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About

Huy Ha is a robotics researcher whose work spans robot learning, manipulation, and generalist robotic systems. His research addresses some of the field's most pressing challenges: enabling robots to operate robustly across diverse, real-world environments through large-scale data and learned policies. Ha has made significant contributions to foundational robotic datasets and models, most notably through his involvement in the **Open X-Embodiment** project (119 citations) and the **DROID** large-scale in-the-wild manipulation dataset (108 citations), both demonstrating that diverse, high-capacity training data can dramatically improve robotic generalization. These efforts parallel breakthroughs seen in NLP and computer vision, pushing robotics toward similarly powerful pretrained backbones. Beyond large-scale learning, Ha has explored creative manipulation strategies, including **FlingBot** (42 citations), which leveraged high-velocity dynamic actions to dramatically improve cloth unfolding efficiency — a counterintuitive yet highly effective approach. His earlier work includes decentralized multi-arm motion planning and generative gripper design via **Fit2Form**, reflecting a broad curiosity spanning hardware, planning, and learned control. With nearly 300 citations across recent publications, Ha's research is shaping the trajectory of scalable, generalizable robot learning — making him a compelling voice for students entering embodied AI and robotic manipulation.