Subhash Pratap

Dr. Subhash Pratap is an Assistant Professor in the Department of Mechanical Engineering at Thapar Institute of Engineering & Technology (TIET), Patiala. His research lies at the intersection of Robotics, Artificial Intelligence, Soft Robotics, Wearable Sensing, and Human–Robot Interaction.

He completed his International Joint Ph.D. at IIT Guwahati, India, and Gifu University, Japan, where he explored how wearable sensing and AI can enable robots to understand human hand movements, grasping behaviour, and intent. His doctoral work combined multisensory data gloves, deep learning, soft robotic exoskeletons, and intelligent control to move from human sensing toward robotic assistance. Following his doctoral research, he served as a JST–India Young Invited Researcher (Early Postdoctoral Fellow) in Japan, supported by the Japan Science and Technology Agency (JST). He worked on “Imitation Learning of Grasping Motions for Robotic Automation in Production Environments,” further extending his research into robot learning and intelligent robotic automation.

His current research is expanding toward neuroadaptive and human-centred AI, combining EEG, EMG, wearable sensing, multimodal AI, and robotics to develop systems that can adapt to human state, intention, and behaviour. He is particularly interested in Neuro-AI, brain–machine and human–machine interfaces, neuroadaptive robotics, soft robotics, robotic rehabilitation, grasp intelligence, and embodied AI.

With a multidisciplinary approach spanning mechanical engineering, sensing, AI, neuroscience, and robotics, he is interested in research problems that bring together diverse disciplines to create the next generation of adaptive, intelligent, and human-centred machines.

Research Interests:
Neuroadaptive AI · EEG & EMG · Brain–Machine Interfaces · Artificial Intelligence · Soft Robotics · Wearable Sensing · Human–Robot Interaction · Grasp Intelligence · Robotic Rehabilitation · Multimodal Learning · Embodied AI

🚀 Vision and Future Work

My overarching goal is to develop intelligent, adaptive, and human-aware robotic systems that can sense, learn, and respond in real time—bridging the gap between human intent and robotic execution.

In future work, I aim to focus on:

Soft robotic hands and wearable exosuits for upper-limb rehabilitation, motor recovery, and assistive support in daily activities.
Multimodal sensing systems integrating vision, touch, haptics, and wearable biosignals (e.g., EEG/EMG) to enhance perceptual awareness and intent understanding.
Learning-based control strategies such as imitation learning, reinforcement learning, and foundation model integration (e.g., LLMs + Robotics) for flexible and scalable robotic behavior.
Visual-tactile intelligence for deformation-aware, contact-rich manipulation in both structured and unstructured environments.
EEG/EMG-based intent decoding to enable intuitive and direct brain/muscle-interfaced control of robotic assistive devices, particularly for individuals with severe motor deficits.
Synergy-based, co-adaptive control frameworks to personalize robotic assistance based on user performance, fatigue levels, and task complexity.

I am particularly interested in contributing to interdisciplinary teams focused on the next generation of robotic dexterity, assistive technologies, and autonomous systems for applications in neurorehabilitation, healthcare, and collaborative robotics in industrial settings.

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