Assistant Professor

University of Pennsylvania

I am an assistant professor at UPenn’s GRASP lab, with a primary appointment in CIS, and a secondary appointment in ESE. I lead the Perception, Action, and Learning (PennPAL) Research Group, where we work on problems at the intersection of robotics, machine learning, and computer vision.

Research Statement: My research statement, last updated Aug 2024, explains our research vision.

Teaching: In Spring 2025, I am co-teaching a robot-learning seminar CIS 7000/ESE 6800 with Antonio Loquercio. Here are the past courses I have taught.

For prospective masters and undergraduate students If you're an undergraduate or MS student interested in collaborating with our PennPAL research group, please complete this form. We monitor responses regularly, and will reach out to you if there's an opening. Women and underrepresented minorities are especially encouraged to apply.
For prospective PhD students Formal admissions are processed through the university's various programs. You may send me email too, but please note that I might not be able to respond.

Bio: Here is a bio page that can be used for talks.

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Recent Publications
Long Le, Jason Xie, William Liang, Hung-Ju Wang, Yue Yang, Yecheng Jason Ma, Kyle Vedder, Arjun Krishna, Dinesh Jayaraman, Eric Eaton (2025). Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model. ICLR.
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Christopher Watson, Arjun Krishna, Rajeev Alur, Dinesh Jayaraman (2025). Illustrated Landmark Graphs for Long-Horizons Policy Learning. TMLR.
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Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford (2025). Learning to Achieve Goals with Belief State Transformers. ICLR.
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Jake Welde*, Nishanth Rao*, Pratik Kunapuli*, Dinesh Jayaraman, Vijay Kumar (2025). Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems. ICRA.
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Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee (2025). REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments. ICLR.
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