Papers
6
Total Citations
149
H-Index
3
About
Sarthak Bhagat is a robotics researcher whose work spans intelligent soft robotics, deep reinforcement learning, and human-robot interaction. He is perhaps best known for his highly influential 2019 review, "Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges," which has accumulated over 124 citations and stands as a foundational reference bridging embodied intelligence with the emerging field of soft robotics. This work synthesized the convergence of deep reinforcement learning and compliant robotic structures at a pivotal moment for the field, helping to define research directions that continue to resonate today. More recently, Bhagat has expanded his focus toward real-world robotic autonomy, contributing innovative work on zero-shot task-oriented grasping through geometric decomposition using large language models, as well as neuro-symbolic approaches to short-context action anticipation for assistive in-home robots. These efforts reflect a forward-looking research agenda aimed at enabling robots to operate intelligently in unstructured, everyday environments. With a total citation footprint exceeding 145 citations, Bhagat's trajectory demonstrates a meaningful evolution from foundational reviews to cutting-edge applied research, making his profile particularly relevant for students exploring the intersection of machine learning, soft robotics, and human-centered AI.
Research Focus
Key Achievements
Top Papers
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- 5<i>Let Me Help You!</i> Neuro-Symbolic Short-Context Action Anticipation2 citations · 2024
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