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Dinesh Jayaraman

Dinesh Jayaraman

· Assistant Professor

University of Pennsylvania · Computer and Information Science

Active 2012–2026

h-index24
Citations3.4k
Papers13683 last 5y
Funding—

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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Research topics

  • Computer science
  • Artificial intelligence
  • Machine learning
  • Computer vision
  • Human–computer interaction

Selected publications

  • DrEureka: Language Model Guided Sim-To-Real Transfer

    2024-07-15 · 23 citations

    articleSenior author
  • TLControl: Trajectory and Language Control for Human Motion Synthesis

    Lecture notes in computer science · 2024-12-01 · 15 citations

    book-chapter
  • Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model

    arXiv (Cornell University) · 2024-10-03 · 1 citations

    preprintOpen access

    Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting their broader applications. To overcome this challenge, we present Articulate-Anything, a system that automates the articulation of diverse, complex objects from many input modalities, including text, images, and videos. Articulate-Anything leverages vision-langua…

  • Motion Capture with Millimeter-Wave Tags

    2026-05-08

    articleOpen access

    This paper introduces M3oCap, a millimeter-wave (mmWave) tag-based motion capture system that delivers accurate 6 degrees of freedom motion tracking. M3oCap utilizes a single commercial mmWave radar and custom-designed mmWave backscatter tags (2.5 cm × 3 cm) to localize and track the motion of tagged objects. Our system features novel algorithms that effectively isolate weak backscattered signals while accurately recovering phase changes induced by motion, allowing high-rate tracking of tag move…

  • Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping

    arXiv (Cornell University) · 2026-03-03

    preprintOpen accessSenior author

    The ability to conduct and learn from interaction and experience is a central challenge in robotics, offering a scalable alternative to labor-intensive human demonstrations. However, realizing such "play" requires (1) a policy robust to diverse, potentially out-of-distribution environment states, and (2) a procedure that continuously produces useful robot experience. To address these challenges, we introduce Tether, a method for autonomous functional play involving structured, task-directed inte…

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