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About

Natasha Jaques is a robotics researcher whose work pushes the boundaries of what machines can achieve in dynamic, real-world environments. Her primary research areas include robot learning, reinforcement learning, and multi-agent systems. Jaques’s most notable contribution is leading the first learned robotic system to achieve amateur human-level performance in competitive table tennis—a physically demanding task requiring rapid perception, planning, and control. This breakthrough, detailed in her 2024 and 2025 papers, represents a significant step toward the long-standing robotics goal of human-level speed and dexterity in real-world tasks. Beyond physical robotics, she has advanced combinatorial optimization through multi-agent reinforcement learning for sequential satellite assignment problems, addressing complex, real-time allocation challenges. Earlier in her career, Jaques contributed to the RoboCup Small Size League as part of UBC Thunderbots, where she helped redesign hardware and AI systems for competitive robot soccer. Her work, while still early in citation accumulation, has already demonstrated high-impact, interdisciplinary contributions spanning from agile manipulation to space-based multi-agent coordination.