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Found 9 papers, 0 papers with code
Deep Learning based acoustic measurement approach for robotic applications on orthopedics
Therefore, we could label bone peaks in the raw US signals.
Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groups
The optimal control problem is phrased as an optimization of a neural ODE on the Lie group SE(3), and the controller is iteratively optimized.
On the use of energy tanks for robotic systems
In this document we describe and discuss energy tanks, a control algorithm which has gained popularity inside the robotics and control community over the last years.
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipeline plants.
Autonomous Navigation
Hierarchical Reinforcement Learning
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Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part II. Compressible and Incompressible Flow
Starting from the group of diffeomorphisms as a configuration space for the fluid, the Stokes Dirac structure is derived by Poisson reduction and then augmented by boundary ports and distributed ports.
Fluid Dynamics Mathematical Physics Differential Geometry Mathematical Physics
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part I. Foundations and Kinetic Energy
In this two-parts paper, we present a systematic procedure to extend the known Hamiltonian model of ideal inviscid fluid flow on Riemannian manifolds in terms of Lie-Poisson structures to a port-Hamiltonian model in terms of Stokes-Dirac structures.
Differential Geometry Mathematical Physics Mathematical Physics Fluid Dynamics
Low Dimensional State Representation Learning with Reward-shaped Priors
We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space.
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information.
