5 accepted papers
Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe exploration through the lens of epistemic uncertainty, where the actor’s sensitivity to parameter perturbations serves as a practical proxy for regions of h…
Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate…
In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated using uncertainty-based weights in the optimization process.…
Active localization consists of generating robot actions that allow it to maximally disambiguate its pose within a reference map. Traditional approaches use an information-theoretic criterion for action selection and hand-crafted perceptual models. In this work we propose an end-to-end differentiabl
Unmanned blimps are a safe and reliable alternative to conventional drones when flying above people. On-board real-time tracking of their pose and velocities is a necessary step toward autonomous navigation. There is a need for an easily deployable technology that is able to accurately and robustly