Smruti Amarjyoti
Papers
2
Total Citations
71
H-Index
2
About
Smruti Amarjyoti is a researcher working at the intersection of robotics, machine learning, and autonomous systems, with a particular focus on applying deep reinforcement learning to real-world robotic challenges. Their most influential work, "Deep Reinforcement Learning for Robotic Manipulation—The State of the Art" (2017), has garnered 57 citations and stands as a comprehensive survey of the evolving landscape of reinforcement learning algorithms applied to robotic manipulation tasks. The paper critically examines how the field has shifted away from specialized policy representations and human demonstrations toward more flexible, data-driven approaches, making it a valuable reference for researchers navigating this rapidly advancing domain. Amarjyoti's earlier work on visual servoing control of Baxter robot arms (2015), which has accumulated 14 citations, demonstrates a strong foundation in practical robotics, addressing the nuanced challenge of obstacle avoidance through kinematic redundancy. Together, these contributions reflect a research trajectory that bridges theoretical algorithmic development with hands-on robotic implementation, offering meaningful insights to both newcomers and experienced practitioners in the field of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Deep Reinforcement Learning for Robotic Manipulation-The state of the art57 citations · 2017
- 2