Robotics Institute Carnegie Mellon University : Robotics Education and Research Leader
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thumbnail image for post: Rethinking Robot Safety: Adaptive and Scalable Methods for Real-World Autonomy

Rethinking Robot Safety: Adaptive and Scalable Methods for Real-World Autonomy

Event Category:
PhD Thesis Defense
Speaker:
Rui Chen
Event Time:
Tuesday, January 27th - 12:00 PM
thumbnail image for post: Empirically Grounded LLM-based Virtual Patients for Psychotherapy Training: Design, Modeling, and Evaluation

Empirically Grounded LLM-based Virtual Patients for Psychotherapy Training: Design, Modeling, and Evaluation

Event Category:
PhD Thesis Proposal
Speaker:
Angela Chen
Event Time:
Friday, January 30th - 9:00 AM
thumbnail image for post: Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns

Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns

Event Category:
PhD Thesis Defense
Speaker:
David Neiman
Event Time:
Tuesday, February 3rd - 12:30 PM

Preserving Pittsburgh’s Public Art, One Pixel at a Time

Public art, especially murals, can be fleeting. But Pittsburgh native Richard Palmer, a longtime collaborator with Carnegie Mellon University's CREATE Lab, has spent the last five years using innovative CMU technology to ensure the city's art and public murals won't be lost to history. [caption i...

Tulsiani Receives NSF CAREER Award

Shubham Tulsiani, an assistant professor in the Robotics Institute at Carnegie Mellon University, received a National Science Foundation CAREER Award for his project developing perception systems to better understand the 3D world. The CAREER Award is the NSF's most prestigious recognition for earl...

From Sci-Fi to Reality: SCS Alumni Reimagine Robot Locomotion

Aditya Sripada (left) and Abhishek Warrier (right) Curiosity allows humans to pull ideas from fiction and give life to them in the real world. For Carnegie Mellon University alumni Aditya Sripada and Abhishek Warrier, a question insp...
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Rethinking Robot Safety: Adaptive and Scalable Methods for Real-World Autonomy

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