Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Learning for safety-critical control with control barrier functions
Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …
safety, and have been deployed successfully in multiple domains. Despite this success …
Safe nonlinear control using robust neural lyapunov-barrier functions
Safety and stability are common requirements for robotic control systems; however,
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …
Reinforcement learning for safety-critical control under model uncertainty, using control lyapunov functions and control barrier functions
In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-
driven approach. For this purpose, we utilize the structure of an input-ouput linearization …
driven approach. For this purpose, we utilize the structure of an input-ouput linearization …
Learning stable deep dynamics models
JZ Kolter, G Manek - Advances in neural information …, 2019 - proceedings.neurips.cc
Deep networks are commonly used to model dynamical systems, predicting how the state of
a system will evolve over time (either autonomously or in response to control inputs) …
a system will evolve over time (either autonomously or in response to control inputs) …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Learning certified control using contraction metric
In this paper, we solve the problem of finding a certified control policy that drives a robot from
any given initial state and under any bounded disturbance to the desired reference …
any given initial state and under any bounded disturbance to the desired reference …
Neural-swarm2: Planning and control of heterogeneous multirotor swarms using learned interactions
We present Neural-Swarm2, a learning-based method for motion planning and control that
allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation …
allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation …