Papers
13
Total Citations
107
H-Index
6
About
Rania Rayyes is a leading researcher at the intersection of robotics, machine learning, and developmental artificial intelligence. Her work focuses on enabling robots to autonomously learn sensorimotor skills through data-efficient, online, and intrinsically motivated approaches. Rayyes has made major contributions to sample-efficient model learning, pioneering techniques like "Interest-Driven Exploration" and "Goal Babbling" that allow robots to bootstrap inverse kinematics and dynamics models with minimal interaction—reducing the sample complexity that has long hindered lifelong learning in real-world systems. Her highly cited work on *MetaGraspNetV2* (27 citations) advances robotic bin picking by integrating object relationship reasoning and dexterous grasping, while her recent survey on diffusion models for robotic manipulation (16 citations) positions her at the forefront of generative AI in robotics. Rayyes’s research has been published in top venues including ICRA, IROS, and IEEE Robotics and Automation Letters, and her innovations in symmetry-based exploration and hierarchical goal babbling have been recognized for enabling faster, more adaptive robot learning. With over 100 total citations, she is shaping the future of autonomous, open-ended robot skill acquisition.
Research Focus
Key Achievements
Top Papers
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- 2Diffusion models for robotic manipulation: a survey16 citations · 2025
- 3Learning Forward and Inverse Kinematics Maps Efficiently12 citations · 2018
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- 5Efficient Online Interest-Driven Exploration for Developmental Robots9 citations · 2020
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