Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds
This work addresses motion planning challenges for quadruped robots by enabling automatic gait discovery, which is incremental as it builds on existing optimization methods but removes the need for manual contact specification.
The authors tackled the problem of whole-body motion planning for quadruped robots by developing a trajectory optimization framework that automatically discovers gaits and dynamic motions without pre-specifying contact details, resulting in the ability to solve eight different tasks with a simple cost function and online optimization on hardware.
In this work we present a trajectory Optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not pre-specify contact switches, timings, points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, here we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our trajectory Optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrated our approach with the robot's control and estimation framework such that optimization can be run online. By demonstrating a rough manipulation task with multiple dynamic contact switches, we exemplarily show how optimized trajectories and control inputs can be directly applied to hardware.