Praphpreet Dhir
Papers
1
Total Citations
4
H-Index
1
About
Praphpreet Dhir is a robotics researcher whose work lies at the intersection of multi-agent systems, manipulation planning, and artificial intelligence. Her primary research focuses on developing intelligent algorithms for object rearrangement in cluttered, real-world environments—a critical challenge for applications ranging from warehouse logistics to domestic robotics. Dhir’s most notable contribution is the MANER framework (Multi-Agent Neural Rearrangement Planning), which pioneers a neural approach to coordinating multiple robots for efficient, collision-free object reconfiguration. This work addresses a significant gap in the field, where prior research predominantly focused on single-agent solutions, by enabling teams of robots to collaboratively tackle complex spatial tasks. Her research has already garnered attention, with her 2023 paper receiving citations that underscore its growing influence in the robotics community. By advancing multi-agent coordination and neural planning, Dhir is helping to bridge the gap between theoretical robotics and practical, scalable automation—making her a promising voice in the next generation of intelligent robotic systems.
Research Focus
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Top Papers
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