Legged robots have shown remarkable advantages in navigating uneven terrain. However, realizing effective locomotion and manipulation tasks on quadruped robots is still challenging. In addition, object and terrain parameters are generally unknown to the robot in these problems. Therefore, this paper proposes a hierarchical adaptive control framework that enables legged robots to perform loco-manipulation tasks without any given assumption on the object's mass, the friction coefficient, or the slope of the terrain. In our approach, we first present an adaptive manipulation control to regulate the contact force to manipulate an unknown object on unknown terrain. We then introduce a unified model predictive control (MPC) for loco-manipulation that takes into account the manipulation force in our robot dynamics. The proposed MPC framework thus can effectively regulate the interaction force between the robot and the object while keeping the robot balance. Experimental validation of our proposed approach is successfully conducted on a Unitree A1 robot, allowing it to manipulate an unknown time-varying load up to $7$ $kg$ ($60\%$ of the robot's weight). Moreover, our framework enables fast adaptation to unknown slopes (up to $20^\circ$) or different surfaces with different friction coefficients.

翻译：腿式机器人在非平坦地形导航方面展现出显著优势。然而，在四足机器人上实现有效的行走与操控任务仍具挑战性。此外，这类问题中物体和地形参数通常对机器人而言是未知的。为此，本文提出一种分层自适应控制框架，使腿式机器人能够在不对物体质量、摩擦系数或地形坡度作任何先验假设的前提下执行行走-操控任务。在该方法中，我们首先提出一种自适应操控控制策略，通过调节接触力实现对未知地形上未知物体的操控。随后引入一种统一的模型预测控制（MPC）框架用于行走-操控，该框架将操控力纳入机器人动力学模型。所提出的MPC框架能有效调节机器人与物体之间的相互作用力，同时维持机器人平衡。通过在宇树A1机器人上成功开展实验验证，本方法使机器人能够操控高达7千克（占机器人自重的60%）的未知时变负载。此外，该框架支持快速适应未知坡度（最高20°）或具有不同摩擦系数的多种地面。