Indranil Sur
Papers
3
Total Citations
11
H-Index
2
About
Indranil Sur is a researcher working at the intersection of robotics, artificial intelligence, and autonomous systems, with a particular focus on developing machines that can learn, adapt, and even "feel" in a computational sense. His most cited work, "Robots that anticipate pain: Anticipating physical perturbations from visual cues through deep predictive models" (2017, 7 citations), introduces a novel machine learning approach that enables robots to proactively avoid physical damage by anticipating perturbations from visual cues—a concept that reimagines system integrity through the lens of predictive modeling. Sur further explores this provocative theme in "“Should Robots Feel Pain?”—Towards a Computational Theory of Pain in Autonomous Systems" (2019, 2 citations), laying the groundwork for a formal theory of artificial nociception. His more recent work, "System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games" (2022, 2 citations), shifts focus to continual learning, addressing how autonomous systems can adapt in dynamic environments. By bridging deep learning, pain-inspired computation, and lifelong adaptation, Sur is pioneering frameworks that could fundamentally reshape how we design resilient, self-preserving AI and robotic systems.
Research Focus
Key Achievements
Top Papers
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