Learning objectives

On successful completion of the module, students should be able to:

  • Be able to formulate a short term research question, plan a methodology that involves making a robotic test bed, conduct experiments, analyse evidence, and make conclusions.
  • Apply robotics system design methods to develop an experimental prototype.
  • Select and utilize appropriate software in the programming and demonstration of a robotic system.
  • Be able to report test results in a structured manner (conference paper format).


The service robotics industry is growing at around 15% now. However, there are many challenges to be solved to make them safer and efficient. In this regard, Design Engineers will come in handy if they know how to formulate clear questions about service robots and test them in a methodical way. These questions can span many aspects of a robot from mechanisms, user interaction, control systems and their interfaces, to machine intelligence. The most important challenge is to be able to design an experiment to quantify the factors that will add most value to a robot in a given user context.

In this module, students will come up with a user case for a robotic solution, develop a hypothesis about the effect of a design criterion on the user experience, develop the robotic solution (experimental set up), test, obtain measurements, analyse data, and write a report in a conference paper format (6-8 pages). This covers a training needed to complete the full cycle of a methodical study in a short span of 12 weeks to produce a scientific report with evidence and interpretations for management decisions.

More details


A 2013 report by McKinsey [1] estimated that advanced robotics could generate a potential economic impact of USD1.7-4.5 trillion by 2025. It mentions that “These advanced robots have greater mobility, dexterity, flexibility, and adaptability, as well as the ability to learn from and interact with humans. In advanced economies, some workers might find new job opportunities in developing, maintaining, or working with robots”. The users of these future robotic co-workers will be diverse in terms of gender and culture. Therefore, it is pertinent to develop a robotics module that accounts for how gender matters in the design process. Recent work shows that user experiments must cover gender to obtain a robust picture of how different features of products and services affect user safety and satisfaction [2]. Recent work at the MIT media lab has found that gender plays an important role in human-robot interaction in terms of the persuasiveness or the ability to influence a user to change behavior [3]. Therefore, we will choose several domains of human-robot interaction that allow us to raise questions about how gender and culture of robot users and designers should be integrated into the design process of robots.

[1] McKinsey global report 2013: Disruptive technologies

[2] Schiebinger, Londa. “Gendered innovations: harnessing the creative power of sex and gender analysis to discover new ideas and develop new technologies.” Triple Helix 1.1 (2014): 9.

[3] Siegel, Mikey, Cynthia Breazeal, and Michael I. Norton. “Persuasive robotics: The influence of robot gender on human behavior.” Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. IEEE, 2009.

Suggested projects

The following suggested project scopes can be used to discuss further to identify a specific question you want to address for 12 weeks. You can discuss with the module leaders Thrishantha Nanayakkara, Petar Kormushev, and Nicolas Rojas to identify a refined research question to investigate deeper or you can propose a new project. You can do a project in a team of up to 3 members. You will work in the robotics research labs in the basement of the Dyson Building to conduct experiments. You will also get the support of the research teams in the robotics labs.

The role of morphological features of the fingertip in efficient touch sensation (Supervised by Professor Thrishantha Nanayakkara): Have you ever wondered why we have nails and particular shapes of bones in the fingers? Well there can be multiple purposes. We have questioned whether they have a role in simplifying the neural computation needed to separate touch sensations from different objects. In this project, you will do a simple FEM simulation with and without a nail and a distal palange bone in the fingertip to see how stress concentrations develop in the soft fingertip tissue for different tactile interaction scenarios. Then you will fabricate soft fingertips using silicone rubber with and without the above morphological features to conduct experiments. You will simulate biological mechanoreceptors using 3 Hall Effect sensors mounted around a permament magnet. The 3 data streams will be analysed using wavelet decomposition and eigen vector analysis to quantify the relative contributions from each morphological feature to improve the accuracy for distinguishing one object from another. You will use standard lab equipment and collaborate with a PhD student or a postdoc in the lab.

Haptic feedback from an endoscopic capsule (supervised by Professor Thrishantha Nanayakkara/Yukun Ge): Endoscopic capsulaes are used to take images inside the intestine to detect tissue anomalies such as bowel cancer. However, existing camera capsules can just move forward. We have tested an origami inspired soft capsule that can change its shape to go both ways to take more data from a suspected site in the intestine. The aim of this project is to design a soft tactile sensor for the capsule so that it can sense changes in mechanical properties in the intestine due to early development of tumors before visual symptoms appear from inside. You will collaborate with Yukun Ge, who is a former IDE student and a PhD candidate.

