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A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
A review of safe reinforcement learning: Methods, theories and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
[HTML][HTML] A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …
require varying contact with the environment, such that the robot must either implicitly or …
Safe control under input limits with neural control barrier functions
We propose new methods to synthesize control barrier function (CBF) based safe controllers
that avoid input saturation, which can cause safety violations. In particular, our method is …
that avoid input saturation, which can cause safety violations. In particular, our method is …
Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents
perform a significant number of random exploratory steps. In the real world, this can limit the …
perform a significant number of random exploratory steps. In the real world, this can limit the …
The exact sample complexity gain from invariances for kernel regression
In practice, encoding invariances into models improves sample complexity. In this work, we
study this phenomenon from a theoretical perspective. In particular, we provide minimax …
study this phenomenon from a theoretical perspective. In particular, we provide minimax …
Safe reinforcement learning using black-box reachability analysis
Reinforcement learning (RL) is capable of sophisticated motion planning and control for
robots in uncertain environments. However, state-of-the-art deep RL approaches typically …
robots in uncertain environments. However, state-of-the-art deep RL approaches typically …
Fast kinodynamic planning on the constraint manifold with deep neural networks
Motion planning is a mature area of research in robotics with many well-established
methods based on optimization or sampling the state space, suitable for solving kinematic …
methods based on optimization or sampling the state space, suitable for solving kinematic …
Regularized deep signed distance fields for reactive motion generation
Autonomous robots should operate in real-world dynamic environments and collaborate
with humans in tight spaces. A key component for allowing robots to leave structured lab and …
with humans in tight spaces. A key component for allowing robots to leave structured lab and …
Triple-q: A model-free algorithm for constrained reinforcement learning with sublinear regret and zero constraint violation
This paper presents the first model-free, simulator-free reinforcement learning algorithm for
Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint …
Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint …