Balancing and walking using full dynamics LQR control with contact constraints

Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humanoid performing balancing and stepping tasks. This feedback control approach captures the necessary synergies between the DoFs of the robot to guarantee good control performance. We show that for the considered tasks, it is only necessary to re-linearize the dynamics of the robot at different contact configurations and that increasing the number of LQR controllers along desired trajectories does not improve performance. Our result suggest that very simple controllers can yield good performance competitive with current state of the art, but more complex, optimization-based whole-body controllers. A video of the experiments can be found at https://youtu.be/5T08CNKV1hw.

I. Introduction

Biped robots that are expected to locomote in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. Although walking is a complex task involving both hybrid dynamics and underactuation, the level of controller complexity required to execute such a task is unclear. In recent years, optimal control strategies have seen success both in simulation and on real systems for torque controlled humanoids. Previous work, [1]–[5], have utilized Quadratic Programs (QPs) to compute inverse dynamics control optimized over a variety of constraints (e.g. dynamic consistency, joint tracking, friction cones, etc.). Trajectories are often planned in operational space and then converted to joint torques using the QPs. The problem can further be organized into hierarchies to solve whole-body control problems according to a set priority in goals such that tasks of higher priority will always be achieved first [6]. Unfortunately, along with the growing flexibility of these methods comes added computational overhead, complexity in tuning, and a lack of theoretical disturbance rejection metrics (such as the gain and phase margin of classical controls).