Anuj Shrivatsav Srikanth
Papers
1
Total Citations
6
H-Index
1
About
Anuj Shrivatsav Srikanth is a rising researcher at the forefront of robotic manipulation, whose work bridges the critical gap between visual and force-based feedback in policy learning. His primary research areas include robot learning, force-sensitive manipulation, and symmetric neural architectures for control. In his landmark 2024 paper, "Symmetric Models for Visual Force Policy Learning," Srikanth challenges the conventional reliance on visual feedback alone, demonstrating that integrating force feedback through symmetric neural models dramatically improves both sample efficiency and task performance in robotic manipulation. This work has already garnered 6 citations, signaling its growing influence in the robotics community. By showing that force information—long acknowledged as beneficial but rarely leveraged in policy learning—can be systematically incorporated, Srikanth is paving the way for more dexterous, physically-aware robots. His contributions are particularly notable for their potential to enable robots to handle delicate or complex tasks that require nuanced tactile understanding. As a young scholar, Srikanth is establishing himself as a key voice in the next generation of robot learning researchers, with his work promising to reshape how robots interact with the physical world.
Research Focus
Key Achievements
Top Papers
- 1Symmetric Models for Visual Force Policy Learning6 citations · 2024