Fast Planning for Multi-object Multi-target Throwing

2026-10-06

AI Summary

This study addresses the challenge of computationally expensive trajectory planning in multi-object, multi-target (MOMT) throwing by proposing the first unified MOMT planning framework. The method integrates reverse-flight dynamics, robot kinematics, and state filtering algorithms to construct an offline feasible-set model coupled with a rapid online matching mechanism. This approach achieves coordinated multi-object throwing planning within 5 milliseconds, effectively balancing trajectory duration against computational efficiency while supporting dynamic replanning. Experimental results demonstrate that execution times are reduced by 46% in simulation and 29% on physical hardware, with dynamic target replanning completed within 100 milliseconds.

Abstract

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

Problem

• multi-object multi-target throwing
• fast motion planning
• robotic manipulation
• throughput optimization
• trajectory generation

Innovation

• Multi-object multi-target throwing
• Fast motion planning
• Inverted flying dynamics
• Anthropomorphic hand
• High-throughput manipulation