Results 21 to 30 of about 277,892 (265)
Spacecraft Rendezvous Guidance Method Based on Safe Reinforcement Learning [PDF]
Jisuanji kexue, 2023 With the increasing complexity of spacecraft rendezvous and docking tasks,the requirements for its efficiency,autonomy and reliability are highly demanded.In recent years,the introduction of reinforcement learning technology to solve the problem of ...
XING Linquan, XIAO Yingmin, YANG Zhibin, WEI Zhengmin, ZHOU Yong, GAO Saijun
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Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
CoRR, 2021 Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing safe reinforcement learning on highly nonlinear and high-dimensional dynamical systems, one possible approach is to find a low-dimensional safe region via data-driven feature ...
Zhehua Zhou +4 more
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Modeling Task Uncertainty for Safe Meta-Imitation Learning
Frontiers in Robotics and AI, 2020 To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach.
Tatsuya Matsushima +5 more
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Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation
Current Directions in Biomedical Engineering, 2022 Cardiovascular diseases are the main cause of death worldwide. State-of-the-art treatment often includes the process of navigating endovascular instruments through the vasculature.
Ritter Jacqueline +5 more
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Robust and Safe Autonomous Navigation for Systems With Learned SE(3) Hamiltonian Dynamics
IEEE Open Journal of Control Systems, 2022 Stability and safety are critical properties for successful deployment of automatic control systems. As a motivating example, consider autonomous mobile robot navigation in a complex environment. A control design that generalizes to different operational
Zhichao Li, Thai Duong, Nikolay Atanasov
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Safe RLHF: Safe Reinforcement Learning from Human Feedback
CoRR, 2023 With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training.
Josef Dai +7 more
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Adaptive Safe Semi-Supervised Extreme Machine Learning
IEEE Access, 2019 Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples.
Jun Ma, Chao Yuan
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Safe Motion Planning and Learning for Unmanned Aerial Systems
Aerospace, 2022 To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems.
Baris Eren Perk, Gokhan Inalhan
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Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs
Drones, 2023 Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications.
Yanbo Fu, Wenjie Zhao, Liu Liu
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Revisiting Safe Exploration in Safe Reinforcement learning
CoRRSafe 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 limit. However, this metric fails to distinguish how costs accrue, treating infrequent severe cost events as equal to frequent mild ...
David Eckel +2 more
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