Jamie Rice
Papers
1
Total Citations
2
H-Index
1
About
Jamie Rice is a robotics researcher whose work centers on computationally efficient perception and manipulation for real-world robotic systems. Rice’s most-cited contribution, "An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items" (2025, 2 citations), tackles the critical challenge of enabling mobile manipulators to grasp diverse objects in unstructured environments. By integrating object detection, pose estimation, and grasp planning into a streamlined, end-to-end framework, Rice’s system achieves robust performance while minimizing computational overhead—a key barrier to deploying robots in dynamic settings like warehouses or homes. Though early in its citation impact, this work has already been recognized for its practical approach to bridging the gap between perception and action. Rice’s research addresses the broader need for lightweight, scalable solutions in assistive robotics and logistics automation, demonstrating a commitment to making robotic manipulation more accessible and efficient. With a focus on real-world applicability, Rice continues to push the boundaries of vision-based grasping, offering a promising path toward cost-effective, autonomous systems that can operate reliably in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1