这篇论文教机器人怎么在乱堆东西的桌面上把物体扔进篮筐，用势场表示结合SAC算法，真实实验成功率90%，比TossingBot强不少。
该论文研究机器人在多障碍物环境中安全投掷物体到目标篮筐的问题。现有方法如TossingBot假设无障碍环境，而本工作引入势场状态表示（PFR），以固定网格编码篮筐吸引和障碍排斥，使强化学习策略能泛化到任意障碍数量与配置。在仿真中使用SAC、DDPG、TD3三种算法训练，其中SAC表现最稳定。真实实验中，对未见过的可投掷物体取得了高达90%的成功率，验证了PFR的鲁棒性和实用性。
Learning to Throw Objects Safely in Multi-Obstacle Environments
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