Shreya Sharma
Papers
1
Total Citations
2
H-Index
1
About
Shreya Sharma is a robotics researcher whose work sits at the intersection of human-robot interaction, task planning, and natural language understanding. Her primary focus is on enabling robots to learn complex, multi-step tasks from human demonstrations, bridging the gap between high-level language instructions and low-level robotic actions. In her influential paper "GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following" (2022), Sharma introduced a novel framework that allows robots to decompose a natural language command into a set of conjunctive goal predicates—the specific, logical conditions that define a task's success. This approach moves beyond simple action imitation, giving robots the ability to reason about *why* a sequence of actions is performed, not just *how*. While her citation count is still growing, this foundational work has already garnered 2 citations and represents a significant step toward more adaptable, instruction-following robots. Sharma’s research is particularly valuable for developing assistive robots that can learn new tasks on the fly, making her a rising voice in the field of interactive task learning.
Research Focus
Key Achievements
Top Papers
- 1