Guilherme Baretto
Papers
1
Total Citations
3
H-Index
1
About
Guilherme Baretto’s research lies at the intersection of robotics, computer vision, and motor control, with a focus on enabling machines to learn coordinated movements directly from visual input. His most cited work, "Learning visuo-motor coordination for pointing without depth calculation" (2012, 3 citations), introduces a paradigm-shifting approach: rather than relying on complex 3D reconstructions or depth calculations, Baretto demonstrates that pointing can be achieved through a holistic, direct mapping from an object’s pixel coordinates in the visual field to the joint angles defining a robot’s pose. This elegantly simple yet powerful framework bypasses traditional geometric computations, offering a more efficient and biologically plausible method for visuo-motor coordination. By showing that an agent can learn to orient its hand, arm, head, or body toward a target using only 2D visual information, Baretto’s work has implications for adaptive robotics, human-robot interaction, and our understanding of sensorimotor learning. Though his citation count is modest, his contribution challenges conventional assumptions about the necessity of depth perception for action, paving the way for more agile, computationally frugal robotic systems that learn from raw sensory streams.
Research Focus
Key Achievements
Top Papers
- 1Learning visuo-motor coordination for pointing without depth calculation3 citations · 2012