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About

Valentin Dalibard is a leading researcher at the forefront of robotic manipulation and artificial intelligence, where his work bridges the gap between simulation and real-world dexterity. His primary research areas include reinforcement learning, sim-to-real transfer, and the development of generalist robotic agents. Dalibard’s major contributions are exemplified by his role in creating **RoboCat**, a self-improving generalist agent that leverages heterogeneous robotic experience to quickly master novel skills and embodiments—a foundational step toward versatile, multi-task robots. He also pioneered **DemoStart**, a demonstration-led auto-curriculum method that enables complex, multi-fingered hand manipulation from sparse rewards in simulation, drastically reducing the need for real-world data. With over 11 citations across his most prominent works, Dalibard’s impact is already evident in the rapid adoption of his methods by the robotics community. His achievements include advancing the use of foundation models for embodied AI, making him a key figure in the push toward autonomous, adaptable robots that can learn and improve across diverse tasks and environments.