Microsoft Research Asia – Tokyo
Tokyo Talk Series is an initiative designed to foster intellectual exchange and collaboration within our research community. This series brings prominent speakers to Microsoft Research Asia – Tokyo to share their ideas and experiences. The talks will be scheduled on an ad-hoc basis, allowing flexibility to accommodate the availability of our guest speakers.
April 1, 2025: From Automation to Autonomy: Machine Learning for Next-generation Robotics
Speaker: Professor Sethu Vijayakumar FRSE, The University of Edinburgh, UK
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Abstract:
The new generation of robots work much more closely with humans, other robots and interact significantly with the environment around it. As a result, the key paradigms are shifting from isolated decision making systems to one that involves shared control — with significant autonomy devolved to the robot platform; and end-users in the loop making only high level decisions.
This talk will briefly introduce powerful machine learning technologies ranging from robust multi-modal sensing, shared representations, scalable real-time learning and adaptation, and compliant actuation that are enabling us to reap the benefits of increased autonomy while still feeling securely in control.
This also raises some fundamental questions: while the robots are ready to share control, what is the optimal trade-off between autonomy and control that we are comfortable with?
Domains where this debate is relevant include deployment of robots in extreme environments, self-driving cars, asset inspection, repair & maintenance, factories of the future and assisted living technologies including exoskeletons and prosthetics to list a few.
Bio:
Sethu Vijayakumar is the Professor of Robotics at the University of Edinburgh, UK and the Founding Director of the Edinburgh Centre for Robotics. He has pioneered the use of large-scale machine learning techniques in the real-time control of several iconic robotic platforms such as the SARCOS and the HONDA ASIMO humanoids, KUKA-LWR robot arm and iLIMB prosthetic hand. He had held adjunct faculty positions at the University of Southern California (USC), Los Angeles and the RIKEN Brain Science Institute, Japan. One of his landmark projects (2016) involved a collaboration with NASA Johnson Space Centre on the Valkyrie humanoid robot being prepared for unmanned robotic pre-deployment missions to Mars. Professor Vijayakumar, who has a PhD from the Tokyo Institute of Technology, holds the Royal Academy of Engineering (RAEng) – Microsoft Research Chair at Edinburgh. He has published over 250 peer reviewed and highly cited articles [H-index 50, Citations > 13,000 as of 2025] on topics covering robot learning, optimal control, and real-time planning in high dimensional sensorimotor systems. He is a Fellow of the Royal Society of Edinburgh, a judge on BBC Robot Wars and winner of the 2015 Tam Dalyell Prize for excellence in engaging the public with science. Professor Vijayakumar helps shape and drive the UK Robotics and Autonomous Systems (RAS) agenda in his recent role as the Programme Director for Robotics and Human AI Interfaces at The Alan Turing Institute, the UK’s national institute for data science and AI.
Sethu also serves as Senior Independent Director (SID) on the Japan Small Caps fund with Baillie Gifford Investment and has been advisor to several UK-Japan Governmental research initiatives for the Japan Ministry of Finance as well as the Ministry of Economy, Trade and Industry.
Webpage: https://web.inf.ed.ac.uk/slmc (opens in new tab)
LinkedIn: https://www.linkedin.com/in/sethu-vijayakumar (opens in new tab)
April 1, 2025: Contact-rich and Whole-body Manipulation
Speaker: Dr. João Moura, Senior Researcher, The University of Edinburgh, UK
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Abstract:
Contact-rich manipulation, which involves intricate contact and force interactions with the environment, is crucial for enabling dexterous robotic capabilities. Non-prehensile manipulation tasks, such as pushing or catching objects, present unique challenges due to under-actuation, hybrid dynamics, and model uncertainty. This talk will explore both model-based trajectory optimization and model-free reinforcement learning approaches for addressing these challenges. Furthermore, whole-body manipulation can significantly enhance a robot’s reachability and mobility. However, the increased degrees of freedom and multiple contact points introduce additional complexities, including high-dimensionality and nonlinear dynamics. Advancing contact-rich whole-body manipulation holds great potential across various domains, from nuclear decommissioning and assistive healthcare to enhancing robots’ ability to support daily-life activities.
Bio:
João Moura is a senior researcher at The University of Edinburgh, UK, and a research fellow at the Robotics and AI Collaboration (RAICo) in Whitehaven, UK. He earned his PhD (2021) and MSc by Research (with distinction, 2016) in Robotics and Autonomous Systems, jointly awarded by Heriot-Watt University and The University of Edinburgh, UK. He also holds a diploma degree (BSc + MSc, 2012) in Mechanical Engineering from the University of Aveiro, Portugal, where he graduated top of his cohort. Before pursuing PhD, João worked as a research assistant at INESC TEC Porto’s Centre for Robotics and Intelligent Systems, contributing to the FP7 European Project ICARUS—Integrated Components for Assisted Rescue and Unmanned Search operations. He also served as a teaching assistant at the University of Aveiro, lecturing courses in robotics, control systems, and programming. Currently, he collaborates with Waseda University as part of Goal 3 of Japan’s Moonshot programme. Previously, he participated in the H2020 European Project Harmony—Enhancing Healthcare with Assistive Robotic Mobile Manipulation. João has published in the top-tier robotics conferences and journals, including IROS, ICRA, CoRL, R:SS, RA-L, IJRR, and T-RO, having received nominations for Best Student Paper Award and Best Paper Award at the R:SS conference. His research focuses on trajectory optimization (TO), model predictive control (MPC), learning from demonstration (LfD), and reinforcement learning (RL) for contact-rich and non-prehensile manipulation.
Webpage: https://sites.google.com/view/joaomoura (opens in new tab)
LinkedIn: https://www.linkedin.com/in/joaopousamoura (opens in new tab)