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About

Wojciech Zaremba is a pioneering AI researcher whose work sits at the intersection of deep reinforcement learning, robotics, and sim-to-real transfer — the challenge of training artificial agents in simulation and deploying them reliably in the physical world. His most influential contribution, domain randomization, introduced a deceptively elegant idea: by randomizing visual and physical parameters during simulated training, neural networks learn representations robust enough to bridge the "reality gap" to real hardware. This work has accumulated over 2,700 citations and fundamentally shaped how the robotics community approaches data-efficient policy learning. Zaremba extended these ideas through landmark projects including teaching a Shadow Dexterous Hand to reorient a Rubik's Cube entirely through sim-to-real transfer, and developing automatic domain randomization (ADR) to scale this approach to problems of unprecedented physical complexity. His contributions to Hindsight Experience Replay offered an elegant solution to sparse reward learning, while his multi-goal robotics benchmarks gave the research community shared infrastructure for progress. Across more than a dozen highly cited publications spanning one-shot imitation learning and asymmetric actor-critic methods, Zaremba has helped establish the modern foundations of dexterous robotic manipulation and remains a defining voice in applied reinforcement learning research.