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 …
A concise guide to modelling the physics of embodied intelligence in soft robotics
Embodied intelligence (intelligence that requires and leverages a physical body) is a well-
known paradigm in soft robotics, but its mathematical description and consequent …
known paradigm in soft robotics, but its mathematical description and consequent …
Neural-fly enables rapid learning for agile flight in strong winds
Executing safe and precise flight maneuvers in dynamic high-speed winds is important for
the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the …
the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the …
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 …
Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
Robotic simulators are crucial for academic research and education as well as the
development of safety-critical applications. Reinforcement learning environments—simple …
development of safety-critical applications. Reinforcement learning environments—simple …
Review of advanced guidance and control algorithms for space/aerospace vehicles
The design of advanced guidance and control (G&C) systems for space/aerospace vehicles
has received a large amount of attention worldwide during the last few decades and will …
has received a large amount of attention worldwide during the last few decades and will …
[HTML][HTML] Integrating machine learning with human knowledge
Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …
However, achieving high accuracy requires a large amount of data that is sometimes …
When physics meets machine learning: A survey of physics-informed machine learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …
of physics, which is the high level abstraction of natural phenomenons and human …