Statistical Machine Learning and Motor Control Group
Humans are remarkable in their ability to achieve complex dynamic tasks that require memory, planning and optimal use of their body. Most importantly, we seem to be extremely good at adapting to changes in the environment or to our own bodies; re-learning at various timescales ranging from milliseconds to days and months. Would it not be great to have machines that are as versatile and robust?
In our group, we study all aspects of robot motion synthesis, from planning and representation to actuator design and control. We employ techniques from the fields of probabilistic inference and learning, stochastic optimal control, reinforcement (and apprenticeship) learning and large-scale optimization to tackle real world, real-time problems in anthropomorphic robotic systems. A cornerstone of our approach is data driven methods for learning and adaptation.
Broadly speaking, the Statistical Machine Learning and Motor Control Group at the University of Edinburgh conducts research under the following themes:
- Learning algorithms for optimal planning and control of large degree of freedom anthropomorphic robotic systems.
- Design, development and control of novel anthropomorphic hardware (e.g., variable impedance actuators).
- Study of optimal multi-sensory integration strategies and implications for neuro-prosthetics and exoskeletons.
- Study of computational principles behind human sensorimotor control (including psychophysics of human movement).
Meet the academic and professional staff, postdoc researchers and PhD students.
An up-to-date publication list can be found here, along with the topics of recent interest
Find out what has been happening in SLMC recently.
Teaching resources and list of Courses at Edinburgh University our group is associated with.
Our latest outreach activities including live demonstrations, keynotes and public engagement.
Videos that explain our research and outreach content for public engagement
A list of our active projects and funding agencies.
A look at the lab facilties and hardware we use and software open-sourced by our group.
Key topics of research and representative publications.
- Papers accepted at IROS 2023
- Congratulations (Dr.) Ran Long for successfully passing his DPhil viva
- Published Paper Royal Society Open Science
- Congratulations (Dr.) Russell Buchanan for successfully passing his DPhil viva
- Congratulations (Dr.) Traiko Dinev for a successful PhD Defence
- Congratulations (Dr.) Jiayi Wang for a successful PhD Defence