Jeff Mahler
Papers
2
Total Citations
39
H-Index
2
About
Jeff Mahler is a leading researcher in robotic manipulation, with a focus on developing robust grasping algorithms for real-world applications. His work lies at the intersection of machine learning, uncertainty quantification, and industrial automation. Mahler is best known for his foundational contributions to grasp planning under uncertainty, where he applied multi-armed bandit models to evaluate candidate grasps by sampling perturbations in shape, pose, and gripper approach. His 2015 paper on this topic, with 37 citations, has been influential in advancing robust grasping for warehouse order fulfillment and manufacturing. More recently, Mahler has explored the integration of deep learning with programmable logic controllers (PLCs) for industrial robot grasping, addressing the grand challenge of universal grasping of diverse, previously unseen objects from heaps. His 2020 work, though early in citation impact, signals a shift toward practical, deployable solutions in e-commerce and home service robotics. Mahler’s research bridges theory and application, making him a key figure in the push toward reliable, autonomous manipulation in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Multi-armed bandit models for 2D grasp planning with uncertainty37 citations · 2015
- 2