Throwing with a legged robot involves precise coordination of object manipulation and locomotion - crucial for advanced real-world interactions. Most research focuses on either manipulation or locomotion, with minimal exploration of tasks requiring both. This work investigates leveraging all available motors (full-body) over arm-only throwing in legged manipulators. We frame the task as a deep reinforcement learning (RL) objective, optimising throwing accuracy towards any user-commanded target destination and the robot's stability. Evaluations on a humanoid and an armed quadruped in simulation show that full-body throwing improves range, accuracy, and stability by exploiting body momentum, counter-balancing, and full-body dynamics. We introduce an optimised adaptive curriculum to balance throwing accuracy and stability, along with a tailored RL environment setup for efficient learning in sparse-reward conditions. Unlike prior work, our approach generalises to targets in 3D space. We transfer our learned controllers from simulation to a real humanoid platform.

翻译：利用腿式机器人进行投掷需要精确协调物体操控与移动能力——这对实现高级现实世界交互至关重要。现有研究大多集中于操控或移动的单一领域，对需要两者协同的任务探索甚少。本研究探讨了在腿式机械臂系统中，如何利用所有可用电机（全身协同）而非仅依赖机械臂进行投掷。我们将该任务构建为深度强化学习目标，通过优化投掷精度（指向用户指定的任意目标位置）与机器人稳定性来实现。在仿真环境中对人形机器人与武装四足机器人的评估表明：通过利用身体动量、反向平衡机制及全身动力学特性，全身协同投掷能显著提升投掷距离、精度与稳定性。我们提出了一种优化的自适应课程学习框架，以平衡投掷精度与稳定性要求，并设计了针对稀疏奖励条件下高效学习的强化学习环境配置。与现有研究不同，本方法可泛化至三维空间中的任意目标位置。最终，我们将仿真环境中训练得到的控制器成功迁移至真实人形机器人平台。