无需预知物体信息，机器人通过强化学习学会推物到目标位置。
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
- 用约束强化学习训练机械臂和移动基座协同推物
- 仿真成功率91.35%，硬件实验至少80%成功
- 能自适应不同形状质量的未知物体，防止倾倒
非抓取式推动物体以实现定位与姿态调整是一种通用的移动操作技能。现实中物体物理属性及与地面摩擦存在显著不确定性，使移动操作器完成任务极具挑战。本文提出一种基于学习的控制器，使移动操作器通过一系列推动作将未知物体移动至目标位置与偏航姿态。该控制器通过约束强化学习（Constrained RL）方法训练机械臂与移动基座的运动策略。实验中在配备机械臂的四足机器人上验证了其能力。在仿真中成功率高达91.35%，在真实硬件上于复杂场景中仍保持不低于80%的成功率。大量硬件实验证明，该方法对不同质量、材质、尺寸与形状的未知物体具有高度鲁棒性。系统能实时发现最优推击点与方向，仅依赖物体位姿观测即可实现丰富的接触交互行为。此外，所学策略还展现出有效防止物体倾覆的自适应能力。
原文摘要 · Abstract (English)
Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floor contain significant uncertainties, which makes the task challenging for a mobile manipulator. In this paper, we develop a learning-based controller for a mobile manipulator to move an unknown object to a desired position and yaw orientation through a sequence of pushing actions. The proposed controller for the robotic arm and the mobile base motion is trained using a constrained Reinforcement Learning (RL) formulation. We demonstrate its capability in experiments with a quadrupedal robot equipped with an arm. The learned policy achieves a success rate of 91.35% in simulation and at least 80% on hardware in challenging scenarios. Through our extensive hardware experiments, we show that the approach demonstrates high robustness against unknown objects of different masses, materials, sizes, and shapes. It reactively discovers the pushing location and direction, thus achieving contact-rich behavior while observing only the pose of the object. Additionally, we demonstrate the adaptive behavior of the learned policy towards preventing the object from toppling.
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