Sequential action control (SAC) is a recently developed algorithm for optimal control of nonlinear systems. Previous work by the authors demonstrates that SAC performs well on several benchmark control problems. This work demonstrates applicability of SAC to a variety of robotic systems; we show that SAC can also be easily applied to hybrid systems without any modification and that its scalability facilitates application to high-dimensional systems. First, SAC is applied to a popular hybrid dynamic running model known as the spring-loaded inverted pendulum (SLIP). The results show that SAC can achieve dynamic hopping without using prescribed touchdown angles/leg stiffness. Moreover no specialized hybrid methods are necessary to handle the contact dynamics, despite the nonsmooth nature of the problem. The same SAC-controlled SLIP model is also implemented in a game for the Android operating system, demonstrating the minimal computational requirements for implementing SAC. Our second example involves successful stabilization and tracking control of a nonlinear, constrained dynamic model of a humanoid marionette with 56 states and 8 inputs. Finally, a discussion that includes best practices on tuning parameters of the SAC algorithm as well as the challenges of hardware implementation is also provided, along with a video that shows the resulting simulations for each example.
['Emmanouil Tzorakoleftherakis', 'Alex Ansari', 'Andrew D. Wilson', 'Jarvis A. Schultz', 'Todd D. Murphey']
Model-Based Reactive Control for Hybrid and High-Dimensional Robotic Systems