Robotics Institute Carnegie Mellon University : Robotics Education and Research Leader
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thumbnail image for post: Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning

Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning

Event Category:
PhD Thesis Defense
Speaker:
Yeeho Song
Event Time:
Monday, December 2nd - 12:30 PM
thumbnail image for post: Spatial Reasoning and Semantic Representations for Intelligent Multi-Robot Exploration and Navigation

Spatial Reasoning and Semantic Representations for Intelligent Multi-Robot Exploration and Navigation

Event Category:
PhD Thesis Proposal
Speaker:
Seungchan Kim
Event Time:
Monday, December 2nd - 3:30 PM
thumbnail image for post: Practical Challenges and Recent Advances in Data Attribution

Practical Challenges and Recent Advances in Data Attribution

Event Category:
VASC Seminar
Speaker:
Jiaqi Ma
Event Time:
Monday, December 2nd - 3:30 PM

Takeo Kanade Among 2024 John Scott Award Recipients

Takeo Kanade, professor of computer science and robotics at Carnegie Mellon University, has received the 2024 John Scott Award alongside two other world leaders in robotics research. The John Scott Award, established in 1815 by Scottish chemist and pharmacist John Scott to honor Benjamin Franklin,...

CMU and Partners Redefine Robotic Perception with NeuralFeels

A research team at Carnegie Mellon teamed up with Meta FAIR, the University of California, Berkeley, Technical University Dresden in Germany and the Centre for Tactile Internet with Human-in-the-Loop (CeTI) to propose NeuralFeels, a machine learning model that combines vision and touch sensing on a...

CMU Researchers Illuminate New Horizons in Low-Light Robotic Exploration

The framework allows the robot to create photorealistic scene representations in low-light environments. Robots lift heavy loads in warehouses, deliver meals to diners, and even tackle housework. However, a lot of robotic work goes u...
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Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning

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