Carina Liebers
Papers
1
Total Citations
7
H-Index
1
About
Carina Liebers is a researcher at the forefront of human-robot interaction and simulation-based training, with a particular focus on ensuring that humans remain integral to autonomous systems. Her most-cited work, "Keep the Human in the Loop: Arguments for Human Assistance in the Synthesis of Simulation Data for Robot Training" (2024), challenges the prevailing reliance on fully automated domain randomization in reinforcement learning. Liebers argues persuasively for strategic human involvement in generating training environments, enhancing both the robustness and real-world transferability of robotic systems. This paper has already garnered 7 citations, signaling its growing influence in the robotics and AI communities. Her research bridges the gap between simulation and reality, advocating for a balanced partnership between human intuition and machine learning. Liebers’ contributions are particularly notable for their practical implications—improving safety and adaptability in autonomous robots deployed in unpredictable settings. By championing a "human-in-the-loop" approach, she is shaping a more responsible, effective future for robot training methodologies. Her work is essential reading for anyone interested in the ethical and technical dimensions of embodied AI.
Research Focus
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Top Papers
- 1