Cyril Ibrahim
Papers
1
Total Citations
2
H-Index
1
About
Cyril Ibrahim is a robotics researcher whose work lies at the intersection of tactile sensing and reinforcement learning, with a particular focus on enabling robots to learn complex, contact-rich tasks under sparse-reward conditions. His most-cited paper, "Touch-based Curiosity for Sparse-Reward Tasks" (2021), introduces a novel intrinsic motivation framework that leverages surprise from mismatches in tactile feedback—such as force and torque sensor readings—to guide exploration. This approach allows robots to autonomously discover meaningful interactions with their environment, even when external rewards are rare or absent. By integrating touch-based curiosity, Ibrahim’s work addresses a critical bottleneck in robotic manipulation: the difficulty of learning from limited feedback in real-world settings. Though early in his career, his contributions are already shaping how researchers think about combining physical sensing with curiosity-driven learning. His research holds promise for advancing autonomous systems in manufacturing, healthcare, and domestic robotics, where tactile feedback is essential for safe and dexterous interaction.
Research Focus
Key Achievements
Top Papers
- 1Touch-based Curiosity for Sparse-Reward Tasks2 citations · 2021