1
- 2Acquiring visual-motor models for precision manipulation with robot hands23 citations · 1996
- 3Learning Navigational Behaviors Using a Predictive Sparse Distributed Memory21 citations · 1996
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- 6Experiments on dextrous manipulation without prior object models12 citations · 2002
- 7The virtual tool approach to dextrous telemanipulation10 citations · 2002
- 8
- 9Learning dextrous manipulation skills using the evolution strategy7 citations · 2002
- 10