Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
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 …
Infogail: Interpretable imitation learning from visual demonstrations
The goal of imitation learning is to mimic expert behavior without access to an explicit
reward signal. Expert demonstrations provided by humans, however, often show significant …
reward signal. Expert demonstrations provided by humans, however, often show significant …
Multi-agent generative adversarial imitation learning
Imitation learning algorithms can be used to learn a policy from expert demonstrations
without access to a reward signal. However, most existing approaches are not applicable in …
without access to a reward signal. However, most existing approaches are not applicable in …
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …
Model-based inverse reinforcement learning from visual demonstrations
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks
with unknown dynamics remains an open problem. The key challenges lie in learning good …
with unknown dynamics remains an open problem. The key challenges lie in learning good …
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 …
Maximum-entropy multi-agent dynamic games: Forward and inverse solutions
In this article, we study the problem of multiple stochastic agents interacting in a dynamic
game scenario with continuous state and action spaces. We define a new notion of …
game scenario with continuous state and action spaces. We define a new notion of …
Pontryagin differentiable programming: An end-to-end learning and control framework
This paper develops a Pontryagin differentiable programming (PDP) methodology, which
establishes a unified framework to solve a broad class of learning and control tasks. The …
establishes a unified framework to solve a broad class of learning and control tasks. The …
A survey on teaching workplace skills to construction robots
The construction industry is seeking a robotic revolution to meet increasing demands for
productivity, quality, and safety. Typically, construction robots are usually pre-programmed …
productivity, quality, and safety. Typically, construction robots are usually pre-programmed …