2021
DOI: 10.48550/arxiv.2105.05019
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Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs

Manash Pratim Das,
Anirudh Vemula,
Mayank Pathak
et al.

Abstract: Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal de… Show more

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