Anshul Tomar
Papers
1
Total Citations
4
H-Index
1
About
Anshul Tomar is a leading researcher at the frontier of embodied AI, specializing in sim-to-real transfer for audio-visual navigation. His work addresses the critical challenge of bridging the gap between simulated training environments and real-world deployment, particularly for robots that must navigate using both sight and sound. Tomar’s major contribution is the development of frequency-adaptive acoustic field prediction, a novel technique that enables robotic policies trained in simulation to generalize effectively to the complex, noisy acoustics of the physical world. His most-cited paper, “Sim2Real Transfer for Audio-Visual Navigation with Frequency-Adaptive Acoustic Field Prediction” (2024, 4 citations), is already attracting attention for its practical approach to a longstanding problem. By tackling the sim-to-real gap for audio-visual navigation—an area that has lagged behind vision-only transfer—Tomar is paving the way for more robust, perceptually aware robots. His work holds significant promise for applications in search-and-rescue, autonomous exploration, and assistive robotics, where reliable navigation in dynamic, real-world soundscapes is essential.
Research Focus
Key Achievements
Top Papers
- 1