59 citations · 457 across the 92 of their papers we have counts for
15 papers · 1 filter
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…
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…
Affordance-Aware Handovers with Human Arm Mobility Constraints
Paola Ardón, Maria E. Cabrera, Èric Pairet +4
Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities. While th…
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control
Simón C. Smith, Subramanian Ramamoorthy
We propose an architecture for training generative models of counterfactual conditionals of the form, 'can we modify event A to cause B instead of C?', motivated by applications in…
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Todor Davchev, Kevin Sebastian Luck, Michael Burke +3
Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can…
Action sequencing using visual permutations
Michael Burke, Kartic Subr, Subramanian Ramamoorthy
Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instil this ability in robots trained from relativel…
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study
Simón C. Smith, Subramanian Ramamoorthy
Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in…
Self-Assessment of Grasp Affordance Transfer
Paola Ardón, Èric Pairet, Ronald P. A. Petrick +2
Reasoning about object grasp affordances allows an autonomous agent to estimate the most suitable grasp to execute a task. While current approaches for estimating grasp affordances…
From Demonstrations to Task-Space Specifications: Using Causal Analysis to Extract Rule Parameterization from Demonstrations
Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that i…
Learning from Demonstration with Weakly Supervised Disentanglement
Yordan Hristov, Subramanian Ramamoorthy
Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teaching robots, is difficul…
Affordances in Robotic Tasks -- A Survey
Paola Ardón, Èric Pairet, Katrin S. Lohan +2
Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects. Historically, the concept derives from…
Interpretable Goal-based Prediction and Planning for Autonomous Driving
Stefano V. Albrecht, Cillian Brewitt, John Wilhelm +4
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition infor…
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
Francisco Eiras, Majd Hawasly, Stefano V. Albrecht +1
Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to…
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
Michael Burke, Katie Lu, Daniel Angelov +4
Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal…
Elaborating on Learned Demonstrations with Temporal Logic Specifications
Craig Innes, Subramanian Ramamoorthy
Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra…