Adaptive CLF-MPC With Application To Quadrupedal Robots
This work addresses the need for stable and adaptive planning algorithms for quadrupedal robots in real-world scenarios like disaster response, though it appears incremental as it integrates existing CLF and MPC methods.
The paper tackled the problem of enabling legged robots to perform motion and manipulation tasks accurately in unknown environments by combining Control Lyapunov Functions (CLFs) and Model Predictive Control (MPC) into an adaptive framework, resulting in improved performance during interactions with un-modeled payloads and heavy objects as validated in simulation and hardware tests.
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are needed. In this work, we impose that the optimal state-input trajectories generated by Model Predictive Control (MPC) satisfy the Lyapunov function criterion derived in adaptive control for robotic systems. As a result, we combine the stability guarantees provided by Control Lyapunov Functions (CLFs) and the optimality offered by MPC in a unified adaptive framework, yielding an improved performance during the robot's interaction with unknown objects. We validate the proposed approach in simulation and hardware tests on a quadrupedal robot carrying un-modeled payloads and pulling heavy boxes.