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Person# Sylvain Calinon

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Courses taught by this person (1)

EE-613: Machine Learning for Engineers

The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.

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Robot

A robot is a machine—especially one programmable by a computer—capable of carrying out a complex series of actions automatically. A robot can be guided by an external control device, or the contro

Robotics

Robotics is an interdisciplinary branch of electronics and communication, computer science and engineering. Robotics involves the design, construction, operation, and use of robots. The goal of rob

Learning

Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; th

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Many problems in robotics are fundamentally problems of geometry, which have led to an increased research effort in geometric methods for robotics in recent years. The results were algorithms using the various frameworks of screw theory, Lie algebra, and dual quaternions. A unification and generalization of these popular formalisms can be found in geometric algebra. The aim of this article is to showcase the capabilities of geometric algebra when applied to robot manipulation tasks. In particular, the modeling of cost functions for optimal control can be done uniformly across different geometric primitives leading to a low symbolic complexity of the resulting expressions and a geometric intuitiveness. We demonstrate the usefulness, simplicity, and computational efficiency of geometric algebra in several experiments using a Franka Emika robot. The presented algorithms were implemented in c++20 and resulted in the publicly available library gafro. The benchmark shows faster computation of the kinematics than state-of-the-art robotics libraries.

Sylvain Calinon, Amirreza Razmjoo Fard, Jie Zhao

Daily manipulation tasks are characterized by regular features associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Only using Cartesian coordinate systems cannot fully represent such geometric descriptors. In this article, we consider other candidate coordinate systems and propose a learning approach to extract the optimal representation of an observed movement/behavior from these coordinates. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyze a small set of user demonstrations statistically represented in different coordinate systems. We formulate the skill generalization as a general optimal control problem based on the (iterative) linear quadratic regulator ((i)LQR), where the Gaussian distribution in the proper coordinate systems is used to define the cost function. We apply our approach to object grasping and box-opening tasks in simulation and on a 7-axis Franka Emika robot using open-loop and feedback control, where precision matrices result in the automatic determination of feedback gains for the controller from very few demonstrations represented in multiple coordinate systems. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations. The results show high variation along the do-not-matter direction, while maintaining the invariant characteristics of the task in the coordinate system(s) of interest. We then tested the approach in a human-robot shared control task. Results show that the robot can modify its grasping strategy based on the geometry of the object that the user decides to grasp.

We present drozBot: le robot portraitiste, a robotic system that draws artistic portraits of people. The input images for the portrait are taken interactively by the robot itself. We formulate the problem of drawing portraits as a problem of coverage which is then solved by an ergodic control algorithm to compute the strokes. The ergodic computation of the strokes for the portrait gives an artistic look to them. The specific ergodic control algorithm that we chose is inspired by the heat equation. We employed a 7-axis Franka Emika robot for the physical drawings and used an optimal control strategy to generate joint angle commands. We explain the influence of the different hyperparameters and show the importance of the image processing steps. The attractiveness of the results was evaluated by conducting a survey where we asked the participants to rank the portraits produced by different algorithms.