Daphne Antotsiou

ISN Group · Electrical and Electronic Engineering · Imperial College London · South Kensington Campus · London SW7 2AZ · UK d.antotsiou17@imperial.ac.uk

I am currently a PhD candidate at Imperial College London and a member of the Imperial Computer Vision and Learning Lab. My main research interests are Machine Learning and Computer Vision. My main research topic is Imitation Learning in human-centric environments that involve object manipulation.


Presented at ICRA

Xi'an, China

Presented the work titled "Adversarial Imitation Learning with Trajectorial Augmentation and Correction" at ICRA.

June 2021

Presented at WiML

Montréal, Canada

Presented the work titled "Task Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation" at the WiML workshop of NeurIPS. Was awarded the WiML travel grant.

December 2018

Presented at ECCV

Munich, Germany

Presented the work titled "Task Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation" at the HANDS workshop of ECCV.

September 2018

PhD Candidate

Imperial College London, UK
September 2017 - Present

Research Software Engineer

Nordson DAGE, UK

Developing algorithms for Image Analysis and Automation on Manual X-ray Inspection Systems.

July 2015 - August 2017

MSc in Advanced Computing: Creative Technology

University of Bristol, UK

Thesis: Introducing Programming Concepts With WebGL.

2013 - 2014

Master's Diploma in Electrical and Computer Engineering

Aristotle University of Thessaloniki, Greece

Thesis: Detection and Classification of Gestures During the Eating Process Using Camera Feed.

2007 - 2013


Adversarial Imitation Learning with Trajectorial Augmentation and Correction


Abstract: Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks. A way to overcome this shortage of labels is through data augmentation. However, this cannot be easily applied to control tasks due to the sequential nature of the problem. In this work, we introduce a novel augmentation method which preserves the success of the augmented trajectories. To achieve this, we introduce a semi-supervised correction network that aims to correct distorted expert actions. To adequately test the abilities of the correction network, we develop an adversarial data augmented imitation architecture to train an imitation agent using synthetic experts. Additionally, we introduce a metric to measure diversity in trajectory datasets. Experiments show that our data augmentation strategy can improve accuracy and convergence time of adversarial imitation while preserving the diversity between the generated and real trajectories.

June 2021

Task Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation


Abstract: Human hand actions are quite complex, especially when they involve object manipulation, mainly due to the high dimensionality of the hand and the vast action space that entails. Imitating those actions with dexterous hand models involves different important and challenging steps: acquiring human hand information, retargeting it to a hand model, and learning a policy from acquired data. In this work, we capture the hand information by using a state-of-the-art hand pose estimator. We tackle the retargeting problem from the hand pose to a 29 DoF hand model by combining inverse kinematics and PSO with a task objective optimisation. This objective encourages the virtual hand to accomplish the manipulation task, relieving the effect of the estimator's noise and the domain gap. Our approach leads to a better success rate in the grasping task compared to our inverse kinematics baseline, allowing us to record successful human demonstrations. Furthermore, we used these demonstrations to learn a policy network using generative adversarial imitation learning (GAIL) that is able to autonomously grasp an object in the virtual space.

September 2018