IEEE RAS Embodied Intelligence Summer School

A five-day robotics and AI summer school at UCL East in London from 6-10 July 2026, focused on embodied intelligence through expert teaching, hands-on labs and team projects.

Dimitrios demoing silver robot to 3 students

About the Summer School

Where robotics meets embodied intelligence

This five-day intensive summer school at UCL East introduces embodied AI – the study of intelligent systems that perceive, reason and act in the physical world.

The programme is supported by the Institute of Electrical and Electronics Engineers Robotics and Automation Society (IEEE RAS) as part of its Technical Education Programme, with endorsement from its specialist technical committees and input from UCL academic experts.

The course brings together core robotics with modern learning approaches, from state estimation and navigation to whole-body control and vision-language-action models.

Participants take part in expert lectures, hands-on lab sessions and collaborative team projects, working with both simulation tools and real robot hardware. Lunch is provided daily.

A key feature of the programme is its near-peer model, pairing university students – IEEE RAS members and non-members – with high school students from London state schools. This creates a shared learning environment that supports collaboration across different levels of experience.

Applications are open

Complete our short application form for summer 2026. Places are limited and allocated through a competitive process. Apply by 24 May 2026.

Apply now

Who is the Summer School for?

Two tracks, one shared experience

University and high school students take part in the same lectures, labs and projects, working together in mixed teams.

Track A: University students

Undergraduate, Master’s and early-stage PhD students worldwide

Whether you’re studying computer science, engineering, physics or a related field, this programme will deepen your understanding of robotics and AI while building practical skills you can apply in study or research.

Entry requirements

Enrolled in an undergraduate, Master’s or early-stage PhD programme
Background or interest in robotics, AI, computer science or engineering
Proficiency in Python or a similar language (labs involve independent coding)
Solid grounding in mathematics, including linear algebra, calculus and probability
IEEE RAS membership is welcome but not required.

Track B: High school students

Year 11-13 students from Greater London state schools

Not sure if robotics or engineering is right for you? This is a practical introduction to the field – five days working alongside university students and researchers on real problems.

Entry requirements

Aged 16-18 and attending a Greater London state secondary school
A genuine interest in STEM (no prior robotics experience required)
GCSE or A-level Maths and/or Computer Science preferred.

What you will gain from the programme

Hands-on experience in robotics and AI

Go beyond theory. Work with simulation tools and real hardware to deploy what you've learned in lectures within the same day.

Learn from academic experts

Work directly with UCL's leading Robotics and AI researchers through lectures, labs and project mentoring.

Collaborate and build your network

Work closely with peers from different backgrounds and countries, building connections that continue beyond the programme.

Strengthen your CV or personal statement

Receive an IEEE RAS certificate of completion and gain experience that supports future study and career applications.

Mentoring and leadership skills (University students)

Develop mentoring and communication skills by supporting high school students in a near-peer learning environment.

Early exposure to the field (High school students)

Gain a clear understanding of robotics and AI before making subject and UCAS choices.

What you will study

The curriculum spans the full stack of embodied intelligence – from low-level state estimation to high-level vision-language-action models.

State Estimation & SLAM

Probabilistic state estimation, sensor fusion, simultaneous localisation and mapping, and visual SLAM for robotic systems.

Navigation, Planning & Safety

Autonomous navigation, path planning, formal methods for safe autonomy, and perception-driven decision-making for robots.

Whole-Body Control

Humanoid dynamics, balance control, gait generation, whole-body motion coordination, and locomotion-manipulation tasks.

Hands, Manipulation & VLA

Dexterous manipulation, tactile sensing, grasping strategies, and the emerging Vision-Language-Action paradigm for robot learning.

Programme structure

Each day combines expert lectures in the morning with hands-on tutorials and labs in the afternoon.

09:00 – 09:15: Welcome & Opening

Welcome address, summer school overview, logistics, and participant introductions.

09:15 – 10:00: Lecture 1: Foundations of State Estimation

Probabilistic methods for state estimation, Kalman filtering, sensor fusion for robotics, and IMU/encoder integration.

Tutor: Amir Patel

10:15 – 11:00: Lecture 2: Simultaneous Localisation & Mapping

SLAM fundamentals, graph-based SLAM, occupancy and feature-based mapping, and multi-robot SLAM.

Tutor: Sajad Saeedi

11:15 – 12:00: Lecture 3: Visual SLAM & Visual-Inertial Odometry

Feature-based and direct visual SLAM methods, visual-inertial odometry, and deep learning approaches for visual localisation.

