16,160 research outputs found
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
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
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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