让机器人在有障碍物的环境中安全投掷物体，成功率高达90%。
Learning to Throw Objects Safely in Multi-Obstacle Environments
- 用势场网格编码目标吸引和障碍排斥，实现复杂场景泛化。
- 强化学习策略在仿真中表现稳定，SAC算法效果最佳。
- 真实机器人实验验证了从仿真到现实的可靠迁移能力。
机器人投掷可实现快速高效地将物体放置于工作区之外，但复杂环境下的可靠投掷仍研究不足。现有方法如TossingBot依赖视觉输入学习投掷策略，但假设无障碍物环境。本文解决在随机布置障碍物的场景中将物体投入目标篮筐的问题。提出一种势场状态表示法，在固定尺寸网格上紧凑编码篮筐吸引力与障碍物排斥力，使强化学习策略能泛化至任意数量和布局的障碍物。策略通过运动学示范初始化，并在仿真中使用三种先进强化学习算法（SAC、DDPG、TD3）优化，其中SAC在各类场景中表现最一致。对比显式状态编码，该方法显著提升成功率并更好扩展至未见障碍配置。真实机器人实验使用未见过的投掷物，验证了良好的仿真到现实迁移能力，杂乱场景下成功率最高达90%。结果表明，势场表示法为非结构化环境中安全高效的机器人投掷提供了实用且鲁棒的解决方案。
原文摘要 · Abstract (English)
Robotic throwing enables fast and efficient object placement beyond the robot's immediate workspace, but reliable throwing in cluttered environments remains underexplored. Existing approaches, such as TossingBot, learn throwing strategies from visual input but assume obstacle-free settings. In this paper, we address the problem of throwing objects into a target basket while avoiding obstacles placed randomly in the scene. We introduce a potential field state representation that compactly encodes both basket attraction and obstacle repulsion on a fixed-size grid, enabling reinforcement learning (RL) policies to generalize across arbitrary numbers and configurations of obstacles. The policy is initialized from kinesthetic demonstrations and optimized in simulation using three state-of-the-art RL algorithms (SAC, DDPG, TD3). Among these, SAC achieves the most consistent performance across scenarios. We compare the potential field representation against explicit state encodings and demonstrate that it achieves higher success rates and better scalability to unseen obstacle configurations. Real-robot experiments with unseen throwable objects confirm robust sim-to-real transfer, achieving up to $90\%$ success in cluttered scenes. These results demonstrate that PFR provides a practical and robust representation for safe and efficient robotic throwing in unstructured environments. A video showcasing our experiments is available at: https://youtu.be/ZZnJf8ua2dE
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