Jigyasa Gupta
Papers
1
Total Citations
2
H-Index
1
About
Jigyasa Gupta is a researcher advancing the frontier of human-robot collaboration, with a focus on enabling robots to understand and execute natural language instructions through learning from demonstration. Her key research areas lie at the intersection of task planning, goal inference, and interactive robot learning. In her notable work, "GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following" (2022), Gupta tackles the challenge of teaching robots to sequence actions by first inferring the specific, conjunctive goal predicates that define a task's success. This contribution is critical for moving robots beyond scripted behaviors toward flexible, instruction-following agents that can generalize from human demonstrations. While her work is early in its citation impact, its conceptual foundation—breaking down high-level task planning into goal inference and action sequencing—positions it as a building block for future research in interactive AI and robotics. Gupta’s research is particularly valuable for students and engineers interested in the practical integration of natural language processing, planning, and human-robot interaction, offering a principled approach to making robots more intuitive and capable partners in shared tasks.
Research Focus
Key Achievements
Top Papers
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