Hamsa Balakrishnan
Papers
2
Total Citations
6
H-Index
2
About
Hamsa Balakrishnan is a researcher at the forefront of autonomous systems, multi-agent coordination, and safe reinforcement learning. Her work addresses some of the most pressing challenges in deploying intelligent robotic systems in complex, real-world environments. A central theme across her research is enabling robust decision-making under uncertainty — whether through long-horizon planning in partially observable multi-agent settings or by developing principled safety frameworks for collaborative robots. Her 2024 paper on long-horizon planning for multi-agent robots tackles the fundamental difficulty of coordinating multiple autonomous agents when information is incomplete, earning early recognition with 4 citations. Building on this foundation, her 2025 work on layered safety in multi-agent reinforcement learning directly confronts a critical barrier to real-world deployment: the inability of learning-based approaches to guarantee collision avoidance. By bridging traditional control-theoretic methods with modern MARL frameworks, she offers a compelling path toward safe, scalable multi-robot navigation. Balakrishnan's contributions are particularly valuable for researchers and engineers working at the intersection of robotics, control theory, and machine learning, where safety and autonomy must coexist without compromise.
Research Focus
Key Achievements
Top Papers
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