Papers
30
Total Citations
1,327
H-Index
13
About
Michael Danielczuk is a robotics researcher whose work sits at the intersection of robot manipulation, computer vision, and cloud robotics. He is best known for his contributions to robot grasping and bin picking, most notably his co-authorship of "Learning Ambidextrous Robot Grasping Policies" (2019), which has amassed over 578 citations and addresses the grand challenge of reliably grasping novel objects from unstructured heaps — a critical capability for e-commerce and manufacturing automation. His research on unknown object segmentation using Mask R-CNN trained on synthetic depth data (189 citations) has meaningfully advanced perception pipelines for robotic manipulation. Danielczuk also pioneered work on "Mechanical Search," developing algorithms that enable robots to locate and retrieve occluded target objects in cluttered environments. Beyond manipulation, he has made notable contributions to cloud and fog robotics through the FogROS and FogROS2 frameworks, which allow resource-constrained robots to seamlessly leverage cloud computing infrastructure. Additional contributions span push policies for bin picking, grasp motion planning optimization, adversarial grasp objects, and novel tactile suction cup design. With over 1,000 cumulative citations, Danielczuk has established himself as a versatile and impactful voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1Learning ambidextrous robot grasping policies578 citations · 2019
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- 3Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter108 citations · 2019
- 4Linear Push Policies to Increase Grasp Access for Robot Bin Picking62 citations · 2018
- 5GOMP: Grasp-Optimized Motion Planning for Bin Picking50 citations · 2020
- 6FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 238 citations · 2023
- 7FogROS: An Adaptive Framework for Automating Fog Robotics Deployment37 citations · 2021
- 8Adversarial Grasp Objects30 citations · 2019
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