Manuel Bied
Papers
3
Total Citations
5
H-Index
1
About
Manuel Bied is a researcher at the intersection of human-robot interaction and intelligent transportation systems, with key contributions in sensorimotor learning and social robotics for traffic safety. His most cited work, "Exploring the Difference between Solving and Teaching in Sensorimotor Tasks" (2020, 3 citations), introduces a novel perspective on Learning from Demonstration (LfD) by distinguishing between a human's task-solving behavior and their active teaching role when instructing robots—a critical insight for improving robot learning algorithms. Bied has also advanced the use of social robots for traffic orchestration, as seen in his 2025 poster study on public opinion toward such robots (1 citation) and his visionary proposal for multi-modal crash prediction using V2X and visual information (1 citation). By integrating social robots into Vehicle-to-Everything (V2X) communication frameworks, he addresses the challenge of protecting vulnerable road users (VRUs) in increasingly automated traffic environments. His work bridges cognitive robotics and real-world safety applications, offering practical pathways for robots to mediate human-vehicle interactions. With a growing portfolio that tackles both fundamental learning dynamics and applied traffic solutions, Bied is shaping how robots can learn from and collaborate with humans in complex, safety-critical settings.
Research Focus
Key Achievements
Top Papers
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