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Nino Calandra is a distinguished Research Scientist at NVIDIA, focusing on Artificial Intelligence, particularly in the realms of Robotics, Reinforcement Learning, and Embodied AI. He has a strong academic background with a PhD from TU Darmstadt and postdoctoral research at UC Berkeley, where he made significant contributions to Bayesian optimization and robot learning. Before joining NVIDIA, Nino was a Research Scientist at Meta AI (FAIR), further advancing the frontiers of intelligent robotic systems. His work often involves developing algorithms that enable robots to learn complex tasks through interaction with their environment, bridging the gap between simulation and real-world application. He is passionate about creating autonomous systems that can perceive, reason, and act effectively in complex, unstructured environments.
Nino Calandra's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Authored and co-authored numerous influential publications in top-tier AI and Robotics conferences and journals (e.g., NeurIPS, ICRA, CoRL, JMLR), significantly advancing areas like sample-efficient reinforcement learning, sim-to-real transfer, and Bayesian optimization for robotics.
Led and contributed to projects developing cutting-edge AI models that enable robots to perform complex manipulation tasks, such as dexterous in-hand manipulation and object rearrangement, often leveraging deep learning and reinforcement learning techniques.
Contributed to the AI research community through open-sourcing code, datasets, and benchmarks, fostering collaboration and accelerating progress in the field of robotic learning.
Developed and demonstrated effective techniques for transferring policies learned in simulation to physical robots, addressing the reality gap and enabling practical application of learned behaviors.
Completed a PhD focusing on 'Bayesian Optimization for Policy Search in Robotics' from TU Darmstadt, providing foundational work for efficient learning in robotic systems.
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