Tanmay Agarwal
Papers
3
Total Citations
40
H-Index
3
About
Tanmay Agarwal is an emerging researcher at the intersection of multisensory machine learning, autonomous systems, and human-robot collaboration. His most influential contribution is to the ObjectFolder Benchmark project, a comprehensive suite of ten tasks designed to advance multisensory object-centric learning by integrating sight, sound, and touch — a framework that has rapidly garnered 24 citations since its 2023 publication and represents a significant step forward in how machines perceive and interact with physical objects. Beyond perception, Agarwal has explored the challenges of autonomous decision-making in complex real-world environments, developing affordance-based reinforcement learning approaches for urban driving that aim to improve the generalizability of autonomous vehicle pipelines to previously unseen scenarios. His work also extends beneath the ocean's surface, where he has investigated predictive motion modeling of human divers to enable more fluid and effective collaboration between autonomous underwater vehicles and scuba divers. Collectively, Agarwal's research reflects a broad commitment to building robots and autonomous systems that can sense richer information, adapt intelligently, and work alongside humans across diverse and demanding environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Affordance-based Reinforcement Learning for Urban Driving9 citations · 2021
- 3