Tutor: Sajad Saeedi

12:00 – 14:00: Lunch

Provided for all participants.

14:00 – 17:00: Tutorial & Lab: Hands-on SLAM & Visual SLAM

Practical implementation of SLAM and visual SLAM — working with camera and IMU data in simulation, building maps, evaluating localisation accuracy, and team formation with project brief introduction.

09:00 – 09:45: Lecture 1: Perception for Robot Navigation

Visual and geometric perception for autonomous navigation, terrain analysis, and scene understanding for legged and mobile platforms.

Dimitrios Kanoulas

10:00 – 10:45: Lecture 2: Path Planning for Autonomous Robots

Planning algorithms for autonomous systems, sampling-based and optimisation-based planners, and multi-robot coordination.

Pian Yu

11:00 – 11:45: Lecture 3: Formal Methods for Safe Autonomy

Safe robot decision-making, formal verification techniques, temporal logic specifications, and human–robot collaboration under safety guarantees.

Pian Yu

12:00 – 14:00: Lunch

Provided for all participants.

14:00 – 17:00: Tutorial & Lab: Navigation & Planning in Simulation

Implementing navigation and planning algorithms, integrating perception with planning pipelines, testing on simulated robotic platforms, and team project ideation with instructor feedback.

09:00 – 09:45: Lecture 1: Humanoid Dynamics & Balance Control

Humanoid kinematics and dynamics, ZMP and CoM control, stability and balance, and whole-body motion coordination frameworks.

Chengxu Zhou

10:00 – 10:45: Lecture 2: Gait Generation & Locomotion-Manipulation

Bipedal gait generation, whole-body trajectory optimisation, and coordinating locomotion with manipulation tasks.

Valerio Modugno

11:00 – 14:00: Lunch & Free Discussion

Provided for all participants. Extended break for informal discussion and project planning.

14:00 – 17:00: Tutorial & Lab: Whole-Body Control on Simulated Humanoid

Hands-on control of a simulated humanoid robot — tuning balance controllers, implementing coordinated locomotion, performing manipulation while maintaining stability, and applying concepts to team projects.

09:00 – 09:45: Lecture 1: Dexterous Manipulation & Tactile Sensing

Robotic hands and grasping, tactile perception, sensorimotor learning for manipulation, and imitation learning for embodied agents.

Lorenzo Jamone

10:00 – 10:45: Lecture 2: Vision-Language-Action Models for Robotics

The emerging VLA paradigm — integrating visual perception, language understanding, and action generation for generalised robot control and manipulation.

Chris Xiaoxuan Lu

12:00 – 14:00: Lunch

Provided for all participants.

14:00 – 17:00: Tutorial & Lab: Manipulation, VLA & Team Project Work

Hands-on manipulation and VLA experimentation, followed by extended team project development — integrating the week's techniques with instructor mentoring. Testing on simulation and hardware.

09:00: Project Finalisation

Teams finalise their projects, prepare demonstrations, and rehearse presentations. Testing on Unitree humanoid hardware where applicable.

11:30: Project Presentations & Live Demos

Each team showcases their robot performing a learned behaviour or task. Live demonstrations and Q&A with all participants and instructors.

14:00: Panel: Future of Embodied AI & Robotics

Panel discussion on open research problems in embodied AI, career pathways in robotics (academia and industry), and how to stay involved with IEEE RAS.

All instructors + invited panellists

15:30: Certificate Awards & Closing

Best project awards, IEEE RAS certificates of completion, group photo, and closing remarks. Networking reception.

Academic demoing a yellow robot to a group of students

Why choose the UCL Summer School in Embodied Intelligence?

UCL is ranked #9 in the world

UCL is consistently ranked among the top universities globally (QS World University Rankings), recognised for excellence in education and research.

#2 in UK for research power

In the latest Research Excellence Framework (REF), UCL Computer Science ranked 2nd in the UK for research power, reflecting the scale and quality of its research.

State-of-the-art facilities

Study at UCL East’s Intelligent Robotics Lab, with access to dedicated robotics spaces, advanced equipment and real robot platforms.

World-leading researchers

UCL is home to internationally recognised researchers in robotics and AI, whose work shapes the field and informs teaching on the programme.

Meet the team

Prof. Dimitrios Kanoulas

Professor in Robotics & AI

Legged robots, perception, autonomous navigation, whole-body motion planning, embodied AI.