Diffusion and flow matching policies have recently shown remarkable
performance in robotic applications by accurately capturing multimodal robot
trajectory distributions. However, their computationally expensive inference, due
to iterative denoising or numerical integration of an ODE, limits their application as
real-time controllers for robots. We introduce a methodology that utilizes Optimal
Transport couplings between noise and samples, in order to force straight solutions

Abstract: Fast and accurate depth estimation remains a major challenge in computer vision, particularly at long distances and in dynamic scenes.

Inspired by biological vision, we explore the similarities between insect compound eyes and light field cameras - both of which enable depth perception by capturing multiple viewpoints of a scene. Additionally, we investigate whether the light-induced motion of photoreceptors in the fly eye can be used to

Abstract: Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with under-actuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty, and where there is a relative motion between the object and the robot, becomes a critical problem. In this talk, I will present some of our recent work on non-prehensile manipulation through a model-free paradigm with reinforcement learning.

As AI systems advance at a mind-blowing pace, privacy and security risks remain critically overlooked, even in privacy-preserving frameworks. Federated Learning (FL) was originally designed to enhance privacy by keeping data decentralized, yet it remains vulnerable to inference attacks, which can extract sensitive information from shared model updates.

Abstract: Robotics is enjoying a surge in interest, in part driven by the rapidly emerging developments in AI in the past decade. Beyond the research interest, what are autonomous robots really good for? I believe that autonomy used in an assistive capacity is a good goal to aim for. Such a human-centred perspective raises some new questions for AI, and how it is used within robotics.

Welcome to the UK Robotics Summer School web pages. We are organising a Summer School to take place from 2 to 6 June 2025 in the beautiful city of Edinburgh.

Overview: This is a 5 day Summer School from June 2-6 2025, aimed at developing learning, engagement and knowledge exchange within the field of robotics. This school will offer a combination of lectures, tutorials, and industry presentations, focusing on cutting-edge topics in robotics.

Location: Postgraduate Centre (PG01) Heriot-Watt University.

Abstract: The provoking interpretation of the reward hypothesis in “Reward is enough” [Silver e.a., 2021] is due to an overly wide scope “of what we mean by goals and purposes”, while in a more restricted setting the validity of the reward hypothesis can be precisely established [Bowling e.a., 2023]. There are obviously applications of reinforcement learning where the validity of the reward hypothesis is less clear, such as in multi-agent, multi-objective, and biological reinforcement learning, as well as in learning under uncertainty or from human feedback.

 Dr. Oscar Mendez Maldonado is an award-winning researcher specializing in robotics, computer vision, and machine learning. Currently serving as the Machine Vision Lead at Locus Robotics, he has significantly contributed to the fields of autonomous agents, collaborative robotics, and high-level scene understanding. Dr. Mendez earned his Ph.D. in Robotics and Computer Vision from the University of Surrey, where his thesis on "Collaborative Strategies for 3D Reconstruction, Localisation, and Pathplanning" was awarded the BMVA Sullivan Thesis Prize in 2018. Dr.

Abstract

Speaker: Leonardo Hinckeldey

Title: Assistax: A Benchmark for Robust Reinforcement Learning in Collaborative Multi-Agent Settings