Subramanian Ramamoorthy

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Reader in the School of Informatics at The University of Edinburgh, Executive Committee Member of the Edinburgh Centre for Robotics

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

Robotic control tasks are often first run in simulation for the purposes...
11/02/2020 ∙ by Craig Innes, et al. ∙ 0 ∙ share

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

Achieving the right balance between planning quality, safety and runtime...
11/01/2020 ∙ by Henry Pulver, et al. ∙ 0 ∙ share

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Affordance-Aware Handovers with Human Arm Mobility Constraints

Reasoning about object handover configurations allows an assistive agent...
10/29/2020 ∙ by Paola Ardón, et al. ∙ 0 ∙ share

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Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control

We propose an architecture for training generative models of counterfact...
09/18/2020 ∙ by Simón C. Smith, et al. ∙ 14 ∙ share

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Residual Learning from Demonstration

Contacts and friction are inherent to nearly all robotic manipulation ta...
08/18/2020 ∙ by Todor Davchev, et al. ∙ 2 ∙ share

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Action sequencing using visual permutations

Humans can easily reason about the sequence of high level actions needed...
08/03/2020 ∙ by Michael Burke, et al. ∙ 0 ∙ share

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Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Semi-supervised learning improves the performance of supervised machine ...
07/23/2020 ∙ by Simón C. Smith, et al. ∙ 10 ∙ share

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Self-Assessment of Grasp Affordance Transfer

Reasoning about object grasp affordances allows an autonomous agent to e...
07/04/2020 ∙ by Paola Ardón, et al. ∙ 0 ∙ share

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Learning from Demonstration with Weakly Supervised Disentanglement

Robotic manipulation tasks, such as wiping with a soft sponge, require c...
06/16/2020 ∙ by Yordan Hristov, et al. ∙ 5 ∙ share

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From Demonstrations to Task-Space Specifications: Using Causal Analysis to Extract Rule Parameterization from Demonstrations

Learning models of user behaviour is an important problem that is broadl...
06/08/2020 ∙ by Daniel Angelov, et al. ∙ 0 ∙ share

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Affordances in Robotic Tasks – A Survey

Affordances are key attributes of what must be perceived by an autonomou...
04/15/2020 ∙ by Paola Ardón, et al. ∙ 0 ∙ share

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Integrating Planning and Interpretable Goal Recognition for Autonomous Driving

The ability to predict the intentions and driving trajectories of other ...
02/06/2020 ∙ by Stefano V. Albrecht, et al. ∙ 0 ∙ share

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A Two-Stage Optimization Approach to Safe-by-Design Planning for Autonomous Driving

Lessons learned from the increasing diversity of road trial deployments ...
02/06/2020 ∙ by Francisco Eiras, et al. ∙ 0 ∙ share

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Learning robotic ultrasound scanning using probabilistic temporal ranking

This paper addresses a common class of problems where a robot learns to ...
02/04/2020 ∙ by Michael Burke, et al. ∙ 0 ∙ share

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Elaborating on Learned Demonstrations with Temporal Logic Specifications

Most current methods for learning from demonstrations assume that those ...
02/03/2020 ∙ by Craig Innes, et al. ∙ 2 ∙ share

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Lower Dimensional Kernels for Video Discriminators

This work presents an analysis of the discriminators used in Generative ...
12/18/2019 ∙ by Emmanuel Kahembwe, et al. ∙ 61 ∙ share

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Learning with Modular Representations for Long-Term Multi-Agent Motion Predictions

Context plays a significant role in the generation of motion for dynamic...
11/29/2019 ∙ by Todor Davchev, et al. ∙ 0 ∙ share

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Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification

Precision cutting of soft-tissue remains a challenging problem in roboti...
09/16/2019 ∙ by Artūras Straižys, et al. ∙ 0 ∙ share

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Disentangled Relational Representations for Explaining and Learning from Demonstration

Learning from demonstration is an effective method for human users to in...
07/31/2019 ∙ by Yordan Hristov, et al. ∙ 0 ∙ share

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E-HBA: Using Action Policies for Expert Advice and Agent Typification

Past research has studied two approaches to utilise predefined policy se...
07/23/2019 ∙ by Stefano V. Albrecht, et al. ∙ 1 ∙ share

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Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems

This paper is concerned with evaluating different multiagent learning (M...
07/22/2019 ∙ by Stefano V. Albrecht, et al. ∙ 3 ∙ share

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Composing Diverse Policies for Temporally Extended Tasks

Temporally extended and sequenced robot motion tasks are often character...
07/18/2019 ∙ by Daniel Angelov, et al. ∙ 0 ∙ share

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Vid2Param: Online system identification from video for robotics applications

Robots performing tasks in dynamic environments would benefit greatly fr...
07/15/2019 ∙ by Martin Asenov, et al. ∙ 0 ∙ share

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On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems

While many multiagent algorithms are designed for homogeneous systems (i...
07/15/2019 ∙ by Stefano V. Albrecht, et al. ∙ 0 ∙ share

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An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types

Many multiagent applications require an agent to learn quickly how to in...
07/10/2019 ∙ by Stefano V. Albrecht, et al. ∙ 5 ∙ share

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Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
07/10/2019 ∙ by Stefano V. Albrecht, et al. ∙ 1 ∙ share

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Hybrid system identification using switching density networks

Behaviour cloning is a commonly used strategy for imitation learning and...
07/09/2019 ∙ by Michael Burke, et al. ∙ 4 ∙ share

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DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL

Many realistic robotics tasks are best solved compositionally, through c...
06/24/2019 ∙ by Daniel Angelov, et al. ∙ 0 ∙ share

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Learning Grasp Affordance Reasoning through Semantic Relations

Reasoning about object affordances allows an autonomous agent to perform...
06/24/2019 ∙ by Paola Ardón, et al. ∙ 0 ∙ share

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Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer

We propose a lifelong learning architecture, the Neural Computer Agent (...
06/17/2019 ∙ by Adeel Mufti, et al. ∙ 2 ∙ share

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Reasoning on Grasp-Action Affordances

Artificial intelligence is essential to succeed in challenging activitie...
05/25/2019 ∙ by Paola Ardón, et al. ∙ 0 ∙ share

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Towards Evaluating and Understanding Robust Optimisation under Transfer

This work evaluates the efficacy of adversarial robustness under transfe...
05/07/2019 ∙ by Todor Davchev, et al. ∙ 18 ∙ share

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Learning Programmatically Structured Representations with Perceptor Gradients

We present the perceptor gradients algorithm -- a novel approach to lear...
05/02/2019 ∙ by Svetlin Penkov, et al. ∙ 4 ∙ share

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To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions

Our brains are able to exploit coarse physical models of fluids to solve...
04/04/2019 ∙ by Tatiana Lopez-Guevara, et al. ∙ 0 ∙ share

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Using Causal Analysis to Learn Specifications from Task Demonstrations

Learning models of user behaviour is an important problem that is broadl...
03/04/2019 ∙ by Daniel Angelov, et al. ∙ 0 ∙ share

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From explanation to synthesis: Compositional program induction for learning from demonstration

Hybrid systems are a compact and natural mechanism with which to address...
02/27/2019 ∙ by Michael Burke, et al. ∙ 2 ∙ share

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Learning Best Response Strategies for Agents in Ad Exchanges

Ad exchanges are widely used in platforms for online display advertising...
02/10/2019 ∙ by Stavros Gerakaris, et al. ∙ 6 ∙ share

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Active Localization of Gas Leaks using Fluid Simulation

Sensors are routinely mounted on robots to acquire various forms of meas...
01/28/2019 ∙ by Martin Asenov, et al. ∙ 0 ∙ share

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Interpretable Latent Spaces for Learning from Demonstration

Effective human-robot interaction, such as in robot learning from human ...
07/17/2018 ∙ by Yordan Hristov, et al. ∙ 2 ∙ share

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Efficient Computation of Collision Probabilities for Safe Motion Planning

We address the problem of safe motion planning. As mobile robots and aut...
04/15/2018 ∙ by Andrew Blake, et al. ∙ 0 ∙ share

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The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces

We present the UK Robotics and Artificial Intelligence Hub for Offshore ...
03/06/2018 ∙ by Helen Hastie, et al. ∙ 0 ∙ share

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Reasoning about Unforeseen Possibilities During Policy Learning

Methods for learning optimal policies in autonomous agents often assume ...
01/10/2018 ∙ by Craig Innes, et al. ∙ 0 ∙ share

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Using Program Induction to Interpret Transition System Dynamics

Explaining and reasoning about processes which underlie observed black-b...
07/26/2017 ∙ by Svetlin Penkov, et al. ∙ 0 ∙ share

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Grounding Symbols in Multi-Modal Instructions

As robots begin to cohabit with humans in semi-structured environments, ...
06/01/2017 ∙ by Yordan Hristov, et al. ∙ 0 ∙ share

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Explaining Transition Systems through Program Induction

Explaining and reasoning about processes which underlie observed black-b...
05/23/2017 ∙ by Svetlin Penkov, et al. ∙ 0 ∙ share

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Estimating Activity at Multiple Scales using Spatial Abstractions

Autonomous robots operating in dynamic environments must maintain belief...
07/25/2016 ∙ by Majd Hawasly, et al. ∙ 0 ∙ share

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Belief and Truth in Hypothesised Behaviours

There is a long history in game theory on the topic of Bayesian or "rati...
07/28/2015 ∙ by Stefano V. Albrecht, et al. ∙ 0 ∙ share

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A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems

The ad hoc coordination problem is to design an autonomous agent which i...
06/03/2015 ∙ by Stefano V. Albrecht, et al. ∙ 0 ∙ share

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Bayesian Policy Reuse

A long-lived autonomous agent should be able to respond online to novel ...
05/01/2015 ∙ by Benjamin Rosman, et al. ∙ 0 ∙ share

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Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks

Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
01/30/2014 ∙ by Stefano V. Albrecht, et al. ∙ 0 ∙ share

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Subramanian Ramamoorthy

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