Shikha Surana

About

Shikha Surana is a rising researcher in robotics and machine learning, whose work focuses on the intersection of data-driven control and efficient skill acquisition. Her primary research areas include locomotion control, reinforcement learning, and the development of reusable priors for robotic systems. Surana’s major contribution lies in pioneering methods that enable robots to learn complex locomotion skills more efficiently by leveraging diverse environmental trajectory generator priors. Her most-cited paper, "Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors" (2023), demonstrates how incorporating structured locomotion priors—such as trajectory generators—can dramatically reduce the sample complexity of learning robust controllers for unstructured terrains. This work has already garnered attention in the field, with 3 citations in a short time, signaling its potential impact. By bridging the gap between classical control and modern learning, Surana is advancing the practicality of autonomous robots in real-world environments. Her research holds promise for applications in search-and-rescue, exploration, and assistive robotics, making her a notable emerging voice in the robotics community.

Research Focus

Key Achievements

Top Papers

1. 1
   Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors

   3 citations · 2023

Key Collaborators

Contact & Links