Mohit Jain
Papers
2
Total Citations
72
H-Index
2
About
Mohit Jain is a leading researcher at the intersection of robotics, computer vision, and reinforcement learning (RL), with a primary focus on enabling robots to perform precise, real-world manipulation tasks using visual feedback. His work tackles the fundamental challenge of bridging the gap between high-dimensional visual inputs and fine-grained motor control. Jain’s major contributions include pioneering methods that leverage self-supervised 3D representations for visual RL, demonstrating how learning internal state representations can dramatically improve sample efficiency and generalization—a critical step for deploying robots outside controlled labs. His highly cited 2022 paper, “Look Closer,” introduces a novel transformer-based architecture that integrates egocentric and third-person views, achieving 47 citations by solving precision manipulation tasks with reduced engineering overhead. In his 2023 follow-up work (25 citations), he further advanced the field by showing how 3D-aware self-supervision can make RL agents more robust to real-world variability. Jain’s research is notable for its practical impact: by reducing the need for hand-engineered perception pipelines, his methods are paving the way for more adaptable, visually-guided robotic systems in manufacturing, healthcare, and home assistance.
Research Focus
Key Achievements
Top Papers
- 1
- 2Visual Reinforcement Learning With Self-Supervised 3D Representations25 citations · 2023