Publication

An Optimal Control Formulation of Tool Affordance Applied to Impact Tasks

Related publications (28)

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

In this paper, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances ...

2022

3PAC: A Plug-and-Play System for Distributed Power Sharing and Communication in Modular Robots

Jamie Paik, Kevin Andrew Holdcroft, Christoph Heinrich Belke

In modular robotics, there is classically a compromise between coupling complexity and functionality. The mechanical and electrical designs are linked, such that adding electrical features often requires redesigning the mechanical components. However, incr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Navigation of Concentric Tube Continuum Robots using Optimal Control

John Maddocks, Siva Prasad Chakri Dhanakoti

Recently developed Concentric Tube Continuum Robots (CTCRs) are widely exploited in, for example in minimally invasive surgeries which involve navigating inside narrow body cavities close to sensitive regions. These CTCRs can be controlled by extending and ...

SCITEPRESS2022

VIO-UWB-Based Collaborative Localization and Dense Scene Reconstruction within Heterogeneous Multi-Robot Systems

Fabrizio Schiano

Effective collaboration in multi-robot systems requires accurate and robust estimation of relative localization: from cooperative manipulation to collaborative sensing, and including cooperative exploration or cooperative transportation. This paper introdu ...

IEEE2022

Social/dialogical roles of social robots in supporting children's learning of language and literacy-A review and analysis of innovative roles

Barbara Bruno

One of the many purposes for which social robots are designed is education, and there have been many attempts to systematize their potential in this field. What these attempts have in common is the recognition that learning can be supported in a variety of ...

FRONTIERS MEDIA SA2022

Introducing Productive Engagement for Social Robots Supporting Learning

Jauwairia Nasir

We have all been one such student or seen such students who can maintain the 'good student' image while playing a video game under the table or those loyal backbenchers, seemingly always distracted, who then ace their exams. These intricacies of human beha ...

EPFL2022

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

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, Julius Maximilian Jankowski, Emmanuel Pignat, Teguh Santoso Lembono

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

Product of experts for robot learning from demonstration

Emmanuel Pignat

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

EPFL2021