16,160 research outputs found

    Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

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    We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks

    Deriving Quests from Open World Mechanics

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    Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they could be generated organically from the gameplay itself? The goal of our project was to develop a model of the mechanics in Minecraft that could be used to determine the ideal placement of objectives in an open world setting. We formalized the game logic of Minecraft in terms of logical rules that can be manipulated in two ways: they may be executed to generate graphs representative of the player experience when playing an open world game with little developer direction; and they may be statically analyzed to determine dependency orderings, feedback loops, and bottlenecks. These analyses may then be used to place achievements on gameplay actions algorithmically.Comment: To appear at Foundations of Digital Games (FDG) 201
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