Janne Karttunen

Papers

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About

Janne Karttunen is a researcher at the intersection of robotics and artificial intelligence, with a primary focus on bridging the gap between simulated and real-world learning. His key research areas include deep reinforcement learning, transfer learning, and robotic action-space adaptation. Karttunen’s major contribution lies in developing methods that allow policies trained in video game-like simulations to be effectively transferred to physical robots, dramatically reducing the need for costly real-world training hours. His most cited work, "From Video Game to Real Robot: The Transfer Between Action Spaces" (2020), has garnered 2 citations and explores how action-space discrepancies between virtual and physical environments can be reconciled, enabling robots to leverage the speed and safety of simulation-based learning. This foundational study has implications for accelerating robotic skill acquisition in manufacturing, healthcare, and autonomous systems. Karttunen’s work is notable for its practical approach to one of robotics’ most persistent challenges—the sim-to-real gap—and positions him as a promising voice in the ongoing effort to make machine learning more deployable in the physical world.