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Dorsa Sadigh | Stanford HAI

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peopleFaculty,Senior Fellow

Dorsa Sadigh

Associate Professor of Computer Science and of Electrical Engineering, Stanford University | Senior Fellow, Stanford HAI

Topics
Robotics
External Bio

Dorsa Sadigh is an associate professor in Computer Science at Stanford University.  Her research interests lie in the intersection of robot learning and human-robot interaction. Specifically, she is interested in developing algorithms for adaptive learning agents that can learn from humans and interact with them. Sadigh received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and received her bachelor’s degree in EECS from UC Berkeley in 2012.  She is awarded the Sloan Fellowship, NSF CAREER, ONR Young Investigator Award and MIT TR35.

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Latest Related to Dorsa Sadigh

Research
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ReMix: Optimizing Data Mixtures for Large Scale Imitation Learning

Dorsa Sadigh, Chethan Anand Bhateja, Joey Hejna, Karl Pertsch, Yichen Jiang
Computer VisionRoboticsNatural Language ProcessingSep 05

Increasingly large robotics datasets are being collected to train larger foundation models in robotics. However, despite the fact that data selection has been of utmost importance to scaling in vision and natural language processing (NLP), little work in robotics has questioned what data such models should actually be trained on. In this work we investigate how to weigh different subsets or "domains'' of robotics datasets during pre-training to maximize worst-case performance across all possible downstream domains using distributionally robust optimization (DRO). Unlike in NLP, we find that these methods are hard to apply out of the box due to varying action spaces and dynamics across robots. Our method, ReMix, employs early stopping and action normalization and discretization to counteract these issues. Through extensive experimentation on both the Bridge and OpenX datasets, we demonstrate that data curation can have an outsized impact on downstream performance. Specifically, domain weights learned by ReMix outperform uniform weights by over 40% on average and human-selected weights by over 20% on datasets used to train the RT-X models.

Research
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Targeted Data Acquisition for Evolving Negotiation Agents

Dorsa Sadigh, Minae Kwon, Siddharth Karamcheti
Nov 25

Targeted Data Acquisition for Evolving Negotiation Agents