SOTAVerified

Safe Exploration

Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Papers

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Showing 21–30 of 135 papers

TitleStatusHype
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
A Safe Semi-supervised Graph Convolution Network—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Data Efficient Reinforcement Learning for Legged Robots—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
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