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About

Christoph Pohl is a robotics researcher whose work centers on affordance-based grasping, manipulation, and mobile manipulation in real-world, unstructured environments. His major contributions lie in developing frameworks that enable robots to identify interaction possibilities with complex scenes, fuse spatio-temporal affordance information probabilistically to handle noise and occlusions, and optimize robot placement for task success. His most cited paper, "Affordance-Based Grasping and Manipulation in Real World Applications" (2020, 24 citations), addresses the core challenge of making robotic solutions practical in unknown settings. Pohl has also advanced task-oriented grasping through visual imitation learning and introduced MAkEable, a memory-centered framework for transferring mobile manipulation skills across robots and environments. Notably, he contributed to the euROBIN robotics hackathon's door-to-door parcel delivery system and applied humanoid grasping to nuclear decontamination tasks, demonstrating real-world impact. His work on uncertainty-aware grasp metrics and sensitivity optimization further improves success rates in cluttered scenes. With a focus on bridging perception and action under uncertainty, Pohl’s research is shaping the next generation of versatile, autonomous robots capable of operating in human-centered spaces.