Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Robot Perception and Manipulation
Code COMP341
Coordinator Dr S Luo
Computer Science
Year CATS Level Semester CATS Value
Session 2021-22 Level 6 FHEQ Second Semester 15


This module aims to provide students with a comprehensive understanding of the benefits and drawbacks of the various methods in robotperception and manipulation and hands on experience using tools as well as coding of own algorithms.

Learning Outcomes

(LO1) Demonstrate a systematic understanding of the theoretical and practical aspects of robot perception and manipulation.

(LO2) Describe state-of-the-art techniques in robotics, particularly on visual and touch perception, perception algorithms and control methods for robot manipulation.

(LO3) Debate the benefits and drawbacks of the various methods in robot perception and manipulation.

(LO4) Apply the taught methods in real-world applications (e.g., warehouse robotics problems).

(LO5) Illustrate hands-on experience using tools as well as coding of own algorithms for robot perception and manipulation.

(S1) Self-management readiness to accept responsibility (i.e. leadership), flexibility, resilience, self-starting, appropriate assertiveness, time management, readiness to improve own performance based on feedback/reflective learning.

(S2) Positive attitudeA 'can-do' approach, a readiness to take part and contribute; openness to new ideas and a drive to make these happen. Employers also value entrepreneurial graduates who demonstrate an innovative approach, creative thinking, bring fresh knowledge and challenge assumptions.

(S3) Teamwork respecting others, co-operating, negotiating / persuading, awareness of interdependence with others.

(S4) Communication skills listening and questioning, respecting others, contributing to discussions, communicating in a foreign language.

(S5) Literacy application of literacy, ability to produce clear, structured written work and oral literacy - including listening and questioning.

(S6) Application of numeracy manipulation of numbers, general mathematical awareness and its application in practical conte§xts (e.g. measuring, weighing, estimating and applying formulae).

(S7) Problem solving analysing facts and situations and applying creative thinking to develop appropriate solutions.



•Overview of Robotics (2 lectures)
•Kinematics and dynamics (2 lectures)
•Sensors for robot perception: vision, force, tactile (2 lectures)
•Visual perception: object classification detection, segmentation (4 lectures)
•Contact perception: contact detection, contact modeling (4 lectures)
•Deep learning for robot perception: Artificial Neural Networks, Convolutional Neural Networks (4lectures)
•Control and Optimisation for dexterous manipulation: model predictive control, learning fromdemonstration (4 lectures)
•Reinforcement Learning: MC, DP, TD (4 lectures)
•Cooperating Robots: cooperative/dual-arm manipulators (2 lectures)
•Revision (2 lectures)

Teaching and Learning Strategies

Teaching Method 1 - lectures
Description: students will be expected to attend three hours of formal lectures in a typical week

Teaching Method 2 - labs
Description: one hour of labs per week (run by a PhD student demonstrator).

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 30


Timetable (if known)              
Private Study 110


EXAM Duration Timing
% of
Penalty for late
Final exam  0 minutes    80       
CONTINUOUS Duration Timing
% of
Penalty for late
(341.2) CA2  10 hours over the co    10       
(341.1) CA1  10 hours over the co    10       

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.