发表机构
EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出多物体多目标投掷的统一规划框架，通过两阶段方法快速生成可行投掷，显著减少执行时间，并在仿真和真实实验中验证了其有效性和可扩展性。
AI 中文摘要
机器人投掷已成为一种有前景的技术，通过扩大工作空间和加速流程来提高物流和仓库自动化的效率。为了显著提高投掷系统的吞吐量，我们开发了一次挥动中投掷多个物体的策略。这种多物体多目标投掷（MOMT）利用了拟人手的自由度大的特点。关键在于快速生成快速且可行的投掷运动，这涉及短轨迹持续时间与短规划时间之间的复杂权衡。我们分两个阶段解决这个问题。离线时，我们通过结合物体的倒飞动力学和机器人的运动学与动力学来构建可行集模型。在线时，我们通过快速解匹配和过滤物体的有效分离状态以及机器人的可行状态来生成可行的投掷，这些状态可以在不到5毫秒内组成投掷序列。我们在配备多指手的7自由度机械臂上验证了该框架。在仿真中，协调的双物体投掷相比独立的单物体规划将执行时间减少了高达46%，并且当扩展到三个物体时，这种改进得以保持。使用两个物体的真实世界实验确认了29%的减少；与理论上的50%之间的剩余差距归因于投掷间过渡开销。当目标位置在执行过程中随机改变时，系统会重新规划并在100毫秒延迟内成功到达新目标，而无需停止机器人。这些结果建立了首个MOMT投掷的统一规划框架——展示了在仿真中扩展到多个物体的能力以及在双物体任务中的真实世界可行性——推进了高吞吐量机器人操作的前沿。一个总结方法和硬件实验的视频可在以下https URL获取。
英文摘要
Robot throwing has emerged as a promising technique for improving efficiency in logistics and warehouse automation, by enlarging the workspace and speeding up the process. To significantly increase the throwing system's throughput, we develop strategies for throwing multiple objects in one swipe. Such multi-object multi-target throwing (MOMT) leverages the large degrees of freedom of anthropomorphic hands. The key is to quickly generate fast and feasible throwing motions, which involves a complex trade-off between short trajectory duration and short planning time. We solve this problem in two stages. Offline, we build a model of the feasible set by combining object's inverted flying dynamics and the robot's kinematics and dynamics. Online, we generate feasible throws through fast solution matching and filtering of object's valid detach state and robot's feasible state that can compose sequences of throws in less than 5 ms. We validate the framework on a 7-DoF manipulator equipped with a multi-fingered hand. In simulation, coordinated two-object throwing reduces execution time by up to 46% compared to independent single-object planning, and this improvement is maintained when scaling to three objects. Real-world experiments with two objects confirm a 29% reduction; the remaining gap to the theoretical 50% is attributed to inter-throw transition overhead. When target positions are randomly changed mid-execution, the system re-plans and successfully reaches the new targets within 100 ms latency without stopping the robot. These results establish the first unified planning framework for MOMT throwing -- demonstrating scalability to multiple objects in simulation and real-world feasibility on two-object tasks -- advancing the frontier of high-throughput robotic manipulation. A video summarizing the method and the hardware experiments is available at https://liuyangdh.github.io/momt-video
CommentsAccepted to IROS 2026. 8 pages, 9 figures. Zhengming Zhu and Yang Liu contributed equally. Video Summary: https://liuyangdh.github.io/momt-video
