Bharat Singh
Papers
12
Total Citations
487
H-Index
5
About
Bharat Singh is a robotics and machine learning researcher whose work spans reinforcement learning, human gait modeling, biped robotics, and robotic manipulator control. He is perhaps best known for his highly influential 2021 comprehensive survey on reinforcement learning in robotic applications, which has garnered an impressive 349 citations and established him as a leading voice in the intersection of AI and robotics. His research demonstrates a strong focus on human-robot collaboration, particularly through the development of data-driven models that predict joint kinematics for prosthetic limbs, biped robots, and human locomotion systems — work that has collectively attracted over 100 additional citations across multiple studies. Singh has made notable contributions to surface electromyography (sEMG)-based activity recognition, kinematic modeling using machine learning, and inverse kinematics solutions for robotic manipulators using both meta-heuristic and deep learning approaches. His probabilistic modeling frameworks, including Hamiltonian Monte Carlo-based sensor modeling, reflect a sophisticated command of statistical methods applied to real-world robotic challenges. Across his body of work, Singh bridges fundamental biomechanics with advanced computational techniques, making his research particularly valuable for engineers and scientists developing next-generation assistive technologies, autonomous robots, and intelligent prosthetic systems.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning in robotic applications: a comprehensive survey349 citations · 2021
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- 6Mapping Model for Genesis of Joint Trajectory using Human Gait Dataset5 citations · 2021
- 7Classical Approaches for Mobile Robot Path Planning: A Review5 citations · 2022
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