Publications related to Robotic mapping

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

We apply inverse reinforcement learning (IRL) with a novel cost feature to the problem of robot navigation in human crowds. Consistent with prior empirical work on pedestrian behavior, the feature anticipates collisions between agents. We efficiently learn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Reinforcement learning for scaffold-free construction of spanning-structures

Maryam Kamgarpour, Stefana Parascho, Gabriel Rémi Vallat, Anna Maria Maddux, Jingwen Wang

In construction robotics, a conventional design-to-fabrication work-flow starts with designing a structure, followed by task and robotic motion planning, and ultimately, fabrication. However, this approach can prove unsuccessful, as we may only discover th ...

New York2023

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Aude Billard, Bernardo Fichera

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

SPRINGER INTERNATIONAL PUBLISHING AG2023

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

Unfreezing Social Navigation: Dynamical Systems based Compliance for Contact Control in Robot Navigation

Aude Billard, Diego Felipe Paez Granados, Vaibhav Gupta

Large efforts have focused on ensuring that the controllers for mobile service robots follow proxemics and other social rules to ensure both safe and socially acceptable distance to pedestrians. Nonetheless, involuntary contact may be unavoidable when the ...

IEEE2022

Deep Learning Methods for Socially-Aware Human Trajectory Forecasting

Parth Ashit Kothari

The ability to forecast human motion, called ``human trajectory forecasting", is a critical requirement for mobility applications such as autonomous driving and robot navigation. Humans plan their path taking into account what might happen in the future. S ...

EPFL2022

Foot-Interfaces Control and Human-Robot Interaction in Four-Handed Manipulation

Jacob Hernandez Sanchez

Humans have a great capacity to perform complex manipulations. However, there are many tasks for which two hands are not enough, such as in surgery. For such tasks, a dyad of people is required. However, when not used to work together, a dyad collaboration ...

EPFL2022

A modular functional framework for the design and evaluation of multi-robot navigation

Alcherio Martinoli, Cyrill Silvan Baumann

In this work, we address the design of tightly integrated control, estimation, and allocation algorithms allowing a group of robots to move collectively. For doing so, we leverage a modular framework that allows us to define precisely the needed functional ...

Elsevier2021

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Alexandre Massoud Alahi, Yuejiang Liu

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

2021

Unfreezing Social Navigation: Dynamical Systems based Compliance for Contact Control in Robot Navigation

Aude Billard, Diego Felipe Paez Granados

Soon human-robot interactions in pedestrian areas will be beyond the novelty effect with the deployment of delivery robots, autonomous personal mobility vehicles, and surveillance robots. Proxemics and other social rules guide these interactions, nonethele ...

2021

Human Detection and Segmentation via Multi-view Consensus

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Helge Jochen Rhodin

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion. For scenes with dynamic activities and ...

IEEE2021

Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter

Alcherio Martinoli, Alicja Barbara Roelofsen, Michiaki Hirayama

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multirobot formation, even when the communication fails and the t ...

Springer Nature2021

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Jie Luo, Mengshi Qi, Yiyu Wang

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Robot skills learning with Riemannian manifolds: Leveraging geometry-awareness in robot learning, optimization and control

Noémie Laure Gwendoline Jaquier

Humans exhibit outstanding learning and adaptation capabilities while performing various types of manipulation tasks. When learning new skills, humans are able to extract important information by observing examples of a task and efficiently refine a priori ...

EPFL2020

Relax and Recover: Guaranteed Range-Only Continuous Localization

Adam James Scholefield, Frederike Dümbgen, Michalina Wanda Pacholska

Range-only localization has applications as diverse as underwater navigation, drone tracking and indoor localization. While the theoretical foundations of lateration---range-only localization for static points---are well understood, there is a lack of unde ...

2020

Stretchable Kirigami Components for Composite Meso-Scale Robots

Jamie Paik, Amir Firouzeh, Tatsuya Higashisaka

Layer-by-layer manufacturing of composite mechanisms allows fast and cost-effective fabrication of customized robots in millimeter and centimeter scales which is promising for research fields that rely on frequent and numerous physical iterations. Due to t ...

2020

Oclussion-Aware Motion Planning at Roundabouts

Denis Gillet

In this work, we present a motion planning framework for automated vehicles to drive safely through intersections despite occlusions and the uncertain behavior of the surrounding vehicles. A context representation based on probably-free gaps is proposed as ...

2021

Recent Advances in Robot Learning from Demonstration

Aude Billard, Athanasios Polydoros

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be n ...

2020

Mapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment

Jan Skaloud, Davide Antonio Cucci, Laurent Valentin Jospin, Emmanuel Pierre Quentin Clédat

Drone mapping with GNSS-assisted photogrammetry is a highly efficient method for surveying small -or medium- sized areas. However, the mapping quality is not intuitively predictable, particularly in complex environments (with steep and cluttered terrain), ...

2020

On raw inertial measurements in dynamic networks

Jan Skaloud, Davide Antonio Cucci

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In thi ...

2019