Papers
75
Total Citations
1,080
H-Index
18
About
Subramanian Ramamoorthy is a prominent robotics and artificial intelligence researcher whose work spans robot manipulation, topological motion planning, embodied cognition, and human-robot interaction. Based at the University of Edinburgh, he has made foundational contributions to how autonomous systems perceive, reason, and act in complex real-world environments. Ramamoorthy's early work introduced topological approaches to trajectory classification using persistent homology and simplicial complexes, offering robots a principled way to reason abstractly about motion across general configuration spaces. His research on object affordances and manipulation has advanced how robots generalize grasping and handover tasks, including sensitive applications for users with mobility constraints. His contributions to learning from demonstration, particularly through residual adaptation of Dynamic Movement Primitives for contact-rich tasks, reflect a recurring theme of bridging theoretical elegance with practical robustness. Beyond manipulation, Ramamoorthy has explored active inference as a framework for embodied cognition, contributed to large-scale robot learning datasets like DROID (108 citations), and investigated robot deployment in high-stakes environments including offshore energy platforms and COVID-19 hospital settings. His interdisciplinary reach — touching neuroscience, topology, and healthcare robotics — underscores a career dedicated to building intelligent systems that operate meaningfully alongside humans in the real world.
Research Focus
Key Achievements
Top Papers
- 1DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset108 citations · 2024
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- 7Multiscale Topological Trajectory Classification with Persistent Homology35 citations · 2014
- 8The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces31 citations · 2018
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- 10Affordance-Aware Handovers With Human Arm Mobility Constraints25 citations · 2021