Scoop-and-Toss：四足系统的动态物体收集
Scoop-and-Toss: Dynamic Object Collection for Quadrupedal Systems
AI总结：
本文提出Scoop-and-Toss框架，通过腿部铲状附件、背部托盘及分层专家-元策略，使四足机器人无需额外执行器即可动态铲抛并收集多个物体。
摘要：
四足机器人在运动方面取得了显著进展，将其能力从受控环境扩展到了现实世界应用。除移动之外，近期工作还探索了利用腿部执行按钮按压或开门等任务的运动-操作。尽管这些努力证明了基于腿部操作的可行性，但大多数都集中在相对静态的任务上。在本工作中，我们提出了一个框架，使四足机器人能够借助腿部的敏捷性，在无需额外执行器的情况下收集物体。通过在一条腿上安装一个简单的铲状附加件，机器人可以铲起物体并将其抛投到安装在背部的收集托盘中。我们的方法采用分层策略结构，包含两个专家策略——一个用于铲起和抛投，另一个用于接近物体位置——以及一个在二者之间动态切换的元策略。专家策略分别训练，随后进行元策略训练，以实现协调的多物体收集。该方法展示了四足机器人的腿部如何能够被有效用于动态物体操作，将其作用扩展到运动之外。
英文摘要：
Quadruped robots have made significant advances in locomotion, extending their capabilities from controlled environments to real-world applications. Beyond movement, recent work has explored loco-manipulation using the legs to perform tasks such as pressing buttons or opening doors. While these efforts demonstrate the feasibility of leg-based manipulation, most have focused on relatively static tasks. In this work, we propose a framework that enables quadruped robots to collect objects without additional actuators by leveraging the agility of their legs. By attaching a simple scoop-like add-on to one leg, the robot can scoop objects and toss them into a collection tray mounted on its back. Our method employs a hierarchical policy structure comprising two expert policies-one for scooping and tossing, and one for approaching object positions-and a meta-policy that dynamically switches between them. The expert policies are trained separately, followed by meta-policy training for coordinated multi-object collection. This approach demonstrates how quadruped legs can be effectively utilized for dynamic object manipulation, expanding their role beyond locomotion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
