Anusha Srikanthan
California University of Pennsylvania, University of Pennsylvania
Papers
2
Total Citations
5
H-Index
2
About
Anusha Srikanthan is a rising roboticist whose work tackles two fundamental challenges in multi-robot coordination and control: task allocation under uncertainty and trajectory generation for complex, underactuated systems. Her research centers on developing data-driven and optimization-based methods that enable robots to operate autonomously in real-world environments where models are imperfect and rewards are unknown. In her highly-cited 2023 paper on concurrent constrained optimization for multi-robot task allocation, Srikanthan introduced a novel framework that allows robot teams to discover and allocate tasks without explicit user-defined reward functions—a critical step toward scalable, autonomous coordination. Her second major contribution, also from 2023, presents a data-driven approach to synthesizing dynamics-aware trajectories for underactuated systems, bridging the gap between simplified planning models and the complex dynamics of actual robots. By integrating trajectory generation with tracking control, her work improves the reliability of systems like quadrotors and walking robots. With her papers already garnering early citations in the competitive field of robotics, Srikanthan is establishing herself as a key voice in the next generation of autonomous multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2