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20152022
most citedAffordances in Robotic Tasks -- A Survey

23 citations · 97 across the 30 of their papers we have counts for

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26 papers · 1 filter

cs.RO2022

Learning robotic cutting from demonstration: Non-holonomic DMPs using the Udwadia-Kalaba method

Artūras Straižys, Michael Burke, Subramanian Ramamoorthy

Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manip…

cs.RO2021

Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots

Simón C. Smith, Subramanian Ramamoorthy

Understanding a controller's performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynam…

cs.RO2021

Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles

Josiah P. Hanna, Arrasy Rahman, Elliot Fosong +5

Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When the…

cs.RO2021

Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning

Todor Davchev, Sarah Bechtle, Subramanian Ramamoorthy +1

Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours. Yet the notion of generality for learnt costs…

cs.RO2020

ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments

Craig Innes, Subramanian Ramamoorthy

Robotic control tasks are often first run in simulation for the purposes of verification, debugging and data augmentation. Many methods exist to specify what task a robot must comp…

cs.RO2020

PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving

Henry Pulver, Francisco Eiras, Ludovico Carozza +3

Achieving a proper balance between planning quality, safety and efficiency is a major challenge for autonomous driving. Optimisation-based motion planners are capable of producing…