Goat hoof inspired shoe sole (supervised by Professor Thrishantha Nanayakkara/Dr. Saeed Bornasi/Shehara Perera): It will be very useful to have a slip resistant shoe for elderly people to go outdoors without worrying about slipping and falling. Our recent experiments to understand how the mountain goat hoofs reduce slip led to the discovery that the stiffness of certain joints play a key role in slip resistance. The hoof can passively deform and vibrate to reduce slip when the key joints in the hoof are in a certain range of stiffness. In this project, you will use biological inspirations and recent robotic findings to design and test a shoe sole printed using a multimaterial 3D printer or made by depositing soft silicon rubber layers. The experiment will involve directional slip resistance characteristics and comparisons between at least 2 designs. You will use already available laboratory equipment like an XY table and a 6 axis force torque sensor to conduct experiments.

A robotic knee joint for efficient locomotion (Supervised by Professor Thrishantha Nanayakkara/Dr. Saeed Bornasi/Shehara Perera): Recent work suggest that the cam profile of the human knee joint provides a joint angle dependent damping profile that make biped walking very efficient. The work is extended in a European Union funded project called Natural Intelligence. In this project, you will develop a new soft damper mounted inside a four-bar linkage mechanism to approximate the cam profile of a knee joint and test how the robot feels collision forces with and without the damper. The insights will be useful not only to design efficient robotic walkers, but also to design wearable exo-knee kits to assist elderly people to enjoy outdoors with ease. You will collaborate with a PhD student and a postdoc in the Natural Intelligence project.

Soft exo-suit to re-train tremor patients (Supervised by Professor Thrishantha Nanayakkara/Dr. Thilina Dulantha Lalitharatne): We have developed a 2-link planar robotic arm to simulate a tremor patient. The robot arm can reach obects with 5-10 Hz tremor. We hypothesize that haptic feedback given via an external exo-suit worn at the elbow would train an auxilliary controller in an unaffected area of the brain to compensate for tremor. We test this in a ROS based controller in a NVIDIA jetson single board computer. Your focus will be to design the haptic exo-suit controller using either electrical or pneumatic actuators (FlowIo system) and to conduct robotic experiments to quantify the effectiveness of its intervention. We will also collaborate with MIT Media lab.

Bioinspired whisker for mobile robots (Supervised by Professor Thrishantha Nanayakkara/Zhenhua Yu): Neuromorphic hardware enables fast and power-efficient neural network-based artificial intelligence, which is well suited for autonomous robots to perform a wide range of sophisticated tasks in complex, unknown environments. We argue that inspiration from animal whiskers is a promising alternative to classic methods in robotics for the artificial intelligence needed for the autonomy of small, mobile robots.We have designed and constructed a whisker sensor for mobile robots' terrain classification and navigation. The aim of this project is to investigate how stiffness control of the tapered whisker can be used as a control parameter to improve its identification performance. This research would enable the mobile robots to capture information across a broad frequency spectrum for environment perception.The mobile robot could also active control the whisker vibration to do objects inspection in narrow space just like the rats rapid ‘whisking’ of all vibrissae. You will use standard lab equipment and collaborate with Zhenhua Yu, a PhD student in the Morph Lab.

Natural Terrain Locomotion (Supervised by Professor Thrishantha Nanayakkara/Barry Mulvey):

Mobile robots are good at navigating on even and structured ground, like in factories and warehouses. However, unstructured environments (such as natural terrains) are more challenging, and wheeled robots can struggle when trying to navigate outdoors. There is significant opportunity to design a new structure (in place of the traditional tyre wheel) for use in these environments. Inspiration can be drawn from continuous tracks, whegs (wheel-legs), omnidirectional wheels, mecanum wheels, variable stiffness wheels, and others. The goal for this project is to develop novel wheel designs and test them against existing structures in natural environments. Experience with mechanics, CAD modelling, and 3D printing is preferable for this project.

Beyond Humanoid Prosthetic Hands (Supervised by Dr. Nicolas Rojas/Digby Chappell) For many years, researchers have attempted to make prosthetic hands as human-like as possible. Yet, despite considerable effort, there are many tasks that are nearly impossible with these existing hands. What if there was another way? Is it possible to surpass anthropomorphic hand design? In this project you will explore non-humanoid prosthetic hand design in order to achieve tasks that are currently not possible with conventional prosthetic hands.

Robotic assistant (Supervised by Dr Petar Kormushev): For this project, the students will use our Pepper robot (https://www.softbankrobotics.com/emea/en/pepper). The goal of this project is twofold: (1) exploratory, to study the built-in capabilities of the Pepper robot, and (2) practical, to implement a real-world human-robot interaction demo using Pepper. The goal is to come up with a meaningful task for Pepper to perform, in order to assist a human in some way. For example, an information assistance task, where a person is asking for information, and the robot is providing it in a multi-modal way (e.g. by talking, showing info on the screen, using hand gestures, or motion of the wheel base). The information could be something relevant to us, e.g. about the Dyson School, the research or location of staff members, or about Imperial’s campus. The research question to address is how to design the HRI interaction in such a way that it maximises the use of available robot capabilities to provide the most useful information in a fast and easy-to-access way. Additionally, it would be interesting to try to adapt the behaviour of the robot to the human user, e.g. according to the gender of the user, or the age, or other differences that are relevant to the task. Students may decide to dress the robot appropriately, or to give it a gender identity, if it makes the interaction task more realistic. Several projects can be made for different interaction tasks.

Walking robot (Supervised by Dr Petar Kormushev): For this project, the students will use our novel bipedal walking robot called SLIDER (http://www.imperial.ac.uk/robot-intelligence/robots/slider/). SLIDER has a unique design with ultra-lightweight knee-less legs made from carbon-fibre tubes. This design allows it to perform energy-efficient locomotion and fast agile push recovery. It also makes it easier to perform walking experiments in the lab due to being so lightweight and thus – safe. The goal of this project is to: (1) conduct virtual walking experiments with a 3D model of SLIDER inside a realistic simulator that we have prepared), (2) conduct real-world walking experiments similar to the simulated ones to test the performance of the physical robot, and (3) benchmark the performance of the robot on various tasks (e.g. walking on flat floor, walking on slope, climbing stairs, stepping over obstacles, etc.) and compare it to other known walking robots. Prominent examples of such other robots include the Boston Dynamics robots which have shown remarkable agile mobility on challenging terrains.


Suggested reading

For recent publications in the three groups involved, please visit Morphlab, REDS lab, and Robot Intelligence Lab.
In addition, the following publications give a good idea about human robot interaction experimental methods:

  1. Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and autonomous systems42(3-4), 143-166. PDF
  2. Lee, H. R., Sung, J., Šabanović, S., & Han, J. (2012, September). Cultural design of domestic robots: A study of user expectations in Korea and the United States. In 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication (pp. 803-808). IEEE. PDF
  3. Lee, H. R., & Sabanović, S. (2014, March). Culturally variable preferences for robot design and use in South Korea, Turkey, and the United States. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (pp. 17-24). ACM. PDF
  4. Carpenter, J., Davis, J. M., Erwin-Stewart, N., Lee, T. R., Bransford, J. D., & Vye, N. (2009). Gender representation and humanoid robots designed for domestic use. International Journal of Social Robotics1(3), 261. PDF
  5. Mumm, J., & Mutlu, B. (2011, March). Human-robot proxemics: physical and psychological distancing in human-robot interaction. In Proceedings of the 6th international conference on Human-robot interaction (pp. 331-338). ACM. PDF
  6. Libin, A. V., & Libin, E. V. (2004). Person-robot interactions from the robopsychologists’ point of view: The robotic psychology and robotherapy approach. Proceedings of the IEEE92(11), 1789-1803. PDF
  7. Blake, M. K., & Hanson, S. (2005). Rethinking innovation: context and gender. Environment and planning A37(4), 681-701. PDF


Deliverables and assessment

Learning outcomes

On successful completion of the module, students should be able to:

  • Apply robotics system design methods and biological principles to the development and construction of a novel intelligent robotic system.
  • Select and utilize appropriate software in the programming and demonstration of a robotic system.
  • Select and utilize appropriate fabrications techniques to develop and build a small robot that includes integrated electrical and mechanical components.
  • Discuss the opportunities and limitations of current robotics technologies.
  • Demonstrate an understanding of the potential scope of application for robotic systems.


Technical presentation on Wednesday 15th  December 2021 from 9am - 12 noon

Percentage of marks: 25%

Duration for each presentation: 15 mins (group or individual)

Assessment panel: Three staff members (Thrishantha Nanayakkara, Petar Kormushev, and Nicolas Rojas) will independently award marks for the clarity of each of the following objectives

Nature of feedback: Oral feedback at the end of each presentation


  1. To show what research question was addressed in the short project showing why it is important.
  2. To show what methods were used to address/investigate the question.
  3. To show what results were obtained and what can be concluded.
  4. To show how these insights can be useful to future robot design approaches.

