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Sylvain Calinon

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Publications associées (85)

Tensor train for global optimization problems in robotics

Sylvain Calinon, Suhan Narayana Shetty, Tobias Löw

The convergence of many numerical optimization techniques is highly dependent on the initial guess given to the solver. To address this issue, we propose a novel approach that utilizes tensor methods to initialize existing optimization solvers near global ...

Sage Publications Ltd2023

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Sylvain Calinon, Suhan Narayana Shetty

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

drozBot: Using Ergodic Control to Draw Portraits

Sylvain Calinon

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Learning to Guide Online Multi-Contact Receding Horizon Planning

Sylvain Calinon, Teguh Santoso Lembono

In Receding Horizon Planning (RHP), it is critical that the motion being executed facilitates the completion of the task, e.g. building momentum to overcome large obstacles. This requires a value function to inform the desirability of robot states. However ...

IEEE2022

Probabilistic Adaptive Control for Robust Behavior Imitation

Sylvain Calinon, Hakan Girgin, Julius Maximilian Jankowski

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling of demonstrated skills. To reproduce a learned skill with a real robot, a feedback controller ...

IEEE2021

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Sylvain Calinon, Teguh Santoso Lembono, Amirreza Razmjoo Fard

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

IEEE2021

Geometry-aware Manipulability Learning, Tracking and Transfer

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design ...

Sage2021

Probabilistic Iterative LQR for Short Time Horizon MPC

Sylvain Calinon, Teguh Santoso Lembono

Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another ...

IEEE2021

Learning Optimal Impedance Control During Complex 3D Arm Movements

Sylvain Calinon

In daily life, humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to unstable dynamics of the external environments. The underlyin ...

IEEE2021

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

2021

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Emmanuel Pignat, Teguh Santoso Lembono, Julius Maximilian Jankowski

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

Optimization of robot configurations for motion planning in industrial riveting

Sylvain Calinon, Hakan Girgin, Teguh Santoso Lembono

Collaborative robots (cobots) have been increasingly used in the industry in recent years. The cobots are often 7-axis kinematically redundant manipulators. Exploiting these redundancies in industrial tasks is still a challenge as the change in the robot j ...

IEEE2021

Active Learning of Bayesian Probabilistic Movement Primitives

Sylvain Calinon, Jean-Marc Odobez, Hakan Girgin, Thibaut Antoine Kulak

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distribu ...

IEEE2021

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Sylvain Calinon, Rohan Budhiraja, Teguh Santoso Lembono

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balanc ...

IEEE2021

A Laser-based Dual-arm System for Precise Control of Collaborative Robots

Sylvain Calinon

IEEE2021

Fourier movement primitives: an approach for learning rhythmic robot skills from demonstrations

Sylvain Calinon, Thibaut Antoine Kulak

Whether in factory or household scenarios, rhythmic movements play a crucial role in many daily-life tasks. In this paper we propose a Fourier movement primitive (FMP) representation to learn such type of skills from human demonstrations. Our approach take ...

2020

Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion

Sylvain Calinon, Teguh Santoso Lembono

In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consisten ...

IEEE2020

Learning, Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction

Sylvain Calinon

This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective non-verbal communication through a probabilistic formulation using joint angle data from human dem ...

ACM2020

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Sylvain Calinon, Noémie Laure Gwendoline Jaquier, Emmanuel Pignat, Hakan Girgin

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this pa ...

IEEE2020

Analysis and Transfer of Human Movement Manipulability in Industry-like Activities

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, humans are able to plan their limbs motion in anticipation of the execution of ...

IEEE2020

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