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Zeynab Talebpour

6 accepted papers

2023

Deep Functional Predictive Control (deep-FPC): Robot Pushing 3-D Cluster Using Tactile Prediction

Kiyanoush Nazari, Gabriele Gandolfi, Zeynab Talebpour, Vishnu Rajendran, Willow Mandil, Paolo Rocco +1
IROS 2023poster

This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a str…

Cited by 8Source ↗Scholar ↗
2019

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

Zeynab Talebpour, Alcherio Martinoli
RA-L 2019

In this letter, we propose an adaptive risk-based replanning strategy in the context of multirobot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and human motion prediction uncertainty.

Cited by 13Source ↗Scholar ↗
2018

Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

Zeynab Talebpour, Alcherio Martinoli
IROS 2018poster

In this paper, we propose a risk-based coordination method for the Multi-Robot Task Allocation (MRTA) problem in human-populated environments. We introduce risk-based bids that incorporate human trajectory prediction uncertainties and furthermore, social costs in their formulation. We demonstrate th…

Cited by 12Source ↗Scholar ↗
2017

Market-based coordination in dynamic environments based on the Hoplites framework

Zeynab Talebpour, Stefano Savarè, Alcherio Martinoli
IROS 2017poster

This work focuses on multi-robot coordination based on the Hoplites framework for solving the Multi-Robot Task Allocation (MRTA) problem. In particular, we investigate three variations of increasing complexity for the MRTA problem: spatial task allocation based on distance, spatial task allocation b…

Cited by 11Source ↗Scholar ↗
2017

Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

José Manuel Palacios-Gasós, Zeynab Talebpour, Eduardo Montijano, Carlos Sagüés, Alcherio Martinoli
ICRA 2017poster

Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions t…

Cited by 54Source ↗Scholar ↗