Add to ORKGReinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19
In recent years, learning models from data has become an increasingly interesting tool for robotics,...
Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this wi...
This paper presents a learning model for obtaining global inverse statics solutions for redundant so...
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accu...
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accu...
It is well-known that inverse dynamics models can improve tracking performance in robot control. The...
It is well-known that inverse dynamics models can improve tracking performance in robot control. The...
Recently the use of neural networks as the inverse-kinematics model of a robot arm has been proposed...
Model learning is a crucial aspect of robotics as it enables the use of traditional and consolidated...
Soft robots that are built from materials with mechanical properties similar to those of living tiss...
Soft robots have the potential to significantly change the way that robots interact with the environ...
While it is well-known that model can enhance the control performance in terms of precision or energ...
As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic ...
Recently the use of neural networks as the inverse-kinematics model of a robot arm has been proposed...
Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major is...
In recent years, learning models from data has become an increasingly interesting tool for robotics,...
Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this wi...
This paper presents a learning model for obtaining global inverse statics solutions for redundant so...
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accu...
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accu...
It is well-known that inverse dynamics models can improve tracking performance in robot control. The...
It is well-known that inverse dynamics models can improve tracking performance in robot control. The...
Recently the use of neural networks as the inverse-kinematics model of a robot arm has been proposed...
Model learning is a crucial aspect of robotics as it enables the use of traditional and consolidated...
Soft robots that are built from materials with mechanical properties similar to those of living tiss...
Soft robots have the potential to significantly change the way that robots interact with the environ...
While it is well-known that model can enhance the control performance in terms of precision or energ...
As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic ...
Recently the use of neural networks as the inverse-kinematics model of a robot arm has been proposed...
Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major is...
In recent years, learning models from data has become an increasingly interesting tool for robotics,...
Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this wi...
This paper presents a learning model for obtaining global inverse statics solutions for redundant so...