Vitchyr Pong

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Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate(More)
In our first model, individuals have opinions in [0, 1]d. Connections are broken at rate proportional to their length `, and a randomly chosen end point x connects to an individual chosen at random. If version (i) the new edge is always accepted. In version (ii) a new connection of length `′ is accepted with probability min{`/`′, 1}. Our second model is a(More)
In this work, we take a step towards bridging the gap between the theory of formal synthesis and its application to real-world, complex, robotic systems. In particular, we present an end-to-end approach for the automatic generation of code that implements high-level robot behaviors in a verifiably correct manner, including reaction to the possible failures(More)
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