Zohar Feldman

Robert Bosch (India), Robert Bosch (Germany)

Papers

Total Citations

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About

Zohar Feldman is a robotics researcher whose work centers on robot manipulation, with a particular focus on robotic grasping and the development of intelligent learning systems that enable robots to interact with objects in real-world environments. Feldman's research bridges classical motion planning and modern machine learning, exploring how robots can be trained to both shift and grasp objects using elaborate motion primitives — a hybrid approach that has garnered 13 citations since its publication in 2022, reflecting meaningful engagement from the robotics community. Building on this foundation, Feldman has advanced the field further by tackling one of its more nuanced challenges: how robots can adapt to novel, out-of-domain scenarios they were never explicitly trained on. His 2024 work on uncertainty-driven exploration strategies introduces an innovative framework for online grasp learning, allowing robots to intelligently explore and adapt in real time rather than relying solely on static, pre-trained models. This focus on uncertainty quantification and adaptive learning represents a forward-thinking direction in robotics research. Altogether, Feldman's contributions speak to a commitment to making robotic systems more robust, flexible, and deployable across the diverse and unpredictable conditions of real-world settings.