We are interested in the control of complex movements in animals and robots. Biological movements can be modeled in detail using optimality principles - which is not surprising given that they are shaped by iterative optimization processes such as evolution, learning, adaptation. Similarly, the best way to engineer a complex control system is to specify a high-level performance criterion and leave the details to numerical optimization. In both areas, the main difficulty lies in actually performing the optimization. Thus our focus is on developing more powerful methods for optimal control and applying them to harder problems. A key tool we use is the MuJoCo physics engine.
Joseph Xu is now Postdoctoral Fellow at Yale.
Yuval Tassa and Tom Erez and now Research Scientists and Google UK.
Krishnamurthy Dvijotham is now CMI Postdoctoral Fellow at Caltech.
Evangelos Theodorou is now Assistant Professor at Georgia Tech.
NEWS ARTICLES ABOUT OUR WORK
Seeing the natural world with a physicists's lens
How can we learn efficiently to act optimally and flexibly?
Motor coordination in humans is guided by optimal feedback control
Optimal strategies for movement: Success with variability
Population vectors in motor cortex: Neural coding or epiphenomenon?