Grade descriptor:

Assessment criteria Pass (D grade) Excellent (A grade)
Research question Broad outline available. A very clear question is articulated with clear evidence of why it is timely and important.
Methods Broad description is available throughout the presentation. Clear analytical and/or experimental methods are shown with clear evidence like figures.
Results Some qualitative and/or quantitative results are available. Insights not readily available in raw data have been derived through analysis.
Insights Student can answer questions about deeper insights. Student presents deeper insights based on own analysis and evidence. Student can relate findings to other work in the field.


Technical report due on 16th December 2021 by 4pm.

Percentage of marks: 75%

Length of report: 6-8 page technical report in IEEE conference paper format (2 column, Arial 11 font)

Assessment panel: At least two staff members (Thrishantha Nanayakkara, Petar Kormushev, Nicolas Rojas) will independently award marks for each report for the clarity of each of the following objectives

Nature of feedback: A collated document of independent written feedback in the format of a technical paper review


  1. To write an abstract showing what research question was addressed in the short project mentioning why it is important.
  2. To write an introduction outlining related work identifying a need to do the chosen project.
  3. To write a methods section showing what research methods were used to address/investigate the question.
  4. To write a results section showing experimental and/or analytical results.
  5. To write a conclusion section clearly articulating what can be concluded from the results.
  6. To write a discussion section showing insights that can be useful to future robot design approaches.

Grade descriptor:

Assessment criteria Pass (D grade) Excellent (A grade)
Abstract Broad outline of the work done is available. A very clear question is articulated highlighting why it is important to address it. The abstract clearly mentions the conclusion with summary findings.
Introduction Gives a broad outline to the project with at least 5 citations to relevant literature. A thorough discussion is done on the background literature citing more than 6 related papers. The introduction identifies an open challenge and outlines how the rest of the report is organized.
Methods Broad description of analytical and/or experimental methods is available. Clear analytical and/or experimental methods are shown with clear evidence like photos, diagrams, tables, and figures.
Results Some qualitative and/or quantitative results are available. Insights not readily available in raw data have been derived through analysis. Plots are clearly presented with labelled axes. Tables are informative and structured. Figure and table captions are concise and self-explanatory.
Conclusion A broad conclusion is available. A clear and concise conclusion is available based on evidence.
Discussions Student discusses broad insights, limitations, and opportunities for improvement. Student presents deeper insights based on own analysis and evidence. Student can relate findings to other work in the field.

Weekly schedule

Each project will be discussed in a weekly meeting during the time tabled slot. This will be the opportunity to discuss how to solve problems, related theory and techniques, related work done by others, report or paper writing, practicing presentations, troubleshooting, and any other project related topics. The first 3-weeks will be used to discuss some related research publications listed above, to refine a research question to be investigated, and to plan the resources and methods. The last 2-weeks will be spent on polishing the demonstration and the report.

Week-1, September 2021: Introduction, outline learning outcomes, and lab visit in the basement of the Dyson Building. Then students go out and prepare draft project proposals consisting of a) Objective, b) Methods, c) Expected outcomes, and d) Gantt chart (maximum 3 pages). We encourage to make groups of 2 students each.

Week-2, September 2021: Students submit project proposals. Meet and discuss to help sharpen the research questions and methods (0.5 hr of brief, 1 hr of discussion about possible research questions). Use this discussion to come up with a detailed experimentation plan and schedule. If there are collaborating PhD students involved, discuss their availability to do experiments.

 Week-3, October, 2021 – Week-10, December 2021: The first hour will be spent on theory discussions. Last year I taught

  • different methods for noise filtering in experimental data, such as Wavelet decomposition, Savitsky-Golay filters, Kalman Filters, smoothing.
  • statistical methods for hypothesis testing. This included power analysis to determine the sample size.
  • different robot design paradigms such as bio-inspiration, situated design, and concepts like embodied intelligence.
  • this year, I plan to discuss AI concepts like Bayesian inferencing, neural networks, and fuzzy inferencing.

The above theory discussion will be followed by a 2-hour lab session where I meet you individually while working on your projects to address ongoing problems. This happens in the basement robotics lab area. Students are expected to spend on average about 8-hours per week doing experiments and analysis.

15th December, 2021: Student groups do technical presentations (9am - 12 noon). These are 15 min conference style talks followed by a 10 min viva. A panel will give marks (25% of final grade).

By 4pm on 16th December 2021: Students submit a 6-page IEEE conference paper style report with a title, abstract, introduction, methods, results, discussion, and conclusions (75% of the final grade).


You will conduct experiments in the robotics lab area in the basement of the Dyson Building.

Resources available:

Pepper robot

3D printers, standard sensors like force sensors, servo motors, 3D motion capturing systems, EMG sensors, our own soft sensors and actuators, force sensors, workshop area to fabricate things.