A survey of imitation learning: Algorithms, recent developments, and challenges
M Zare, PM Kebria, A Khosravi… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …
A survey of inverse reinforcement learning: Challenges, methods and progress
Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …
Guided cost learning: Deep inverse optimal control via policy optimization
Reinforcement learning can acquire complex behaviors from high-level specifications.
However, defining a cost function that can be optimized effectively and encodes the correct …
However, defining a cost function that can be optimized effectively and encodes the correct …
A survey of inverse reinforcement learning
Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning
Autonomous vehicle (AV) software is typically composed of a pipeline of individual
components, linking sensor inputs to motor outputs. Erroneous component outputs …
components, linking sensor inputs to motor outputs. Erroneous component outputs …
Integrating Dijkstra's algorithm into deep inverse reinforcement learning for food delivery route planning
In China, rapid development of online food delivery brings massive orders, which relies
heavily on deliverymen riding e-bikes. In practice, actual delivery routes of most orders are …
heavily on deliverymen riding e-bikes. In practice, actual delivery routes of most orders are …
From inverse optimal control to inverse reinforcement learning: A historical review
Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in
control systems, robotics, Machine Learning (ML) and optimization taking into account the …
control systems, robotics, Machine Learning (ML) and optimization taking into account the …
Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demonstrations
Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal
organs due to the advantages of being real-time and radiation-free. However, due to inter …
organs due to the advantages of being real-time and radiation-free. However, due to inter …
Learning objective functions for manipulation
We present an approach to learning objective functions for robotic manipulation based on
inverse reinforcement learning. Our path integral inverse reinforcement learning algorithm …
inverse reinforcement learning. Our path integral inverse reinforcement learning algorithm …