Dynamic Throwing with Robotic Material Handling Machines
Automation of hydraulic material handling machinery is limited to semi-static pick-and-place cycles. This work uses Reinforcement Learning (RL) to design dynamic controllers for material handlers with underactuated arms as commonly used in logistics. Tested both in simulation and in real-world experiments on a 12-ton test platform, the controllers exploit passive joints for dynamic throws, accurately targeting objects beyond static reach.
Paper Link: https://arxiv.org/abs/2405.19001