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About

Vivek Myers is a researcher advancing the frontier of robot learning, with a focus on enabling machines to understand human intent, commonsense reasoning, and scalable data-driven manipulation. His work tackles a fundamental challenge: how can robots make nuanced decisions that align with human expectations, such as knowing not to dismantle a prized Lego car while tidying a desk? In his highly cited paper “Toward Grounded Commonsense Reasoning” (2024, 12 citations), Myers explores how large language models can be grounded in physical contexts to achieve this kind of judgment. He also contributed to “BridgeData V2” (2023, 12 citations), a large-scale, open dataset of over 60,000 robotic manipulation trajectories that has become a key resource for training generalist robot policies. In “Active Reward Learning from Online Preferences” (2023, 5 citations), he introduced methods for robots to adapt to human preferences without costly offline retraining, enabling more efficient and interactive learning. Myers’ work also includes semi-supervised language interfaces for instruction following and multimodal reward learning from rankings. With over 38 total citations across his most-cited papers, his research is shaping how robots learn grounded, adaptable behaviors from limited human feedback.