Quadruped robots are progressively being integrated into human environments.
Despite the growing locomotion capabilities of quadrupedal robots, their
interaction with objects in realistic scenes is still limited. While additional
robotic arms on quadrupedal robots enable manipulating objects, they are
sometimes redundant given that a quadruped robot is essentially a mobile unit
equipped with four limbs, each possessing 3 degrees of freedom (DoFs). Hence,
we aim to empower a quadruped robot to execute real-world manipulation tasks
using only its legs. We decompose the loco-manipulation process into a
low-level reinforcement learning (RL)-based controller and a high-level
Behavior Cloning (BC)-based planner. By parameterizing the manipulation
trajectory, we synchronize the efforts of the upper and lower layers, thereby
leveraging the advantages of both RL and BC. Our approach is validated through
simulations and real-world experiments, demonstrating the robot's ability to
perform tasks that demand mobility and high precision, such as lifting a basket
from the ground while moving, closing a dishwasher, pressing a button, and
pushing a door. Project website: this https URL

四足机器人在人类环境中的逐渐整合以及与实际场景中物体的互动通过将运动 - 操纵过程分解为低层次的强化学习和高层次的行为克隆，我们使得四足机器人能够仅使用其腿部完成现实世界的操纵任务，并通过模拟和实际实验验证了我们的方法。

从示范中学习视觉四足机器人的运动和操作技能

Learning Visual Quadrupedal Loco-Manipulation from Demonstrations

We study the problem of mobile manipulation using legged robots equipped with
an arm, namely legged loco-manipulation. The robot legs, while usually utilized
for mobility, offer an opportunity to amplify the manipulation capabilities by
conducting whole-body control. That is, the robot can control the legs and the
arm at the same time to extend its workspace. We propose a framework that can
conduct the whole-body control autonomously with visual observations. Our
approach, namely \ourFull~(\our), is composed of a low-level policy using all
degrees of freedom to track the end-effector manipulator position and a
high-level policy proposing the end-effector position based on visual inputs.
We train both levels of policies in simulation and perform Sim2Real transfer
for real robot deployment. We perform extensive experiments and show
significant improvements over baselines in picking up diverse objects in
different configurations (heights, locations, orientations) and environments.
Project page: this https URL

研究了使用装备有机械臂的四足机器人进行移动操纵的问题，提出了一种整体控制的框架，包括以视觉观测为基础的低层次控制策略和基于视觉输入的高层次策略，通过在仿真中训练并进行真实机器人部署的 Sim2Real 转换，取得了在不同配置（高度、位置、方向）和环境中拾取多样物体方面显著的改进。