Karthik Gopalakrishnan
Papers
1
Total Citations
4
H-Index
1
About
Karthik Gopalakrishnan is a researcher advancing the frontier of multi-agent robotics and decision-making under uncertainty. His work focuses on developing algorithms for long-horizon planning in partially observable environments—a critical challenge for autonomous systems operating in the real world. His most-cited paper, "Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments" (2024), has already garnered 4 citations, signaling early impact in a rapidly evolving field. Gopalakrishnan’s contributions address the computational complexity of coordinating multiple robots when they have incomplete information, enabling more robust and scalable solutions for applications like search-and-rescue, autonomous exploration, and warehouse logistics. By integrating techniques from reinforcement learning, probabilistic inference, and multi-agent systems, he is helping to bridge the gap between theoretical planning frameworks and practical deployment. His work is particularly notable for tackling the curse of dimensionality inherent in long-horizon tasks, offering new pathways for robots to make coherent decisions over extended timeframes. As a rising voice in robotics and AI, Gopalakrishnan’s research promises to shape how teams of autonomous agents collaborate in uncertain, dynamic environments.
Research Focus
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Top Papers
- 1