Max Robotics — Global Service Robot Directory + US Certification

About

Sachin Katti is a researcher at the forefront of cloud robotics, networked systems, and machine learning for autonomous platforms. His work addresses a critical challenge in modern robotics: how resource-constrained robots, such as low-power drones and planetary rovers, can effectively leverage remote computational resources without being bottlenecked by communication constraints. Katti's most cited contribution, "Network Offloading Policies for Cloud Robotics: A Learning-Based Approach" (2019, 15 citations), introduced intelligent frameworks for deciding when and how robots should offload computationally expensive deep neural network tasks to the cloud. This line of inquiry extends across his portfolio, with complementary work on task-relevant representation learning that compresses sensory data streams — including video and LiDAR — to preserve only inference-critical information during transmission. His co-design philosophy, treating communication and machine inference as jointly optimizable systems, represents a particularly novel contribution to the field. Beyond robotics infrastructure, Katti has explored privacy-preserving distributed learning, notably applying personalized federated learning to autonomous vehicle trajectory prediction. Though his citation counts remain modest, his research addresses foundational problems that will grow increasingly important as edge-AI and connected autonomous systems become ubiquitous.