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About

Pratibha Tokas is a researcher whose work bridges the frontiers of robotics, bio-signal processing, and swarm intelligence. Her most impactful contribution, a 2024 study on deep ensemble learning for lower limb movement recognition from multichannel sEMG signals, has already garnered 29 citations, demonstrating its immediate relevance to assistive technology and human-robot interaction. This work showcases her ability to apply advanced machine learning to decode complex physiological data for practical applications. In the realm of bipedal robotics, Tokas has made significant strides in dynamic stability, notably through her 2023 paper on foot trajectory planning using polynomial trajectories for a 10-DOF robot on inclined terrain. This research, which has earned 11 citations, addresses a core challenge in legged locomotion by designing efficient and stable swing-phase paths. Earlier in her career, she contributed foundational algorithms for swarm robotics, developing a distributed assembly method for asynchronous robots with limited visibility in the presence of line obstacles. By synchronizing robots using visible bits (a three-color light system), her 2017 work provided a novel solution to a classic coordination problem, laying groundwork for resilient multi-robot systems. Tokas’s diverse portfolio reflects a deep commitment to solving real-world problems in autonomous systems, from rehabilitation to collective robotics.