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.

(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.

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).

Standard on-campus delivery with minimal social distancing.
As our planning has already gone too far, even if the campus opens up, we will offer hybrid teaching
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Laboratory Work
Description: On-line synchronous/asynchronous sessions
Teaching Method 3 - Tutorial
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

•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)