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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:
