Subramanian Ramamoorthy

research

∙ 10/12/2022

DiPA: Diverse and Probabilistically Accurate Interactive Prediction

Accurate prediction is important for operating an autonomous vehicle in ...

Anthony Knittel, et al. ∙

research

∙ 09/24/2022

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

Dynamic Movement Primitives (DMPs) offer great versatility for encoding,...

Artūras Straižys, et al. ∙

research

∙ 09/20/2022

Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models

Testing black-box perceptual-control systems in simulation faces two dif...

Craig Innes, et al. ∙

research

∙ 07/29/2022

Perspectives on the System-level Design of a Safe Autonomous Driving Stack

Achieving safe and robust autonomy is the key bottleneck on the path tow...

Majd Hawasly, et al. ∙

research

∙ 07/18/2022

A Novel Design and Evaluation of a Dactylus-Equipped Quadruped Robot for Mobile Manipulation

Quadruped robots are usually equipped with additional arms for manipulat...

Yordan Tsvetkov, et al. ∙

research

∙ 06/22/2022

On Specifying for Trustworthiness

As autonomous systems are becoming part of our daily lives, ensuring the...

Dhaminda B. Abeywickrama, et al. ∙

research

∙ 06/22/2022

Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?

Autonomous vehicles use a variety of sensors and machine-learned models ...

Aravinda Ramakrishnan Srinivasan, et al. ∙

research

∙ 06/20/2022

Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

Medical robotics can help improve and extend the reach of healthcare ser...

Carlo Tiseo, et al. ∙

research

∙ 05/21/2022

Risk-Driven Design of Perception Systems

Modern autonomous systems rely on perception modules to process complex ...

Anthony L. Corso, et al. ∙

research

∙ 03/15/2022

Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles

Motion prediction of road users in traffic scenes is critical for autono...

Morris Antonello, et al. ∙

research

∙ 02/25/2022

Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators

Soft actuators offer a safe and adaptable approach to robotic tasks like...

Manu Lahariya, et al. ∙

research

∙ 02/12/2022

Robust Learning from Observation with Model Misspecification

Imitation learning (IL) is a popular paradigm for training policies in r...

Luca Viano, et al. ∙

research

∙ 01/27/2022

Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities

Using large pre-trained models for image recognition tasks is becoming i...

Xin Du, et al. ∙

research

∙ 10/09/2021

Active Altruism Learning and Information Sufficiency for Autonomous Driving

Safe interaction between vehicles requires the ability to choose actions...

Jack Geary, et al. ∙

research

∙ 09/21/2021

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles

Local decision rules are commonly understood to be more explainable, due...

Xin Du, et al. ∙

research

∙ 09/16/2021

Automated Testing with Temporal Logic Specifications for Robotic Controllers using Adaptive Experiment Design

Many robot control scenarios involve assessing system robustness against...

Craig Innes, et al. ∙

research

∙ 08/06/2021

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

Understanding a controller's performance in different scenarios is cruci...

Simón C. Smith, et al. ∙

research

∙ 08/05/2021

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

Recognising the goals or intentions of observed vehicles is a key step t...

Josiah P. Hanna, et al. ∙

research

∙ 07/07/2021

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

Inverse reinforcement learning is a paradigm motivated by the goal of le...

Todor Davchev, et al. ∙

research

∙ 06/21/2021

Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications

This paper presents an extension to the probabilistic programming langua...

Craig Innes, et al. ∙

research

∙ 05/14/2021

Building Affordance Relations for Robotic Agents - A Review

Affordances describe the possibilities for an agent to perform actions w...

Paola Ardón, et al. ∙

research

∙ 05/02/2021

Learning data association without data association: An EM approach to neural assignment prediction

Data association is a fundamental component of effective multi-object tr...

Michael Burke, et al. ∙

research

∙ 03/16/2021

Formation Control for UAVs Using a Flux Guided Approach

While multiple studies have proposed methods for the formation control o...

John Hartley, et al. ∙

research

∙ 11/02/2020

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

Robotic control tasks are often first run in simulation for the purposes...

