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Barriernet: Differentiable control barrier functions for learning of safe robot control
W ** for reinforcement learning
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-
world challenges with large state spaces. While RL excels in these tasks, training time …
world challenges with large state spaces. While RL excels in these tasks, training time …
Formal control synthesis for stochastic neural network dynamic models
Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control
systems with complicated physics or black-box components. Due to complexity of NNs …
systems with complicated physics or black-box components. Due to complexity of NNs …
Formally verified neural network control barrier certificates for unknown systems
This paper is concerned with the controller synthesis problem for discrete-time unknown
systems against safety specifications via control barrier certificates. Typically, control barrier …
systems against safety specifications via control barrier certificates. Typically, control barrier …
Zero-shot policy transfer in autonomous racing: reinforcement learning vs imitation learning
There are few technologies that hold as much promise in achieving safe, accessible, and
convenient transportation as autonomous vehicles. However, as recent years have …
convenient transportation as autonomous vehicles. However, as recent years have …
Differentiable control barrier functions for vision-based end-to-end autonomous driving
Guaranteeing safety of perception-based learning systems is challenging due to the
absence of ground-truth state information unlike in state-aware control scenarios. In this …
absence of ground-truth state information unlike in state-aware control scenarios. In this …