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About

Ayesha Khan is a robotics researcher whose work lies at the intersection of bio-inspired algorithms, motion planning, and human-robot interaction. Her research is distinguished by its creative use of biological models—particularly fish locomotion—to solve fundamental challenges in autonomous systems. Her most impactful work, “Using recurrent neural networks (RNNs) as planners for bio-inspired robotic motion” (11 citations), pioneered the use of Long Short-Term Memory networks as motion planners, demonstrating how deep learning can generate smooth, adaptive trajectories from simulated fish data. This approach offers a powerful alternative to traditional path-planning methods. Khan further advanced bio-inspired robotics with her work on localization-free stochastic coverage (4 citations), where she adapted the Persistent Turning Walker model to enable reliable area exploration without external positioning systems—a critical capability for underwater or GPS-denied environments. She has also contributed to human-robot interaction, rigorously analyzing the trade-off between adaptiveness and consistency in expert-based learning algorithms. Through these contributions, Khan is building a reputation for developing robust, nature-inspired solutions that push the boundaries of autonomous navigation and collaborative robotics.