Search Results for author:
Found 8 papers, 2 papers with code
Online Simultaneous Semi-Parametric Dynamics Model Learning
Accurate models of robots' dynamics are critical for control, stability, motion optimization, and interaction.
Adversarial Generation of Informative Trajectories for Dynamics System Identification
Dynamic System Identification approaches usually heavily rely on the evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determining the physical parameters of robot platforms.
Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation
Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning.
Robotics
A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots
In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality.
Robotics
Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing
In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model Predictive Controller (MPC).
Exploiting Spherical Projections To Generate Human-Like Wrist Pointing Movements
The mechanism behind the generation of human movements is of great interest in many fields (e. g. robotics and neuroscience) to improve therapies and technologies.
Robotics
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks
The co-adaptation of robot morphology and behaviour becomes increasingly important with the advent of fast 3D-manufacturing methods and efficient deep reinforcement learning algorithms.
Agile Maneuvers in Legged Robots: a Predictive Control Approach
To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.
