Incorporating a robotic manipulator into a wheel-legged robot enhances its agility and expands its potential for practical applications. However, the presence of potential instability and uncertainties presents additional challenges for control objectives. In this paper, we introduce an arm-constrained curriculum learning architecture to tackle the issues introduced by adding the manipulator. Firstly, we develop an arm-constrained reinforcement learning algorithm to ensure safety and stability in control performance. Additionally, to address discrepancies in reward settings between the arm and the base, we propose a reward-aware curriculum learning method. The policy is first trained in Isaac gym and transferred to the physical robot to do dynamic grasping tasks, including the door-opening task, fan-twitching task and the relay-baton-picking and following task. The results demonstrate that our proposed approach effectively controls the arm-equipped wheel-legged robot to master dynamic grasping skills, allowing it to chase and catch a moving object while in motion. The code can be found at https://github.com/aCodeDog/legged-robots-manipulation. To view the supplemental video, please visit https://youtu.be/sNXT-rwPNMM.

翻译：将机械臂集成至轮腿机器人可增强其灵活性并拓展实际应用潜力，但潜在的不稳定性与不确定性给控制目标带来了额外挑战。本文提出一种手臂约束课程学习架构，以解决引入机械臂所带来的问题。首先，我们开发了一个手臂约束的强化学习算法，确保控制性能的安全性与稳定性。此外，为应对机械臂与基座之间奖励设定的差异，我们提出了一种奖励感知的课程学习方法。该策略先在Isaac Gym中训练，随后迁移至实体机器人完成动态抓取任务，包括开门任务、风扇拨动任务以及接力棒拾取与跟随任务。实验结果表明，我们的方法能有效控制配备机械臂的轮腿机器人掌握动态抓取技能，使其在运动过程中能够追逐并抓取移动物体。代码见https://github.com/aCodeDog/legged-robots-manipulation，补充视频请访问https://youtu.be/sNXT-rwPNMM。