Papers
30
Total Citations
512
H-Index
11
About
Rahul Shome is a robotics researcher whose work spans motion planning, manipulation, multi-arm coordination, and robot perception, with a particular emphasis on solving real-world automation challenges in warehouse and industrial settings. His most cited contribution — a dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place (184 citations) — has become a foundational resource for researchers tackling logistics robotics. Shome has made significant advances in rearrangement planning, developing efficient algorithms for cluttered environments using pebble graphs and multi-arm synchronization strategies that address the combinatorial complexity of coordinating high-degree-of-freedom systems. His work on cloud automation explores how precomputed roadmaps can accelerate flexible industrial manipulation, while his task and motion planning framework for multiple manipulators offers a unified approach to combining discrete and geometric reasoning at scale. Beyond pure robotics, Shome has demonstrated creative interdisciplinary reach, applying robotic platforms to occupational exposure science. His research on legible robot paths further reflects a commitment to human-robot interaction. Collectively, his body of work, accumulating nearly 400 citations, positions him as an influential voice in practical, scalable robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Rearranging similar objects with a manipulator using pebble graphs38 citations · 2014
- 3Cloud Automation: Precomputing Roadmaps for Flexible Manipulation34 citations · 2015
- 4A General Task and Motion Planning Framework For Multiple Manipulators31 citations · 2021
- 5
- 6
- 7Exploring the utility of robots in exposure studies17 citations · 2019
- 8Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter16 citations · 2018
- 9An Experimental Study for Identifying Features of Legible Manipulator Paths15 citations · 2015
- 10