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About

Jon Oberlander is a leading researcher in human-robot interaction and natural-language generation, whose work has fundamentally shaped how robots communicate with people in collaborative tasks. His key research areas include multimodal referring expressions, situated dialogue systems, and adaptive human-robot communication. Oberlander's most influential contribution is his pioneering work on haptic-ostensive referring expressions in cooperative human-robot dialogue, a paper that has garnered 91 citations and established new frameworks for how robots can use touch and pointing gestures alongside speech to reference objects in shared physical spaces. He further advanced the field through rigorous evaluation studies of description and reference strategies in cooperative dialogue systems, demonstrating how naive users respond to different communicative approaches. His innovative work extends beyond physical robots into virtual environments, where he built adaptive museum galleries in Second Life that personalize content to individual visitors. Oberlander's research on situated reference generation in hybrid human-robot interaction systems, which integrates sub-symbolic goal inference with natural-language generation, has been particularly influential in enabling robots to detect and respond to human goals and errors during collaborative assembly tasks. His work represents a crucial bridge between computational linguistics and robotics, making human-robot collaboration more intuitive and effective.