Papers
3
Total Citations
32
H-Index
3
About
Akash Garg’s research lies at the intersection of robotic manipulation, autonomous navigation, and intelligent automation. His most impactful contribution is a model predictive non-sliding manipulation (MPNSM) control framework for non-prehensile object transportation—enabling robots to safely carry objects on tray-like end-effectors without grasping, a challenging task in dynamic environments. This work, published in 2023, has already garnered 26 citations, reflecting its timely relevance to dexterous robotics. Garg has also advanced probabilistic navigation, proposing an overlap-of-Gaussians approach for obstacle avoidance that accounts for state uncertainty—a significant step beyond deterministic methods, earning 3 citations for its theoretical novelty. Beyond physical robotics, he explores hyper-automation in auditing, leveraging robotic process automation (RPA) to streamline control monitoring, a contribution cited 3 times in the context of digital transformation. Garg’s work is notable for bridging rigorous control theory with practical, uncertainty-aware systems, positioning him as an emerging voice in both robotic manipulation and intelligent process automation.
Research Focus
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Top Papers
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