76 citations · 237 across the 17 of their papers we have counts for
19 papers
Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor
Pietro Griffa, Carmelo Sferrazza, Raffaello D'Andrea
Grasping objects whose physical properties are unknown is still a great challenge in robotics. Most solutions rely entirely on visual data to plan the best grasping strategy. Howev…
Offset-free Model Predictive Control: A Ball Catching Application with a Spherical Soft Robotic Arm
Yaohui Huang, Matthias Hofer, Raffaello D'Andrea
This paper presents an offset-free model predictive controller for fast and accurate control of a spherical soft robotic arm. In this control scheme, a linear model is combined wit…
Distributed Estimation, Control and Coordination of Quadcopter Swarm Robots
Zheng Jia, Michael Hamer, Raffaello D'Andrea
In this thesis we are interested in applying distributed estimation, control and optimization techniques to enable a group of quadcopters to fly through openings. The quadcopters a…
Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation
Thomas Bi, Carmelo Sferrazza, Raffaello D'Andrea
This paper aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the…
A Vision-based Sensing Approach for a Spherical Soft Robotic Arm
Matthias Hofer, Carmelo Sferrazza, Raffaello D'Andrea
Sensory feedback is essential for the control of soft robotic systems and to enable deployment in a variety of different tasks. Proprioception refers to sensing the robot's own sta…
Sim-to-real for high-resolution optical tactile sensing: From images to 3D contact force distributions
Carmelo Sferrazza, Raffaello D'Andrea
The images captured by vision-based tactile sensors carry information about high-resolution tactile fields, such as the distribution of the contact forces applied to their soft sen…
A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm
Jasan Zughaibi, Matthias Hofer, Raffaello D'Andrea
This paper presents the application of a learning control approach for the realization of a fast and reliable pick-and-place application with a spherical soft robotic arm. The arm…
Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
Carmelo Sferrazza, Thomas Bi, Raffaello D'Andrea
Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by conce…
Towards vision-based robotic skins: a data-driven, multi-camera tactile sensor
Camill Trueeb, Carmelo Sferrazza, Raffaello D'Andrea
This paper describes the design of a multi-camera optical tactile sensor that provides information about the contact force distribution applied to its soft surface. This informatio…
Vision-Based Proprioceptive Sensing for Soft Inflatable Actuators
Peter Werner, Matthias Hofer, Carmelo Sferrazza +1
This paper presents a vision-based sensing approach for a soft linear actuator, which is equipped with an integrated camera. The proposed vision-based sensing pipeline predicts the…
Ground truth force distribution for learning-based tactile sensing: a finite element approach
Carmelo Sferrazza, Adam Wahlsten, Camill Trueeb +1
Skin-like tactile sensors provide robots with rich feedback related to the force distribution applied to their soft surface. The complexity of interpreting raw tactile information…
A Method for Reducing the Complexity of Model Predictive Control in Robotics Applications
Michael Muehlebach, Raffaello D'Andrea
This article describes an approach for parametrizing input and state trajectories in model predictive control. The parametrization is designed to be invariant to time shifts, which…
Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm
Matthias Hofer, Lukas Spannagl, Raffaello D'Andrea
This paper presents the application of an iterative learning control scheme to improve the position tracking performance for an articulated soft robotic arm during aggressive maneu…
Transfer learning for vision-based tactile sensing
Carmelo Sferrazza, Raffaello D'Andrea
Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent ad…
On the Approximation of Constrained Linear Quadratic Regulator Problems and their Application to Model Predictive Control - Supplementary Notes
Michael Muehlebach, Raffaello D'Andrea
By parametrizing input and state trajectories with basis functions different approximations to the constrained linear quadratic regulator problem are obtained. These notes present…
Approximation of Continuous-Time Infinite-Horizon Optimal Control Problems Arising in Model Predictive Control - Supplementary Notes
Michael Muehlebach, Raffaello D'Andrea
These notes present preliminary results regarding two different approximations of linear infinite-horizon optimal control problems arising in model predictive control. Input and st…
Iterative MILP Methods for Vehicle Control Problems
Matthew Earl, Raffaello D'Andrea
Mixed integer linear programming (MILP) is a powerful tool for planning and control problems because of its modeling capability and the availability of good solvers. However, for l…
A Decomposition Approach to Multi-Vehicle Cooperative Control
Matthew Earl, Raffaello D'Andrea
We present methods that generate cooperative strategies for multi-vehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team…
Multi-Vehicle Cooperative Control Using Mixed Integer Linear Programming
Matthew G. Earl, Raffaello D'Andrea
We present methods to synthesize cooperative strategies for multi-vehicle control problems using mixed integer linear programming. Complex multi-vehicle control problems are expres…