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researcher

S. Ramamoorthy

109 papers hereh-index 273k citations232 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author22
  • last author82

Across the 105 of 109 papers where every author was matched, so the position is known.

fields
  • cs.RO53
  • cs.AI27
  • cs.CV10
  • cs.LG8
  • cs.CL2
  • cs.GT2
same name
  • S. Ramamoorthy — 2 papers, h 4
  • S. Ramamoorthy — 1 paper, h 4
  • S. Ramamoorthy — 1 paper, h 34
  • S. Ramamoorthy — 1 paper, h 8
  • S. Ramamoorthy — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122026
most citedBelief and Truth in Hypothesised Behaviours

59 citations · 457 across the 92 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018★ 19 cited

Interpretable Latent Spaces for Learning from Demonstration

Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy

Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained w…

cs.RO2018

FPR -- Fast Path Risk Algorithm to Evaluate Collision Probability

Andrew Blake, Alejandro Bordallo, Kamen Brestnichki +4

As mobile robots and autonomous vehicles become increasingly prevalent in human-centred environments, there is a need to control the risk of collision. Perceptual modules, for exam…

cs.AI2018★ 36 cited

The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces

Helen Hastie, Katrin Lohan, Mike Chantler +5

We present the UK Robotics and Artificial Intelligence Hub for Offshore Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded, multi-site project. The ORCA Hub v…

cs.AI2018

Reasoning about Unforeseen Possibilities During Policy Learning

Craig Innes, Alex Lascarides, Stefano V Albrecht +2

Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.