Lakshmi Seelam
Papers
1
Total Citations
7
H-Index
1
About
Lakshmi Seelam’s research lies at the intersection of human-robot interaction and machine learning, with a focus on enabling non-expert users to teach robots complex, long-horizon tasks. Her key contributions center on Learning from Demonstration (LfD), particularly investigating how a user’s prior experience influences their ability to perform hierarchical abstraction—a critical skill for decomposing tasks into manageable subtasks. Her most-cited work, “Investigating the Impact of Experience on a User’s Ability to Perform Hierarchical Abstraction” (2023), has already garnered 7 citations, signaling growing interest in making robot teaching more intuitive. Seelam addresses a persistent challenge: while LfD allows end-users to shape robot behavior without programming expertise, effectively leveraging hierarchical structure for multi-step tasks remains unsolved. Her research provides empirical insights into how users naturally structure demonstrations, paving the way for more accessible robot programming. By bridging cognitive science and robotics, Seelam’s work holds promise for democratizing robot training, empowering everyone from factory workers to home users to teach robots complex behaviors. Her findings are foundational for developing adaptive interfaces that match users’ mental models, reducing the gap between human intuition and machine learning.
Research Focus
Key Achievements
Top Papers
- 1