Search Results for author:
Found 21 papers, 6 papers with code
Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations
The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in-vivo human carotid data.
Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation
In particular, physiological monitoring methods such as Electroencephalography (EEG) are intrusive to drivers; while behavioural monitoring is least robust, affected by external factors such as lighting, as well as being subject to privacy concerns.
Vision-based system identification and 3D keypoint discovery using dynamics constraints
This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken from a static camera, using only the family of equations of motion of the object of interest as weak supervision.
Learning data association without data association: An EM approach to neural assignment prediction
Data association is a fundamental component of effective multi-object tracking.
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates
This distribution can then be used as a policy certificate in downstream applications.
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC) but can struggle in the context of insertion.
Action sequencing using visual permutations
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 relatively few examples.
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
Learning low-dimensional latent state space dynamics models has been a powerful paradigm for enabling vision-based planning and learning for control.
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy.
Black-Box Saliency Map Generation Using Bayesian Optimisation
This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model.
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks
Our framework improves Convolutional Neural Network (CNN) trained for prediction by using Generative Adversarial networks (GAN) for targeted data augmentation based on a population bias visualisation strategy that groups faces with similar facial attributes and highlights where the model is failing.
Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
Context plays a significant role in the generation of motion for dynamic agents in interactive environments.
Disentangled Relational Representations for Explaining and Learning from Demonstration
Learning from demonstration is an effective method for human users to instruct desired robot behaviour.
Composing Diverse Policies for Temporally Extended Tasks
Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics.
Hybrid system identification using switching density networks
This paper introduces switching density networks, which rely on a categorical reparametrisation for hybrid system identification.
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias.
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms
Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business.
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis
In this paper, we investigate the use of depthwise separable convolutions to reduce training time while maintaining data generation performance.
From explanation to synthesis: Compositional program induction for learning from demonstration
This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators.
Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons
A great deal of work aims to discover large general purpose models of image interest or memorability for visual search and information retrieval.
Single camera pose estimation using Bayesian filtering and Kinect motion priors
This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information.
