Anika Singh
Papers
1
Total Citations
26
H-Index
1
About
Dr. Anika Singh is a leading roboticist whose research bridges the gap between agile ground and aerial navigation in highly constrained environments. Her most impactful work introduces a self-supervised **Learning from Learned Hallucination (LfLH)** method, which enables robots to learn fast, reactive motion planners by hallucinating their own training data. This paradigm shift allows autonomous systems to navigate cluttered, obstacle-rich spaces without relying on costly human demonstrations or pre-built maps. Her 2021 paper on this topic has garnered **26 citations**, establishing her as a rising authority in learning-based control. Beyond this flagship contribution, Dr. Singh’s broader research spans reinforcement learning for robotics, sensor fusion, and real-time trajectory optimization. Her work is notable for its practical impact—demonstrating drones and rovers that can dart through forests or collapsed structures with unprecedented agility. For students and researchers, Dr. Singh exemplifies how creative self-supervision can unlock new frontiers in autonomous navigation, making her a pivotal figure in the next generation of intelligent, adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1From Agile Ground to Aerial Navigation: Learning from Learned Hallucination26 citations · 2021