Papers
3
Total Citations
6
H-Index
2
About
Daniel Soto-Guerrero is a robotics researcher whose work lies at the intersection of bio-inspired control, multi-agent systems, and legged locomotion. His key contributions include the development of the Self-Organized Body-Schema (SO-BoS) system, a novel architecture that enables rotorcrafts to learn their configuration space through sensory-motor mapping, drawing inspiration from the posterior parietal cortex. This feedforward formation control approach, published in 2021, demonstrates how self-organization can replace traditional centralized control in multi-robot systems. Soto-Guerrero is also the creator of the K3P algorithm, a walking gait generator designed to simplify the complex task of generating stable locomotion patterns for legged robots. By offering a computationally efficient alternative to model-based and mathematical methods, K3P has become a reference point for researchers seeking bioinspired solutions in legged robotics. His 2017 work on autonomous airborne robotic agents further underscores his commitment to advancing fully independent robotic systems. Though his citation counts are currently modest, Soto-Guerrero’s foundational algorithms and biologically grounded architectures represent a promising direction for scalable, adaptive robotics. His research is particularly relevant for students and engineers exploring decentralized control, embodied cognition, and the intersection of neuroscience and robotics.
Research Focus
Key Achievements
Top Papers
- 1Feedforward Formation Control based on Self-Organized Body-Schema2 citations · 2021
- 2Towards an Autonomous Airborne Robotic Agent2 citations · 2017
- 3K3P: A walking gait generator algorithm2 citations · 2017