Papers
62
Total Citations
1,130
H-Index
21
About
Jeffrey Ichnowski is a robotics researcher whose work spans robot manipulation, motion planning, computer vision, and cloud robotics — with a particular focus on making robots faster, smarter, and more capable in real-world environments. His research has tackled some of the field's most demanding challenges: teaching robots to smoothly manipulate deformable objects like fabric using deep imitation learning (109 citations), enabling robots to perceive and grasp transparent objects through neural radiance fields in his influential Dex-NeRF work (61 citations), and dramatically accelerating motion planning for warehouse bin-picking systems through deep learning integration (70 citations). His GOMP framework advanced grasp-optimized motion planning with direct industrial relevance, while his surgical robotics contributions — including automated peg transfer that surpasses human speed and consistency — demonstrate meaningful clinical potential. Ichnowski also pioneered FogROS and FogROS2, adaptive platforms that connect robots to cloud and fog computing resources, addressing real constraints in onboard computational power (38 citations combined). Spanning dynamic cable manipulation, sim-to-real transfer, and surgical automation, his body of work — collectively accumulating over 500 citations — reflects a researcher consistently bridging fundamental algorithmic innovation with tangible robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Deep learning can accelerate grasp-optimized motion planning70 citations · 2020
- 3Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects61 citations · 2021
- 4GOMP: Grasp-Optimized Motion Planning for Bin Picking50 citations · 2020
- 5
- 6
- 7FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 238 citations · 2023
- 8
- 9
- 10FogROS: An Adaptive Framework for Automating Fog Robotics Deployment37 citations · 2021