代写COMP34212、代做Python/c++程序设计

日期：2024-04-28 08:51

COMP34212 Cognitive Robotics Angelo Cangelosi

COMP34212: Coursework on Deep Learning and Robotics

34212-Lab-S-Report

Submission deadline: 18 April 2024, 18:00 (BlackBoard)

Aim and Deliverable

The aim of this coursework is (i) to analyse the role of the deep learning approach within the

context of the state of the art in robotics, and (ii) to develop skills on the design, execution and

evaluation of deep neural networks experiments for a vision recognition task. The assignment will

in particular address the learning outcome LO1 on the analysis of the methods and software

technologies for robotics, and LO3 on applying different machine learning methods for intelligent

behaviour.

The first task is to do a brief literature review of deep learning models in robotics. You can give a

summary discussion of various applications of DNN to different robotics domains/applications.

Alternatively, you can focus on one robotic application, and discuss the different DNN models used

for this application. In either case, the report should show a good understanding of the key works in

the topic chosen.

The second task is to extend the deep learning laboratory exercises (e.g. Multi-Layer Perceptron

(MLP) and/or Convolutional Neural Network (CNN) exercises for image datasets) and carry out and

analyse new training simulations. This will allow you to evaluate the role of different

hyperparameter values and explain and interpret the general pattern of results to optimise the

training for robotics (vision) applications. You should also contextualise your work within the state

of the art, with a discussion of the role of deep learning and its pros and cons for robotics research

and applications.

You can use the standard object recognition datasets (e.g. CIFAR, COCO) or robotics vision datasets

(e.g. iCub World1, RGB-D Object Dataset2). You are also allowed to use other deep learning models

beyond those presented in the lab.

The deliverable to submit is a report (max 5 pages including figures/tables and references) to

describe and discuss the training simulations done and their context within robotics research and

applications. The report must also include on online link to the Code/Notebook within the report,

or ad the code as appendix (the Code Appendix is in addition to the 5 pages of the core report). Do

not use AI/LLM models to generate your report. Demonstrate a credible analysis and discussion of

1 https://robotology.github.io/iCubWorld/

2 https://rgbd-dataset.cs.washington.edu/index.html

COMP34212 Cognitive Robotics Angelo Cangelosi

your own simulation setup and results, not of generic CNN simulations. And demonstrate a

credible, personalised analysis of the literature backed by cited references.

Marking Criteria (out of 30)

1. Contextualisation and state of the art in robotics and deep learning, with proper use of

citations backing your academic brief review and statements (marks given for

clarity/completeness of the overview of the state of the art, with spectrum of deep learning

methods considered in robotics; credible personalised critical analysis of the deep learning

role in robotics; quality and use of the references cited) [10]

2. A clear introductory to the DNN classification problem and the methodology used, with

explanation and justification of the dataset, the network topology and the hyperparameters

chosen; Add Link to the code/notebook you used or add the code in appendix. [3]

3. Complexity of the network(s), hyperparameters and dataset (marks given for complexity

and appropriateness of the network topology; hyperparameter exploration approach; data

processing and coding requirements) [4]

4. Description, interpretation, and assessment of the results on the hyperparameter testing

simulations; include appropriate figures and tables to support the results; depth of the

interpretation and assessment of the quality of the results (the text must clearly and

credibly explain the data in the charts/tables); Discussion of alternative/future simulations

to complement the results obtained) [13]

5. 10% Marks lost if report longer than the required maximum of 5 pages: 10% Marks lost if

code/notebook (link to external repository or as appendix) is not included.

Due Date: 18 April 2024, h18.00, pdf on Blackboard. Use standard file name: 34212-Lab-S-Report