Craig Innes, et al. ∙

research

∙ 11/01/2020

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

Achieving the right balance between planning quality, safety and runtime...

Henry Pulver, et al. ∙

research

∙ 10/29/2020

Affordance-Aware Handovers with Human Arm Mobility Constraints

Reasoning about object handover configurations allows an assistive agent...

Paola Ardón, et al. ∙

research

∙ 09/18/2020

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

We propose an architecture for training generative models of counterfact...

Simón C. Smith, et al. ∙

research

∙ 08/18/2020

Residual Learning from Demonstration

Contacts and friction are inherent to nearly all robotic manipulation ta...

Todor Davchev, et al. ∙

research

∙ 08/03/2020

Action sequencing using visual permutations

Humans can easily reason about the sequence of high level actions needed...

Michael Burke, et al. ∙

research

∙ 07/23/2020

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

Semi-supervised learning improves the performance of supervised machine ...

Simón C. Smith, et al. ∙

research

∙ 07/04/2020

Self-Assessment of Grasp Affordance Transfer

Reasoning about object grasp affordances allows an autonomous agent to e...

Paola Ardón, et al. ∙

research

∙ 06/16/2020

Learning from Demonstration with Weakly Supervised Disentanglement

Robotic manipulation tasks, such as wiping with a soft sponge, require c...

Yordan Hristov, et al. ∙

research

∙ 06/08/2020

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...

Daniel Angelov, et al. ∙

research

∙ 04/15/2020

Affordances in Robotic Tasks – A Survey

Affordances are key attributes of what must be perceived by an autonomou...

Paola Ardón, et al. ∙

research

∙ 02/06/2020

Integrating Planning and Interpretable Goal Recognition for Autonomous Driving

The ability to predict the intentions and driving trajectories of other ...

Stefano V. Albrecht, et al. ∙

research

∙ 02/06/2020

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

Lessons learned from the increasing diversity of road trial deployments ...

Francisco Eiras, et al. ∙

research

∙ 02/04/2020

Learning robotic ultrasound scanning using probabilistic temporal ranking

This paper addresses a common class of problems where a robot learns to ...

Michael Burke, et al. ∙

research

∙ 02/03/2020

Elaborating on Learned Demonstrations with Temporal Logic Specifications

Most current methods for learning from demonstrations assume that those ...

Craig Innes, et al. ∙

research

∙ 12/18/2019

Lower Dimensional Kernels for Video Discriminators

This work presents an analysis of the discriminators used in Generative ...

Emmanuel Kahembwe, et al. ∙

research

∙ 11/29/2019

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

Context plays a significant role in the generation of motion for dynamic...

Todor Davchev, et al. ∙

research

∙ 09/16/2019

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...

Artūras Straižys, et al. ∙

research

∙ 07/31/2019

Disentangled Relational Representations for Explaining and Learning from Demonstration

Learning from demonstration is an effective method for human users to in...

Yordan Hristov, et al. ∙

research

∙ 07/23/2019

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

Past research has studied two approaches to utilise predefined policy se...

Stefano V. Albrecht, et al. ∙

research

∙ 07/22/2019

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...

Stefano V. Albrecht, et al. ∙

research

∙ 07/18/2019

Composing Diverse Policies for Temporally Extended Tasks

Temporally extended and sequenced robot motion tasks are often character...

Daniel Angelov, et al. ∙

research

∙ 07/15/2019

Vid2Param: Online system identification from video for robotics applications

Robots performing tasks in dynamic environments would benefit greatly fr...

Martin Asenov, et al. ∙

research

∙ 07/15/2019

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

While many multiagent algorithms are designed for homogeneous systems (i...

Stefano V. Albrecht, et al. ∙

research

∙ 07/10/2019

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...

Stefano V. Albrecht, et al. ∙

research

∙ 07/10/2019

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

Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...

Stefano V. Albrecht, et al. ∙

research

∙ 07/09/2019

Hybrid system identification using switching density networks

Behaviour cloning is a commonly used strategy for imitation learning and...

Michael Burke, et al. ∙

Subramanian Ramamoorthy

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