Papers Safe Exploration
“Safe Exploration” 태그가 달린 논문 135편 · 필터 해제
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Autonomous driving promises significant advancements in mobility, road safety and traffic efficiency, yet reinforcement learning and imitation learning face safe-exploration and distribution-shift challenges. Although hu…
Autonomous DrivingImitation LearningSafe ExplorationSelf-LearningSLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators. While reinforcement learning (RL) holds promise for autonomo…
DisentanglementIndustrial RobotsReinforcement Learning (RL)Safe ExplorationBresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-the-art safe RL approaches mitigate risk…
Reinforcement Learning (RL)Safe ExplorationSafe Reinforcement LearningSafe exploration in reproducing kernel Hilbert spaces
Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded no…
Bayesian OptimizationSafe ExplorationSafety Representations for Safer Policy Learning
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 …
Safe ExplorationLearning to explore when mistakes are not allowed
Goal-Conditioned Reinforcement Learning (GCRL) provides a versatile framework for developing unified controllers capable of handling wide ranges of tasks, exploring environments, and adapting behaviors. However, its reli…
Safe ExplorationSafe Reinforcement LearningConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to achieve robust performance due to limite…
Q-LearningSafe ExplorationVision-Language-ActionLearning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults
Merely pursuing performance may adversely affect the safety, while a conservative policy for safe exploration will degrade the performance. How to balance the safety and performance in learning-based control problems is …
Reinforcement Learning (RL)Safe ExplorationSafe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid con…
Bayesian OptimizationHumanoid ControlSafe ExplorationActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively.…
Efficient Explorationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty
The deep reinforcement learning (DRL) based Volt-VAR optimization (VVO) methods have been widely studied for active distribution networks (ADNs). However, most of them lack safety guarantees in terms of power injection u…
Conformal PredictionDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
Safety is one of the key issues preventing the deployment of reinforcement learning techniques in real-world robots. While most approaches in the Safe Reinforcement Learning area do not require prior knowledge of constra…
reinforcement-learningReinforcement LearningSafe ExplorationSafe Reinforcement LearningRevisiting Safe Exploration in Safe Reinforcement learning
Safe reinforcement learning (SafeRL) extends standard reinforcement learning with the idea of safety, where safety is typically defined through the constraint of the expected cost return of a trajectory being below a set…
Benchmarkingreinforcement-learningReinforcement LearningSafe Exploration+1A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the safety-perfo…
Autonomous DrivingEvolutionary AlgorithmsQuantizationSafe ExplorationMeta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning
Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In safe RL, the goal is to maximize reward p…
Safe ExplorationSafe Reinforcement LearningA Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments
Training a model-free reinforcement learning agent requires allowing the agent to sufficiently explore the environment to search for an optimal policy. In safety-constrained environments, utilizing unsupervised explorati…
Binary ClassificationSafe ExplorationExterior Penalty Policy Optimization with Penalty Metric Network under Constraints
In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been studied as an effective approach for han…
Safe ExplorationTransductive Active Learning with Application to Safe Bayesian Optimization
Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization…
Active LearningBayesian OptimizationPredictionReinforcement Learning (RL)+3Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
With the advancement of autonomous driving, ensuring safety during motion planning and navigation is becoming more and more important. However, most end-to-end planning methods suffer from a lack of safety. This research…
Autonomous DrivingDecision MakingMotion PlanningSafe ExplorationHighway Value Iteration Networks
Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable "planning module" that approximates the value iteration algorithm. However, long-term planning remains a challen…
DiversitySafe Exploration