
Dinesh Jayaraman
· Assistant ProfessorUniversity of Pennsylvania · Computer and Information Science
Active 2012–2026
Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.
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 authorTLControl: Trajectory and Language Control for Human Motion Synthesis
Lecture notes in computer science · 2024-12-01 · 15 citations
book-chapterarXiv (Cornell University) · 2024-10-03 · 1 citations
preprintOpen accessInteractive 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 accessThis 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 authorThe 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…
Frequent coauthors
- 39 shared
Kristen Grauman
- 34 shared
Sergey Levine
- 23 shared
Osbert Bastani
California University of Pennsylvania
- 18 shared
Yecheng Jason Ma
- 17 shared
Chelsea Finn
- 14 shared
Jianing Qian
University of Pennsylvania
- 13 shared
Brian Yang
- 12 shared
Glen Berseth
Labs
Penn Engineering's TeamPI
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