May 23, 2015
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.