Carlos Cárdenas
Papers
1
Total Citations
3
H-Index
1
About
Carlos Cárdenas is a roboticist focused on advancing autonomous human-robot interaction through imitation learning. His key research areas include whole-body control, multimodal perception, and end-to-end learning for collaborative robotics. His most notable contribution, the XBG (eXteroceptive Behaviour Generation) system, introduces a pioneering architecture that enables humanoid robots to learn complex interaction behaviors directly from demonstration data. This work, published in 2024, has already garnered 3 citations, signaling early impact in the field. By integrating vision, proprioception, and tactile feedback, Cárdenas’s approach allows robots to perform natural, context-aware actions in real-world collaboration scenarios—a significant step toward seamless human-robot teamwork. His research addresses critical challenges in safety and adaptability, with potential applications in manufacturing, healthcare, and service robotics. As a rising scholar, Cárdenas is helping to shape the next generation of socially intelligent machines, bridging the gap between simulated learning and physical deployment.
Research Focus
Key Achievements
Top Papers
- 1