Nathaniel Glaser
Papers
2
Total Citations
23
H-Index
2
About
Nathaniel Glaser’s research bridges the frontiers of multi-agent robotic perception and humanoid locomotion, with a focus on overcoming real-world constraints like limited bandwidth and physical instability. His most cited work, “Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking” (2021, 21 citations), tackles the challenge of collaborative perception in robotic swarms, where agents must process and exchange unregistered imagery despite obstructions and communication bottlenecks. This contribution advances semantic segmentation in distributed systems, enabling more resilient swarm intelligence. In earlier work, Glaser developed a novel method for online center-of-mass estimation in a wheeled inverted pendulum humanoid robot (2018, 2 citations), combining robust control with adaptive learning to maintain balance despite model inaccuracies. This approach condenses mass model errors into a single CoM error term, allowing for real-time correction—a key step toward practical, dynamically stable humanoid robots. While still early in his career, Glaser’s work demonstrates a clear trajectory toward solving fundamental problems in embodied AI, from swarm coordination to bipedal balance, with potential applications in search-and-rescue, autonomous exploration, and assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 2