Kim Kleiven
Papers
3
Total Citations
19
H-Index
2
About
Kim Kleiven is a robotics researcher pushing the boundaries of real-world deep reinforcement learning (RL) and multi-robot coordination. Their most impactful work demonstrates how deep RL can be scaled from simulation to practical deployment, as shown in their highly cited 2023 study on waste sorting in office buildings using a fleet of mobile manipulators. This system, which has garnered 15 citations, addresses the critical challenge of bootstrapping RL policies for real-world tasks—moving beyond theoretical algorithms to tackle the messy, unpredictable conditions of everyday environments. Kleiven’s contributions also extend to human-robot interaction and multi-robot systems, exemplified by their innovative 2025 work on interactive multi-robot flocking, where gesture responsiveness and musical accompaniment transform traditional efficiency-driven tasks into engaging, collaborative experiences. By integrating deep RL with large-scale, practical applications, Kleiven is helping to bridge the gap between laboratory research and commercial viability, making robots more autonomous and adaptable in shared human spaces. Their work stands out for its focus on real-world impact, from office recycling to expressive multi-robot performances, marking Kleiven as a key figure in the next wave of embodied AI.
Research Focus
Key Achievements
Top Papers
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