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Found 6 papers, 2 papers with code
RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution.
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated.
Causal Explanations for Sequential Decision Making Under Uncertainty
We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reasoning.
Laser2Vec: Similarity-based Retrieval for Robotic Perception Data
As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary.
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process.
Human-in-the-Loop SLAM
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users.
Human-Computer Interaction Robotics
