Tejas Patel
Papers
1
Total Citations
6
H-Index
1
About
Tejas Patel is an emerging researcher at the intersection of distributed systems and robot learning, with a focus on the infrastructure challenges that underpin large-scale machine learning for robotics. His most notable work, "RoboCache: A Distributed Key–Value Store for Petabyte–Scale Multimodal Robot Learning Datasets" (2025), addresses a critical but often overlooked bottleneck in modern robotics research: how to efficiently store, version, and serve the massive multimodal datasets — spanning images, point clouds, proprioceptive signals, actions, and language annotations — that contemporary robot learning pipelines demand. Recognizing the limitations of conventional cloud object stores for this specialized use case, Patel designed RoboCache as a high-performance, fault-tolerant system purpose-built for the unique access patterns and data heterogeneity of robotics workloads. Already accumulating 6 citations shortly after publication, the work signals growing community recognition of data infrastructure as a first-class research problem. For students and researchers building large-scale robot learning systems, Patel's contributions offer both practical tooling and a compelling framework for thinking about how systems design can accelerate progress at the frontier of embodied AI.
Research Focus
Key Achievements
Top Papers
- 1